content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
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| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# **************************************************************************
# Copyright ยฉ 2017 jianglin
# File Name: runserver.py
# Author: jianglin
# Email: [email protected]
# Created: 2017-02-02 09:18:53 (CST)
# Last Update:ๆๆๅ 2017-2-16 22:19:49 (CST)
# By:
# Description:
# **************************************************************************
from flask_apscheduler import scheduler
from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
from scheduler import create_app
from pytz import timezone
app = create_app(Config())
def hello():
'''
่พๅบๆถ้ด
'''
from time import time
print(time())
from apscheduler.jobstores.base import ConflictingIdError
try:
a = scheduler.add_job(hello, trigger='interval', seconds=10, id='3')
except ConflictingIdError as e:
print(e)
if __name__ == '__main__':
app.run()
print(app.url_map)
| [
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7,
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62,
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] | 2.816817 | 333 |
# Str08.py
# a1 a2 a3 a4;print(a1, a2, a3, a4) --> Java, C, SQL, Python
a1, a2, a3, a4 = 'Java,C,SQL,Python'.split(',')
print(a1, a2, a3, a4)
b = ['H', 'e', 'l', 'l', 'o']
b = ''.join(b)
print(b)
a = ''
for i in range(0,len(b)):
a += b[i]
print(a)
print('์๋
ํ์ธ์', sep=' ', end='\n')
print('์๋
ํ์ธ์', end='')
print('์๋
ํ์ธ์', end=' ')
print('์๋
ํ์ธ์')
print('์','๋
','ํ','์ธ','์', sep=' ')
print('์','๋
','ํ','์ธ','์', sep='/')
print('์','๋
','ํ','์ธ','์', sep=',', end='\t')
# sep๋ ๊ทธ ๊ตฌ๋ฌธ ๋ด์์์ ๊ตฌ๋ถ์, end๋ ๋ณต์๊ฐ์ ๊ตฌ๋ฌธ ์ฌ์ด์ ๊ตฌ๋ถ์
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] | 1.298507 | 402 |
import unittest
if __name__ == '__main__':
unittest.main()
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import msgpackrpc
from core.expando import ExpandoServer
ADDR = SERVER = THREAD = None
(ADDR, SERVER, THREAD) = ExpandoServer().serve()
client = msgpackrpc.Client(ADDR, unpack_encoding='utf-8')
client.call('add_expansion', u'esc', u'a vim user I see')
action = client.call('expand', u'esc')
print(action)
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""" the urls of rmon.views """
from flask import Blueprint
from rmon.views.index import IndexView
from rmon.views.server import ServerList,ServerDetail
api = Blueprint('api',__name__)
api.add_url_rule('/',view_func=IndexView.as_view('index'))
api.add_url_rule('/servers/',view_func=ServerList.as_view('server_list'))
api.add_url_rule('/servers/<int:object_id>',view_func=ServerDetail.as_view('server_detail'))
| [
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] | 2.795918 | 147 |
# -*- coding: utf-8 -*-
# encoding: utf-8
import smtplib
from email.mime.text import MIMEText
from email.utils import formataddr
if __name__ == "__main__":
mh = MailHelper('[email protected]', '1992wen1031')
mh.sendmail('[email protected],[email protected], ', 'Pythonๅ้้ฎไปถ', '่ฟๅชๆฏไธไธชๆต่ฏ้ฎไปถ,่ฏทไธ่ฆๅๅค!') | [
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from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
CUDA_FLAGS = []
ext_modules=[
CUDAExtension('soft_renderer.cuda.load_textures', [
'soft_renderer/cuda/load_textures_cuda.cpp',
'soft_renderer/cuda/load_textures_cuda_kernel.cu',
],
extra_compile_args={'cxx': [], 'nvcc': [
'--generate-code=arch=compute_35,code=sm_35',
'--generate-code=arch=compute_37,code=sm_37',
'--generate-code=arch=compute_50,code=sm_50',
'--generate-code=arch=compute_52,code=sm_52',
'--generate-code=arch=compute_53,code=sm_53',
'--generate-code=arch=compute_60,code=sm_60',
'--generate-code=arch=compute_61,code=sm_61',
'--generate-code=arch=compute_62,code=sm_62',
'--generate-code=arch=compute_70,code=sm_70',
'--generate-code=arch=compute_72,code=sm_72',
'--generate-code=arch=compute_75,code=sm_75',
'--generate-code=arch=compute_86,code=sm_86',
]}
),
CUDAExtension('soft_renderer.cuda.create_texture_image', [
'soft_renderer/cuda/create_texture_image_cuda.cpp',
'soft_renderer/cuda/create_texture_image_cuda_kernel.cu',
],
extra_compile_args={'cxx': [], 'nvcc': [
'--generate-code=arch=compute_35,code=sm_35',
'--generate-code=arch=compute_37,code=sm_37',
'--generate-code=arch=compute_50,code=sm_50',
'--generate-code=arch=compute_52,code=sm_52',
'--generate-code=arch=compute_53,code=sm_53',
'--generate-code=arch=compute_60,code=sm_60',
'--generate-code=arch=compute_61,code=sm_61',
'--generate-code=arch=compute_62,code=sm_62',
'--generate-code=arch=compute_70,code=sm_70',
'--generate-code=arch=compute_72,code=sm_72',
'--generate-code=arch=compute_75,code=sm_75',
'--generate-code=arch=compute_86,code=sm_86',
]}
),
CUDAExtension('soft_renderer.cuda.soft_rasterize', [
'soft_renderer/cuda/soft_rasterize_cuda.cpp',
'soft_renderer/cuda/soft_rasterize_cuda_kernel.cu',
],
extra_compile_args={'cxx': [], 'nvcc': [
'--generate-code=arch=compute_35,code=sm_35',
'--generate-code=arch=compute_37,code=sm_37',
'--generate-code=arch=compute_50,code=sm_50',
'--generate-code=arch=compute_52,code=sm_52',
'--generate-code=arch=compute_53,code=sm_53',
'--generate-code=arch=compute_60,code=sm_60',
'--generate-code=arch=compute_61,code=sm_61',
'--generate-code=arch=compute_62,code=sm_62',
'--generate-code=arch=compute_70,code=sm_70',
'--generate-code=arch=compute_72,code=sm_72',
'--generate-code=arch=compute_75,code=sm_75',
'--generate-code=arch=compute_86,code=sm_86',
]}
),
CUDAExtension('soft_renderer.cuda.voxelization', [
'soft_renderer/cuda/voxelization_cuda.cpp',
'soft_renderer/cuda/voxelization_cuda_kernel.cu',
],
extra_compile_args={'cxx': [], 'nvcc': [
'--generate-code=arch=compute_35,code=sm_35',
'--generate-code=arch=compute_37,code=sm_37',
'--generate-code=arch=compute_50,code=sm_50',
'--generate-code=arch=compute_52,code=sm_52',
'--generate-code=arch=compute_53,code=sm_53',
'--generate-code=arch=compute_60,code=sm_60',
'--generate-code=arch=compute_61,code=sm_61',
'--generate-code=arch=compute_62,code=sm_62',
'--generate-code=arch=compute_70,code=sm_70',
'--generate-code=arch=compute_72,code=sm_72',
'--generate-code=arch=compute_75,code=sm_75',
'--generate-code=arch=compute_86,code=sm_86',
]}
),
]
INSTALL_REQUIREMENTS = ['numpy', 'torch', 'torchvision', 'scikit-image', 'tqdm', 'imageio']
setup(
description='PyTorch implementation of "Soft Rasterizer"',
author='Shichen Liu',
author_email='[email protected]',
license='MIT License',
version='1.0.0',
name='soft_renderer',
packages=['soft_renderer', 'soft_renderer.cuda', 'soft_renderer.functional'],
install_requires=INSTALL_REQUIREMENTS,
ext_modules=ext_modules,
cmdclass = {'build_ext': BuildExtension}
)
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6,
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62,
2302,
10354,
10934,
11627,
3004,
92,
198,
8,
198
] | 1.909284 | 2,359 |
import pandas as pd
import numpy as np
from random import randrange
from datetime import timedelta
from datetime import datetime
import random
import string
from faker import Faker
countries = ["ะะฐะทะฐั
ััะฐะฝ", "ะ ะพััะธั", "ะัะฐะปะธั", "ะะตัะผะฐะฝะธั", "ะะธัะฐะน", "ะกะจะ"]
regions = ["ะะปะผะฐัั", "ะัั-ะกัะปัะฐะฝ", "ะจัะผะบะตะฝั"]
choices = ["ะดะฐ", "ะฝะตั"]
hospitals = ["ะะฝะพะณะพะฟัะพัะธะปัะฝัะน ะผะตะดะธัะธะฝัะบะธะน ัะตะฝัั", "ะดะพะผะฐัะฝะธะน ะบะฐัะฐะฝัะธะฝ", "ััะฐัะธะพะฝะฐั", "ััะฐะฝะทะธั", "ะฒัะปะตั ะฒ ัะฒะตะนัะฐัะธั", "ะะพัะพะดัะบะฐั ะธะฝัะตะบัะธะพะฝะฝะฐั ะฑะพะปัะฝะธัะฐ"]
addresses = ["ะกะฐัะฐะนััะบ", "ะัะฝะฐะตะฒะฐ", "ะกะตะนััะปะปะธะฝะฐ", "ะ ััะบัะปะพะฒะฐ", "ะะฑะฐั", "ะะฐะฝะณะตะปัะดะธะฝะฐ", "ะะฐะฝัะธะปะพะฒะฐ", "ะขะพัะตะบัะปะพะฒะฐ", "ะะตะปัะพะบัะฐะฝ", "ะกัะณะฐะฝะฐะบ"]
def random_date(start, end):
"""
This function will return a random datetime between two datetime
objects.
"""
delta = end - start
int_delta = (delta.days * 24 * 60 * 60) + delta.seconds
random_second = randrange(int_delta)
return start + timedelta(seconds=random_second)
generate_fake_csv() | [
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8,
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8612,
378,
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3419
] | 1.696751 | 554 |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import spatial
import picasso.io as io
### High density data
dir_name = '/fs/pool/pool-schwille-paint/Data/p17.lbFCS2/20-12-09_N1_T23_ibidi_cseries/21-01-19_FS_id180'
file_name = 'ibidi_id180_Pm2-05nM_p40uW_1_MMStack_Pos0.ome_locs_render_picked_props.hdf5'
### Low density data
# dir_name = '/fs/pool/pool-schwille-paint/Data/p17.lbFCS2/20-12-10_N1-5xCTC_cseries_varexp/21-01-19_FS_id180_exp400'
# file_name = 'id180_Pm2-2d5nM_p40uW_exp400_1_MMStack_Pos0.ome_locs_render_picked_props.hdf5'
### Load props
props = pd.DataFrame(io.load_locs(os.path.join(dir_name,file_name))[0])
### Build KDtree and ask for nearest neighbor
data = props[['x','y']].values.astype(np.float32)
tree = spatial.cKDTree(data)
d,idx = tree.query(data,k=[2])
### Plot nearest neighbor distance distribution in data
f = plt.figure(0,figsize = [5,4])
f.clear()
ax = f.add_subplot(111)
ax.hist(d,bins=np.linspace(0,12,100))
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] | 2.238202 | 445 |
# Copyright (c) 2012-2019 Eli Janssen
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file.
# this example shows how filtering can be done on the url generation side.
# this example through https urls (no proxying required), and only allows http
# requests over port 80.
import hashlib
import hmac
import base64
from urllib.parse import urlsplit
CAMO_HOST = 'https://img.example.com'
print(camo_url("test", "http://golang.org/doc/gopher/frontpage.png"))
# https://img.example.org/D23vHLFHsOhPOcvdxeoQyAJTpvM/aHR0cDovL2dvbGFuZy5vcmcvZG9jL2dvcGhlci9mcm9udHBhZ2UucG5n
print(camo_url("test", "http://golang.org:80/doc/gopher/frontpage.png"))
# https://img.example.com/8_b8SZkMlTYfsGFtkZS7SyJn37k/aHR0cDovL2dvbGFuZy5vcmc6ODAvZG9jL2dvcGhlci9mcm9udHBhZ2UucG5n
print(camo_url("test", "http://golang.org:8080/doc/gopher/frontpage.png"))
# Nope!
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0,
198
] | 2.366048 | 377 |
#program to create a new string with no duplicate consecutive letters from a given string.
print(no_consecutive_letters("PPYYYTTHON"))
print(no_consecutive_letters("PPyyythonnn"))
print(no_consecutive_letters("Java"))
print(no_consecutive_letters("PPPHHHPPP")) | [
2,
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] | 3.156627 | 83 |
import sys
import pathlib
import numpy as np
from collections import namedtuple
from scipy import interpolate
import math
path_package = pathlib.Path(__file__).parent.absolute()
sys.path.insert(1,str(path_package.parent))
import API as compression
#%%โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# SETUP
path_data = path_package / 'data'
Reference = namedtuple('Reference',
['raw','atol','cmethod','ostyle','compressed'],
defaults=[1e-5,'interp10','monolith',None])
#%%โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# FUNCTIONS
##%%โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
## Auxiliary functions
#%%โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# Reference raw data
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
raw = {'poly0': raw_poly0,
'poly1': raw_poly1,
'poly2': raw_poly2,
'sine': raw_sine}
###โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
class Data():
'''Data container'''
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
references = [Reference(raw['poly0'],1e-5,'interp10','monolith')]
#โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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from office365.teams.shifts.change_tracked_entity import ChangeTrackedEntity
class Shift(ChangeTrackedEntity):
"""
Represents a unit of scheduled work in a shifts.
"""
pass
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"""
time: a
space: a
"""
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"""This module contains the wrapper class to interacte with fzf.
The fzf class should be used for all occasion when fzf needs
to be launched. fzfaws comes with 4 fzf binary files and will
be used if user doesn't specify to use system fzf in config file.
"""
import os
import subprocess
import sys
from typing import Any, Dict, Generator, List, Optional, Union
from fzfaws.utils.exceptions import EmptyList, NoSelectionMade
class Pyfzf:
r"""A simple wrapper class for fzf utilizing subprocess module.
To create a entry into fzf, use Pyfzf.append_fzf() and pass in the string.
To create mutiple entries, would require manually pass in \n to seperate each entry.
For a list of response from boto3, it is recommended to use the process_list() function.
Example:
fzf = Pyfzf()
s3 = boto3.client('s3')
response = s3.list_buckets()
fzf.process_list(response["Buckets"], "Name")
selected_bucket = fzf.execute_fzf(multi_select=False)
The above example process the list of buckets in response and make "Name" the return value.
The selected_bucket will be a bucket name.
"""
def __init__(self) -> None:
"""Construct the Pyfzf instance.
Credit to https://github.com/pmazurek/aws-fuzzy-finder for the binary detection
method.
"""
self.fzf_string: str = ""
if sys.maxsize > 2 ** 32:
arch = "amd64"
else:
arch = "386"
if sys.platform.startswith("darwin"):
system = "darwin"
elif sys.platform.startswith("linux"):
system = "linux"
else:
print(
"fzfaws currently is only compatible with python3.6+ on MacOS or Linux"
)
sys.exit(1)
self.fzf_path: str = (
"fzf"
if os.getenv("FZFAWS_FZF_EXECUTABLE", "binary") == "system"
else "%s/../libs/fzf-0.21.1-%s_%s"
% (os.path.dirname(os.path.abspath(__file__)), system, arch)
)
def append_fzf(self, new_string: str) -> None:
r"""Append stings to fzf_string.
To have mutiple entries, seperate them by '\n'
Example:fzf.append_fzf('hello')
fzf.append_fzf('\n')
fzf.append_fzf('world')
:param new_string: strings to append to fzf entry
:type new_string: str
"""
self.fzf_string += new_string
def execute_fzf(
self,
empty_allow: bool = False,
print_col: int = 2,
preview: Optional[str] = None,
multi_select: bool = False,
header: Optional[str] = None,
delimiter: Optional[str] = None,
) -> Union[List[Any], List[str], str]:
r"""Execute fzf and return formated string.
Example:
fzf = Pyfzf()
fzf.append_fzf('Hello: hello')
fzf.append_fzf('\n')
fzf.append_fzf('World: world')
fzf.append_fzf('\n')
print(fzf.execute_fzf(empty_allow=True, print_col=1, preview='cat {}', multi_select=True))
The selected string would look like "Hello: hello".
Above example would return 'Hello:'' if the first entry is selected, print col is 1,
if print_col was 2, 'hello' would be printed.
:param empty_allow: determine if empty selection is allowed
:type empty_allow: bool, optional
:param print_col: which column of the result to print (used by awk), -1 print everything except first col
:type print_col: int, optional
:param preview: display preview in fzf, e.g.(echo 'hello')
:type preview: str, optional
:param multi_select: enable fzf multi selection
:type multi_select: bool, optional
:param header: header to display in fzf
:type header: str, optional
:param delimiter: the delimiter to seperate print_col, like awk number
:type delimiter: Optional[str]
:raises NoSelectionMade: when user did not make a selection and empty_allow is False
:return: selected entry from fzf
:rtype: Union[list[Any], list[str], str]
"""
# remove trailing spaces/lines
self.fzf_string = str(self.fzf_string).rstrip()
fzf_input = subprocess.Popen(("echo", self.fzf_string), stdout=subprocess.PIPE)
cmd_list: list = self._construct_fzf_cmd()
selection: bytes = b""
selection_str: str = ""
if header:
cmd_list.append("--header=%s" % header)
if multi_select:
cmd_list.append("--multi")
else:
cmd_list.append("--no-multi")
if preview:
cmd_list.extend(["--preview", preview])
try:
selection = subprocess.check_output(cmd_list, stdin=fzf_input.stdout)
selection_str = str(selection, "utf-8")
if not selection and not empty_allow:
raise NoSelectionMade
# if first line contains ctrl-c, exit
self._check_ctrl_c(selection_str)
except subprocess.CalledProcessError:
# this exception may happend if user didn't make a selection in fzf
# thus ending with non zero exit code
if not empty_allow:
raise NoSelectionMade
elif empty_allow:
if multi_select:
return []
else:
return ""
if multi_select:
return_list: List[str] = []
# multi_select would return everything seperate by \n
selections: List[str] = selection_str.strip().splitlines()
for item in selections:
processed_str = self._get_col(item, print_col, delimiter)
return_list.append(processed_str)
return return_list
else:
return self._get_col(selection_str.strip(), print_col, delimiter)
def get_local_file(
self,
search_from_root: bool = False,
cloudformation: bool = False,
directory: bool = False,
hidden: bool = False,
empty_allow: bool = False,
multi_select: bool = False,
header: Optional[str] = None,
) -> Union[List[Any], List[str], str]:
"""Get local files through fzf.
Populate the local files into fzf, if search_from_root is true
all files would be populated.
Note: could be extremely slow to seach from root if fd not installed.
:param search_from_root: search files from root
:type search_from_root: bool, optional
:param cloudformation: only search yaml or json
:type cloudformation: bool, optional
:param directory: search directory
:type directory: bool, optional
:param hidden: search hidden file, only has effect when fd installed
:type hidden: bool, optional
:param empty_allow: allow empty selection
:type empty_allow: bool, optional
:param multi_select: allow multi selection
:type multi_select: bool, optional
:param header: header display in fzf
:type header: str, optional
:raises NoSelectionMade: when empty_allow=False and no selection was made
:return: selected file path or folder path
:rtype: Union[list[Any], list[str], str]
"""
if search_from_root:
home_path = os.path.expanduser("~")
os.chdir(home_path)
if not header and directory:
header = r"Selecting ./ will use current directory"
cmd: str = ""
if self._check_fd():
if directory:
cmd = "echo \033[33m./\033[0m; fd --type d"
elif cloudformation:
cmd = "fd --type f --regex '(yaml|yml|json)$'"
else:
cmd = "fd --type f"
if hidden:
cmd += " -H"
else:
if directory:
cmd = "echo \033[33m./\033[0m; find * -type d"
elif cloudformation:
cmd = 'find * -type f -name "*.json" -o -name "*.yaml" -o -name "*.yml"'
else:
cmd = "find * -type f"
list_file = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, shell=True
)
selected_file_path: bytes = b""
selected_file_path_str: str = ""
try:
cmd_list: list = self._construct_fzf_cmd()
if header:
cmd_list.append("--header=%s" % header)
if multi_select:
cmd_list.append("-m")
else:
cmd_list.append("+m")
selected_file_path = subprocess.check_output(
cmd_list, stdin=list_file.stdout
)
selected_file_path_str = str(selected_file_path, "utf-8")
if not empty_allow and not selected_file_path:
raise NoSelectionMade
self._check_ctrl_c(selected_file_path_str)
except subprocess.CalledProcessError:
# subprocess exception will raise when user press ecs to exit fzf
if not empty_allow:
raise NoSelectionMade
elif empty_allow:
return [] if empty_allow else ""
if multi_select:
# multi_select would return everything seperate by \n
return selected_file_path_str.strip().splitlines()
else:
return selected_file_path_str.strip()
def _construct_fzf_cmd(self) -> List[str]:
"""Construct command for fzf.
:return: command list processable by subprocess
:rtype: list[str]
"""
cmd_list: list = [self.fzf_path, "--ansi", "--expect=ctrl-c"]
if os.getenv("FZFAWS_FZF_OPTS"):
cmd_list.extend(os.getenv("FZFAWS_FZF_OPTS").split(" "))
if os.getenv("FZFAWS_FZF_KEYS"):
cmd_list.append(os.getenv("FZFAWS_FZF_KEYS", ""))
return cmd_list
def _check_ctrl_c(self, fzf_result: str) -> None:
"""Check if ctrl_c is pressed during fzf invokation.
If ctrl_c is pressed, exit entire fzfaws program instead of
keep moving forward.
:param fzf_result: the str output of fzf subprocess
:type fzf_result: tr
"""
result = fzf_result.splitlines()
if len(result) < 1:
return
if result[0] == "ctrl-c":
raise KeyboardInterrupt
def _check_fd(self):
"""Check if fd is intalled on the machine."""
try:
subprocess.run(
["command", "-v", "fd"], stdout=subprocess.DEVNULL, check=True
)
return True
except:
return False
def process_list(
self,
response_list: Union[list, Generator],
key_name: str,
*arg_keys,
empty_allow: bool = False
) -> None:
"""Process list passed in and formatted for fzf.
Processes the list passed into it and prepare the fzf operation
Note: need to invoke fzf.execute_fzf() to pop the fzf
process and get the user selection.
Example:
list = [{'Name': 1, 'Mame': 2}, {'Name': 2, 'Mame': 3}]
fzf.process_list(list, 'Name', 'Mame')
fzf.execute_fzf(empty_allow=False)
In the above example, if first entry is selected, it will return 1.
:param response_list: list to process
:type response_list: list
:param key_name: key_name to search and add into response
:type key_name: str
:param gap: gap between each key
:type gap: int, optional
:raises EmptyList: when the list is empty and did not get any result
"""
for item in response_list:
self.append_fzf("%s: %s" % (key_name, item.get(key_name)))
for arg in arg_keys:
self.append_fzf(" | ")
self.append_fzf("%s: %s" % (arg, item.get(arg)))
self.append_fzf("\n")
if not self.fzf_string and not empty_allow:
raise EmptyList("Result list was empty")
def format_selected_to_dict(self, selected_str: str) -> Dict[str, Any]:
"""Format the selected option into a proper dictionary.
This is only useful if fzf.execute_fzf(print_col=0).
This is useful to use in conjuction with process_list, process_list
might contain a lot of information but printing all of them into
a str may not be useful enough.
Example:
fzf.process_list(
response_generator,
"InstanceId",
"Status",
"InstanceType",
"Name",
"KeyName",
"PublicDnsName",
"PublicIpAddress",
"PrivateIpAddress",
)
result = fzf.execute_fzf(multi_select=multi_select, header=header, print_col=0)
result_dict = fzf.format_selected_to_dict(result)
:param selected_str: the selected str from fzf.execute_fzf
:type selected_str: str
:return: formatted instance details in dict form
:rtype: Dict[str, Any]
"""
formatted_dict: Dict[str, Any] = {}
selected_list = selected_str.split(" | ")
for key_value in selected_list:
key, value = key_value.split(": ")
if value == "None":
formatted_dict.update({key: None})
else:
formatted_dict.update({key: value})
return formatted_dict
def _get_col(self, string: str, print_col: int, delimiter: Optional[str]) -> str:
"""Return the wanted col of the given str.
:param string: string to process
:type string: str
:param print_col: column to return
:type print_col: int
:param delimiter: delimiter that seperate the column
:type delimiter: Optional[str]
:return: the print_col of the string
:rtype: str
"""
if print_col == 0:
return string
else:
delimited_str = string.split(delimiter)
if print_col - 1 > len(delimited_str):
# somewhat similar to awk behavior?
# when the print col exceed the col number, awk return the entire string
return string
return delimited_str[print_col - 1]
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] | 2.145319 | 6,730 |
from gears_cli import run, install_requirements, export_requirements, import_requirements
from click.testing import CliRunner
from RLTest import Defaults
import os
Defaults.decode_responses = True
SSL_ARGS = ['--ssl', 'True', '--ssl-keyfile', './tests/tls/redis.key', '--ssl-certfile', './tests/tls/redis.crt', '--ssl-ca-certs', './tests/tls/ca.crt', '--ssl-password', 'foobar']
| [
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] | 2.781022 | 137 |
# Generated by Django 3.0.8 on 2020-07-20 22:02
from django.db import migrations, models
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1238,
2534,
25,
2999,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
import math
lim = 100
sum = math.trunc(lim * (lim + 1) / 2)
carressum = sum * sum
sumcarres = math.trunc(lim * (lim + 1) * (2 * lim + 1) / 6)
print("%d" % (carressum - sumcarres), end='')
| [
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7061,
8,
628
] | 2.329268 | 82 |
try:
import asyncio
except ImportError:
try:
import trollius as asyncio
except ImportError:
asyncio = None
try:
from threading import RLock as Lock
except ImportError:
from rx.internal.concurrency import NoLock as Lock
try:
from asyncio import Future
except ImportError:
try:
from trollius import Future
except ImportError:
Future = None
# Rx configuration dictionary
config = {
"Future": Future,
"Lock": Lock,
"asyncio": asyncio
}
from .observable import Observable
from .anonymousobservable import AnonymousObservable
from .observer import Observer
from . import checkedobserver
from . import linq
from . import backpressure
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1330,
9493,
80,
198,
6738,
764,
1330,
736,
36151,
628
] | 2.958333 | 240 |
# -*- coding: utf-8 -*-
# File: model_desc.py
from collections import namedtuple
import tensorflow as tf
from ..utils.argtools import memoized_method
from ..tfutils.common import get_op_tensor_name
from ..tfutils.tower import get_current_tower_context
from ..compat import backport_tensor_spec, tfv1
TensorSpec = backport_tensor_spec()
__all__ = ['InputDesc', 'ModelDesc', 'ModelDescBase']
def build_or_reuse_placeholder(tensor_spec):
"""
Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor:
"""
g = tfv1.get_default_graph()
name = tensor_spec.name
try:
tensor = g.get_tensor_by_name(name + ':0')
assert "Placeholder" in tensor.op.type, "Tensor {} exists but is not a placeholder!".format(name)
assert tensor_spec.is_compatible_with(tensor), \
"Tensor {} exists but is not compatible with the signature!".format(tensor)
if tensor.shape == tensor_spec.shape:
# It might be desirable to use a placeholder of a different shape in some tower
# (e.g., a less specific shape)
return tensor
except KeyError:
pass
with tfv1.name_scope(None): # clear any name scope it might get called in
ret = tfv1.placeholder(
tensor_spec.dtype, shape=tensor_spec.shape, name=tensor_spec.name)
return ret
class InputDesc(
namedtuple('InputDescTuple', ['type', 'shape', 'name'])):
"""
An equivalent of `tf.TensorSpec`.
History: this concept is used to represent metadata about the inputs,
which can be later used to build placeholders or other types of input source.
It is introduced much much earlier than the equivalent concept `tf.TensorSpec`
was introduced in TensorFlow.
Therefore, we now switched to use `tf.TensorSpec`, but keep this here for compatibility reasons.
"""
def __new__(cls, type, shape, name):
"""
Args:
type (tf.DType):
shape (tuple):
name (str):
"""
# TODO mark deprecated
assert isinstance(type, tf.DType), type
return tf.TensorSpec(shape=shape, dtype=type, name=name)
class ModelDescBase(object):
"""
Base class for a model description.
"""
@memoized_method
@memoized_method
def get_input_signature(self):
"""
Returns:
A list of :class:`tf.TensorSpec`, which describes the inputs of this model.
The result is cached for each instance of :class:`ModelDescBase`.
"""
with tf.Graph().as_default() as G: # create these placeholder in a temporary graph
inputs = self.inputs()
assert isinstance(inputs, (list, tuple)), \
"ModelDesc.inputs() should return a list of tf.TensorSpec objects! Got {} instead.".format(str(inputs))
if isinstance(inputs[0], tf.Tensor):
for p in inputs:
assert "Placeholder" in p.op.type, \
"inputs() have to return TensorSpec or placeholders! Found {} instead.".format(p)
assert p.graph == G, "Placeholders returned by inputs() should be created inside inputs()!"
return [TensorSpec(shape=p.shape, dtype=p.dtype, name=get_op_tensor_name(p.name)[0]) for p in inputs]
@property
def input_names(self):
"""
Returns:
[str]: the names of all the inputs.
"""
return [k.name for k in self.get_input_signature()]
def inputs(self):
"""
Returns a list of :class:`tf.TensorSpec` or placeholders.
A subclass is expected to implement this method.
If returning placeholders,
the placeholders __have to__ be created inside this method.
Don't return placeholders created in other places.
Also, you should never call this method by yourself.
Returns:
list[tf.TensorSpec or tf.placeholder]. To be converted to :class:`tf.TensorSpec`.
"""
raise NotImplementedError()
def build_graph(self, *args):
"""
Build the whole symbolic graph.
This is supposed to be part of the "tower function" when used with :class:`TowerTrainer`.
A subclass is expected to implement this method.
Args:
args ([tf.Tensor]): tensors that matches the list of inputs defined by ``inputs()``.
Returns:
In general it returns nothing, but a subclass
may require it to return necessary information to build the trainer.
For example, `SingleCostTrainer` expect this method to return the cost tensor.
"""
raise NotImplementedError()
@property
def training(self):
"""
Returns:
bool: whether the caller is under a training context or not.
"""
return get_current_tower_context().is_training
class ModelDesc(ModelDescBase):
"""
A ModelDesc with **single cost** and **single optimizer**.
It has the following constraints in addition to :class:`ModelDescBase`:
1. :meth:`build_graph(...)` method should return a cost when called under a training context.
The cost will be the final cost to be optimized by the optimizer.
Therefore it should include necessary regularization.
2. Subclass is expected to implement :meth:`optimizer()` method.
"""
@memoized_method
def get_optimizer(self):
"""
Return the memoized optimizer returned by `optimizer()`.
Users of :class:`ModelDesc` will need to implement `optimizer()`,
which will only be called once per each model.
Returns:
a :class:`tf.train.Optimizer` instance.
"""
ret = self.optimizer()
assert isinstance(ret, tfv1.train.Optimizer), \
"ModelDesc.optimizer() must return a tf.train.Optimizer! Got {} instead.".format(str(ret))
return ret
def optimizer(self):
"""
Returns a `tf.train.Optimizer` instance.
A subclass is expected to implement this method.
"""
raise NotImplementedError()
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] | 2.524637 | 2,476 |
import numpy as np
import pandas as pd
def check_df_col(df, column, name=None):
"""
Checks for the presence of a column (or columns) in a tidy
DataFrame with an informative error message. Passes silently,
otherwise raises error.
"""
if column is not None:
if type(column) != list:
column = [column]
for col in column:
if name is None:
error_message = f"The value '{col}' is not present in any of the columns of your DataFrame."
else:
error_message = f"Your {name} value '{col}' is not present in any of the columns of your DataFrame."
error_message += "\nYou may be looking for:\n " + str(list(df.columns))
assert col in df.columns, error_message
def check_replicates(df, variable, value, grouping):
"""Checks for the presence of replicates in the values of a dataset,
given some experimental conditions. Returns True if the standard
deviation of the values of each group (if more than one exists) is
greater than, indicating that replicates were performed under the
given criteria.
Parameters
----------
df : Pandas DataFrame in tidy format
The data set to be checked for replicates
variable : immutable object
Name of column of data frame for the independent variable,
indicating a specific experimental condition.
value : immutable object
Name of column of data frame for the dependent variable,
indicating an experimental observation.
group : immutable object of list of immutable objects
Column name or list of column names that indicates how the
data set should be split.
Returns
-------
replicates : boolean
True if replicates are present.
df_out : the DataFrame containing averaged 'variable' values, if
replicates is True. Otherwise returns the original DataFrame.
"""
# Unpack the experimental conditions into a single list of arguments
if type(grouping) != list:
grouping = [grouping]
args = [elem for elem in [variable, *grouping] if elem != None]
# Get stdev of argument groups
grouped = df.groupby(args)[value]
group_stdevs = grouped.std().reset_index()
group_stdev = group_stdevs[value].mean()
# Determine if there are replicates (mean > 0)
replicates = bool(group_stdev > 0)
# Average the values and return
if replicates:
df_mean = grouped.mean().reset_index()
df_mean.columns = list(df_mean.columns[:-1]) + ['Mean of ' + str(value)]
df_return = df.merge(df_mean)
return replicates, df_return | [
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62,
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8,
628,
220,
220,
220,
1441,
2186,
16856,
11,
47764,
62,
7783
] | 2.891775 | 924 |
import sys
import ebf
import numpy as np
import scipy.interpolate
import pdb
import asfgrid
from astropy.io import ascii
from classify_direct import *
dnumodel = asfgrid.Seism()
bcmodel = h5py.File('bcgrid.h5', 'r')
dustmodel = mwdust.Combined15()
x=obsdata()
x.addspec([5065.,-99.0,-0.1],[120.,0.0,0.2])
x.addseismo([231.,16.5],[10.,0.5])
paras=stparas(input=x,dnumodel=dnumodel,bcmodel=bcmodel,dustmodel=dustmodel,\
useav=0,dnucor=0,plot=0)
x.addjhk([6.025,5.578,5.496],[0.019,0.038,0.018])
x.addplx(8.9536/1e3,0.7/1e3)
paras=stparas(input=x,dnumodel=dnumodel,bcmodel=bcmodel,dustmodel=dustmodel,\
useav=0,dnucor=0,plot=0)
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11,
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] | 1.926136 | 352 |
"""
A mixin to convert kwargs to attributes.
"""
class KwargsToDict(object):
"""This mixin converts kwargs passed to the constructor to attributes.
"""
def __init__(self, **kwargs):
"""When a class is created, automatically convert its kwargs to
attributes.
:param dict kwargs: The keyword arguments passed to the class.
"""
for key, value in kwargs.items():
setattr(self, key, value)
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8,
198
] | 2.652941 | 170 |
#!/usr/bin/env python3
import os
import logging
import time
from shared import common_utils
| [
2,
48443,
14629,
14,
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628,
628,
628,
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198
] | 3.1875 | 32 |
"""438. Find All Anagrams in a String"""
| [
37811,
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13,
9938,
1439,
1052,
6713,
82,
287,
257,
10903,
37811,
198
] | 3.153846 | 13 |
from tkinter import *
from tkinter import ttk
from threading import Thread
import time
import path_finder
import os, subprocess
from tkinter.filedialog import askdirectory
import dependency_manager
import config
from PIL import Image, ImageTk
import webbrowser
import utilities
import functools
import version_checker
gui_update_cycles = 0
update_check_queued = False
shown_update = False
root = Tk()
root.title("NWN Launcher - Prisoners of The Mist")
root.resizable(0,0)
#root.iconify()
#root.attributes('-alpha', 0.0) #For icon
#main_window = Toplevel()
#main_window.overrideredirect(False)
#main_window.geometry("1000x1000") #Whatever size
# Load the images we'll use
background_image=ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), "background.png"))
buttons = {}
for button_name in ["clear", "launch", "update", "website", "gear"]:
buttons[button_name + "_button_active"] = ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), button_name + "_button_active.png"))
buttons[button_name + "_button_disabled"] = ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), button_name + "_button_disabled.png"))
buttons[button_name + "_button_hovered"] = ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), button_name + "_button_hovered.png"))
"""def _load_images():
global background_image
background_image=ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), "background.png"))
global button_active_image
button_active_image=ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), "button_active.png"))
global button_disabled_image
button_disabled_image=ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), "button_disabled.png"))
global button_hovered_image
button_hovered_image=ImageTk.PhotoImage(file=os.path.join(path_finder.get_server_images_path(), "button_hovered.png"))
background_label["image"] = background_image
if update_button.enabled:
_image_to_enabled(update_button)
else:
_image_to_disabled(update_button)
_image_to_enabled(launch_button)"""
ttk.Style().configure("TEntry", padding=6, relief="flat",
background="#595b59")
#mainframe = ttk.Frame(main_window, padding="0 0 0 0")
mainframe = ttk.Frame(root, padding="0 0 0 0")
mainframe.grid(column=0, row=0, sticky=(N, W, E, S))
background_label = Label(mainframe, image=background_image, borderwidth=0, background="#000000")
background_label.grid(row=0,column=0, rowspan=8, columnspan=8)
background_label.bind("<Button-1>", lambda event: print("Pressed BG"))
launch_button = _create_label_button("Launch", "launch")
launch_button.place(in_=mainframe, anchor=config.get_gui_conf("launch_button_anchor"),
relx=config.get_gui_conf("launch_button_pos")[0],
rely=config.get_gui_conf("launch_button_pos")[1])
launch_button.bind("<Button-1>",_trigger_launch)
website_button = _create_label_button("Website", "website")
website_button.place(in_=mainframe, anchor=config.get_gui_conf("website_button_anchor"),
relx=config.get_gui_conf("website_button_pos")[0],
rely=config.get_gui_conf("website_button_pos")[1])
website_button.bind("<Button-1>", lambda event: webbrowser.open(config.get_server_conf("website")))
diagnosis_button = _create_label_button("Gear", "gear")
diagnosis_button.place(in_=mainframe, anchor=config.get_gui_conf("gear_button_anchor"),
relx=config.get_gui_conf("gear_button_pos")[0],
rely=config.get_gui_conf("gear_button_pos")[1])
diagnosis_button.bind("<Button-1>",_open_diagnosis)
player_name_label = Label(mainframe, text = "Player name: ", borderwidth = 0, width=0,
padx=2, pady=2,
font=config.get_gui_conf("player_name_label_font", "label_default_font"),
foreground=config.get_gui_conf("player_name_label_fg_color", "label_default_fg_color"),
background=config.get_gui_conf("player_name_label_bg_color", "label_default_bg_color"))
player_name_label.place(in_=mainframe, anchor=config.get_gui_conf("player_name_label_anchor"),
relx=config.get_gui_conf("player_name_label_pos")[0],
rely=config.get_gui_conf("player_name_label_pos")[1])
player_var = StringVar()
player_combobox = ttk.Combobox(mainframe, textvariable=player_var, width=29)
player_combobox["values"] = config.main_conf_values["player_names"]
player_var.set(config.player_name)
#player_combobox.state(['readonly'])
player_combobox.place(in_=mainframe,
anchor=config.get_gui_conf("player_combobox_anchor"),
relx=config.get_gui_conf("player_combobox_pos")[0],
rely=config.get_gui_conf("player_combobox_pos")[1])
#player_var.trace("w", _change_player_conf)
player_save_button = Label(mainframe, text = "Save Name", borderwidth = 0, width=0,
padx=2, pady=2,
font=config.get_gui_conf("save_button_font", "button_font"),
foreground=config.get_gui_conf("save_button_fg_color", "button_enabled_fg_color"),
background=config.get_gui_conf("save_button_bg_color", "button_bg_color"))
player_save_button.place(in_=mainframe,
anchor=config.get_gui_conf("save_button_anchor"),
relx=config.get_gui_conf("save_button_pos")[0],
rely=config.get_gui_conf("save_button_pos")[1])
player_save_button.bind("<Button-1>",lambda e:_save_player())
nwn_path = StringVar()
nwn_path.set(path_finder.get_executable_path())
nwn_path_label = _create_label(nwn_path)
nwn_path_label.place(in_=mainframe,
anchor=config.get_gui_conf("nwn_path_label_anchor"),
relx=config.get_gui_conf("nwn_path_label_pos")[0],
rely=config.get_gui_conf("nwn_path_label_pos")[1])
nwn_path_name_label = Label(mainframe, text = "NWN Path:", borderwidth = 0, width=0,
padx=2, pady=2,
font=config.get_gui_conf("nwn_path_name_label_font", "label_font"),
foreground=config.get_gui_conf("nwn_path_name_label_fg_color", "label_fg_color"),
background=config.get_gui_conf("nwn_path_name_label_bg_color", "label_bg_color"))
nwn_path_name_label.place(in_=mainframe,
anchor=config.get_gui_conf("nwn_path_name_label_anchor"),
relx=config.get_gui_conf("nwn_path_name_label_pos")[0],
rely=config.get_gui_conf("nwn_path_name_label_pos")[1])
nwn_path_label.bind("<Button-1>",_trigger_path_dialogue)
update_button = _create_label_button("Update", "update")
update_button.place(in_=mainframe,
anchor=config.get_gui_conf("update_button_anchor"),
relx=config.get_gui_conf("update_button_pos")[0],
rely=config.get_gui_conf("update_button_pos")[1])
_image_to_disabled(update_button, "update")
music_var = IntVar()
music_var.set(config.main_conf_values["download_music"])
music_checkbox = Checkbutton(mainframe, text="Music", variable=music_var,
foreground=config.get_gui_conf("music_checkbox_select_color", "checkbox_fg_color"),
background=config.get_gui_conf("music_checkbox_bg_color", "checkbox_bg_color"),
selectcolor=config.get_gui_conf("music_checkbox_fg_color", "checkbox_select_color"),
borderwidth=0, pady=0, command=_do_check_update)
music_checkbox.place(in_=mainframe,
anchor=config.get_gui_conf("music_checkbox_anchor"),
relx=config.get_gui_conf("music_checkbox_pos")[0],
rely=config.get_gui_conf("music_checkbox_pos")[1])
overrides_var = IntVar()
overrides_var.set(config.main_conf_values["download_overrides"])
overrides_checkbox = Checkbutton(mainframe, text="Overrides", variable=overrides_var,
foreground=config.get_gui_conf("overrides_checkbox_select_color", "checkbox_fg_color"),
background=config.get_gui_conf("overrides_checkbox_bg_color", "checkbox_bg_color"),
selectcolor=config.get_gui_conf("overrides_checkbox_fg_color", "checkbox_select_color"),
borderwidth=0, pady=0, command=_do_check_update)
overrides_checkbox.place(in_=mainframe,
anchor=config.get_gui_conf("overrides_checkbox_anchor"),
relx=config.get_gui_conf("overrides_checkbox_pos")[0],
rely=config.get_gui_conf("overrides_checkbox_pos")[1])
portraits_var = IntVar()
portraits_var.set(config.main_conf_values["download_portraits"])
portraits_checkbox = Checkbutton(mainframe, text="Portraits", variable=portraits_var,
foreground=config.get_gui_conf("portraits_checkbox_select_color", "checkbox_fg_color"),
background=config.get_gui_conf("portraits_checkbox_bg_color", "checkbox_bg_color"),
selectcolor=config.get_gui_conf("portraits_checkbox_fg_color", "checkbox_select_color"),
borderwidth=0, pady=0, command=_do_check_update)
portraits_checkbox.place(in_=mainframe,
anchor=config.get_gui_conf("portraits_checkbox_anchor"),
relx=config.get_gui_conf("portraits_checkbox_pos")[0],
rely=config.get_gui_conf("portraits_checkbox_pos")[1])
overwrite_var = IntVar()
overwrite_var.set(config.main_conf_values["allow_overwrite"])
overwrite_checkbox = Checkbutton(mainframe, text="Allow overwriting dependencies", variable=overwrite_var,
foreground=config.get_gui_conf("overwrite_checkbox_select_color", "checkbox_fg_color"),
background=config.get_gui_conf("overwrite_checkbox_bg_color", "checkbox_bg_color"),
selectcolor=config.get_gui_conf("overwrite_checkbox_fg_color", "checkbox_select_color"),
borderwidth=0, pady=0, command=_do_check_update)
overwrite_checkbox.place(in_=mainframe,
anchor=config.get_gui_conf("overwrite_checkbox_anchor"),
relx=config.get_gui_conf("overwrite_checkbox_pos")[0],
rely=config.get_gui_conf("overwrite_checkbox_pos")[1])
# DM Checkbox
dm_var = IntVar()
dm_var.set(config.main_conf_values["login_as_dm"])
dm_checkbox = Checkbutton(mainframe, text="Connect as DM", variable=dm_var,
foreground=config.get_gui_conf("dm_checkbox_select_color", "checkbox_fg_color"),
background=config.get_gui_conf("dm_checkbox_bg_color", "checkbox_bg_color"),
selectcolor=config.get_gui_conf("dm_checkbox_fg_color", "checkbox_select_color"),
borderwidth=0, pady=0, command=_dm_checkbox_clicked)
dm_checkbox.place(in_=mainframe,
anchor=config.get_gui_conf("dm_checkbox_anchor"),
relx=config.get_gui_conf("dm_checkbox_pos")[0],
rely=config.get_gui_conf("dm_checkbox_pos")[1])
dm_pass_label = Label(text = "Pass: ", borderwidth = 3, width=0,
padx=2, pady=2,
font=config.get_gui_conf("dm_pass_label_font", "label_font"),
foreground=config.get_gui_conf("dm_pass_label_fg_color", "label_fg_color"),
background=config.get_gui_conf("dm_pass_label_bg_color", "label_bg_color"))
dm_pass_var = StringVar()
dm_pass_entry = Entry(mainframe, show='*', textvariable=dm_pass_var)
_dm_checkbox_clicked()
server_var = StringVar()
server_combobox = ttk.Combobox(mainframe, textvariable=server_var, width=29)
servers = []
for key, value in config.confs.items():
servers.append(value["name_full"])
server_combobox["values"] = servers
server_var.set(config.current_server_full_name)
server_combobox.state(['readonly'])
server_combobox.place(in_=mainframe,
anchor=config.get_gui_conf("server_combobox_anchor"),
relx=config.get_gui_conf("server_combobox_pos")[0],
rely=config.get_gui_conf("server_combobox_pos")[1])
server_var.trace("w", _change_server_conf)
update_button.bind("<Button-1>", _trigger_update)
#music_checkbox.bind("<Button-1>", _do_check_update)
#overrides_checkbox.bind("<Button-1>", _do_check_update)
#portraits_checkbox.bind("<Button-1>", _do_check_update)
update_status = StringVar()
update_status.set(dependency_manager.status + "\nHi" + "\nHello")
update_status_label = Label(mainframe, textvariable=update_status,
width=40, height=4, anchor="w",
justify=LEFT, borderwidth = 3,
relief = SUNKEN, padx=2, pady=2,
font=config.get_gui_conf("update_status_label_font", "label_font"),
foreground=config.get_gui_conf("update_status_label_fg_color", "label_fg_color"),
background=config.get_gui_conf("update_status_label_bg_color", "label_bg_color"))
update_status_label.place(in_=mainframe,
anchor=config.get_gui_conf("update_status_label_anchor"),
relx=config.get_gui_conf("update_status_label_pos")[0],
rely=config.get_gui_conf("update_status_label_pos")[1])
delete_overrides_button = _create_label_button("Delete overrides..", "clear", font="TkHeadingFont 10")
delete_overrides_button.place(in_=mainframe,
anchor=config.get_gui_conf("delete_overrides_button_anchor"),
relx=config.get_gui_conf("delete_overrides_button_pos")[0],
rely=config.get_gui_conf("delete_overrides_button_pos")[1])
delete_overrides_button.bind("<Button-1>", _trigger_delete_overrides)
_check_update_status()
LARGE_FONT= ("Verdana", 12)
NORM_FONT = ("Helvetica", 10)
SMALL_FONT = ("Helvetica", 8)
URL_FONT = ("Helvetica", 10, "underline")
root.after(50, _check_for_upgrade)
if config.main_conf_values["show_gog_warning"]:
root.after(50, _check_for_gog_cd_key)
if config.main_conf_values["show_hosts_file_popupmsg"] and not utilities._check_authentication_skip():
root.after(50, _hosts_file_popupmsg)
root.wm_protocol("WM_DELETE_WINDOW", on_closing)
root.mainloop()
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] | 2.604889 | 4,991 |
import unittest
from flask import request
from webargs import flaskparser
from tests import factories
from tests.common import ApiBaseTest
from webservices import args
from webservices import rest
from webservices import sorting
from webservices.common import models
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import pickle
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
df = pickle.load(open('./data/data_6model_offline712_online704_ensemble.pkl','rb'))
train_df = pd.read_csv('./data/train.csv')
IDS = train_df.id.unique()
dic_off_map = df[['id','offset_mapping']].set_index('id')['offset_mapping'].to_dict()
dic_txt = df[['id','text']].set_index('id')['text'].to_dict()
config = CONFIG()
id2label = {0:'Lead', 1:'Position', 2:'Evidence', 3:'Claim', 4:'Concluding Statement',
5:'Counterclaim', 6:'Rebuttal', 7:'blank'}
label2id = {v:k for k,v in id2label.items()}
preds1_mean = {}
preds2_mean = {}
for irow,row in df.iterrows():
t1, t2 = change_label(row.pred)
preds1_mean[row.id] = t1
preds2_mean[row.id] = t2
all_predictions = []
recall_thre = {
"Lead": 0.06,
"Position": 0.05,
"Evidence": 0.06,
"Claim": 0.05,
"Concluding Statement": 0.06,
"Counterclaim": 0.02,
"Rebuttal": 0.015,
}
# recall sample
for id in tqdm(preds1_mean):
pred1_np = np.array(preds1_mean[id])
pred2_np_all = np.array(preds2_mean[id])
off_map = dic_off_map[id]
off_map_len = len(off_map) if off_map[-1][1] != 0 else len(off_map)-1
max_length = min(config.max_length, off_map_len)
for class_num in range(7):
thre = recall_thre[id2label[class_num]]
pred2_np = pred2_np_all[:, class_num]
i_start = 0
while i_start < max_length:
i = 0
if pred1_np[i_start] > thre and pred2_np[i_start:i_start+10].max() > thre: #ๅผๅคด ไธคไธช้ๅผ
i = i_start + 1
if i>=max_length: break
while pred1_np[i] < (1-thre) and pred2_np[i:i+10].max() > thre: # ๆฏๅฆ็ปๆ ไธคไธช้ๅผ
cond = any([
i+1==max_length,
pred1_np[i] > thre,
i+1<max_length and pred2_np[i] < 0.7 and pred2_np[i] - pred2_np[i+1] > thre
])
if i>i_start+1 and cond:
all_predictions.append((id, id2label[class_num], [i_start, i]))
i += 1
if i>=max_length: break
if i != 0:
if i == max_length:
i -=1
all_predictions.append((id, id2label[class_num], [i_start, i]))
i_start += 1
print(len(all_predictions))
valid_pred = pd.DataFrame(all_predictions, columns=['id', 'class', 'pos'])
predictionstring = []
for cache in tqdm(valid_pred.values):
id = cache[0]
pos = cache[2]
off_map = dic_off_map[id]
txt = dic_txt[id]
txt_max = len(txt.split())
start_word = len(txt[:off_map[pos[0]][0]].split())
L = len(txt[off_map[pos[0]][0]:off_map[pos[1]][1]].split())
end_word = min(txt_max, start_word+L) - 1
predictionstring.append((start_word, end_word))
valid_pred['predictionstring'] = predictionstring
L_k = {
"Evidence": 0.85,
"Rebuttal": 0.6,
}
# select sample with high boundary threshold and choice 65% length with the highest probability of the current class as a new sample
valid_pred = deal_predictionstring(valid_pred)
valid_oof = train_df.copy()
tmp = valid_oof.predictionstring.map(lambda x:x.split())
tmp1 = [(int(x[0]),int(x[-1])) for x in tmp]
valid_oof['predictionstring'] = tmp1
def calc_overlap(row):
"""
Calculates the overlap between prediction and
ground truth and overlap percentages used for determining
true positives.
"""
try:
start_pred, end_pred = row.predictionstring_pred
start_gt, end_gt = row.predictionstring_gt
except:
return [0,0]
# Length of each and intersection
len_gt = end_gt - start_gt + 1
len_pred = end_pred - start_pred + 1
inter = min(end_pred, end_gt) - max(start_pred, start_gt) + 1
overlap_1 = inter / (len_gt+1e-5)
overlap_2 = inter / (len_pred+1e-5)
return [overlap_1, overlap_2]
gt_df = (
valid_oof[["id", "discourse_type", "predictionstring"]]
.reset_index(drop=True)
.copy()
)
pred_df = valid_pred[["id", "class", "predictionstring"]].reset_index(drop=True).copy()
pred_df["pred_id"] = pred_df.index
gt_df["gt_id"] = gt_df.index
# Step 1. all ground truths and predictions for a given class are compared.
joined = pred_df.merge(
gt_df,
left_on=["id", "class"],
right_on=["id", "discourse_type"],
how="outer",
suffixes=("_pred", "_gt"),
)
joined["predictionstring_gt"] = joined["predictionstring_gt"].fillna(" ")
joined["predictionstring_pred"] = joined["predictionstring_pred"].fillna(" ")
joined["overlaps"] = joined.apply(calc_overlap, axis=1)
joined["overlap1"] = joined["overlaps"].apply(lambda x: eval(str(x))[0])
joined["overlap2"] = joined["overlaps"].apply(lambda x: eval(str(x))[1])
joined["potential_TP"] = (joined["overlap1"] >= 0.5) & (joined["overlap2"] >= 0.5)
joined["max_overlap"] = joined[["overlap1", "overlap2"]].max(axis=1)
joined["min_overlap"] = joined[["overlap1", "overlap2"]].min(axis=1)
valid_pred['label'] = 0
valid_true_id = joined[joined.potential_TP==True]['pred_id']
valid_pred.loc[valid_true_id, 'label'] = 1
overlap = joined[['pred_id', 'min_overlap']]
overlap = overlap[~ overlap.pred_id.isna()]
overlap = overlap.groupby('pred_id')['min_overlap'].max().reset_index()
valid_pred = valid_pred.merge(overlap, left_index=True, right_on='pred_id', how='left')
valid_pred = valid_pred.drop('pred_id',axis=1)
pickle.dump(valid_pred, open('./data/recall_data.pkl','wb+')) | [
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# -*- coding: utf-8 -*-
###########################################################################
# Copyright (c), The AiiDA team. All rights reserved. #
# This file is part of the AiiDA code. #
# #
# The code is hosted on GitHub at https://github.com/aiidateam/aiida-core #
# For further information on the license, see the LICENSE.txt file #
# For further information please visit http://www.aiida.net #
###########################################################################
# pylint: disable=too-many-arguments,import-error,too-many-locals
"""`verdi export` command."""
import click
import tabulate
from aiida.cmdline.commands.cmd_verdi import verdi
from aiida.cmdline.params import arguments
from aiida.cmdline.params import options
from aiida.cmdline.utils import decorators
from aiida.cmdline.utils import echo
from aiida.common.links import GraphTraversalRules
@verdi.group('export')
def verdi_export():
"""Create and manage export archives."""
@verdi_export.command('inspect')
@click.argument('archive', nargs=1, type=click.Path(exists=True, readable=True))
@click.option('-v', '--version', is_flag=True, help='Print the archive format version and exit.')
@click.option('-d', '--data', is_flag=True, help='Print the data contents and exit.')
@click.option('-m', '--meta-data', is_flag=True, help='Print the meta data contents and exit.')
def inspect(archive, version, data, meta_data):
"""Inspect contents of an exported archive without importing it.
By default a summary of the archive contents will be printed. The various options can be used to change exactly what
information is displayed.
.. deprecated:: 1.5.0
Support for the --data flag
"""
import dataclasses
from aiida.tools.importexport import CorruptArchive, detect_archive_type, get_reader
reader_cls = get_reader(detect_archive_type(archive))
with reader_cls(archive) as reader:
try:
if version:
echo.echo(reader.export_version)
elif data:
# data is an internal implementation detail
echo.echo_deprecated('--data is deprecated and will be removed in v2.0.0')
echo.echo_dictionary(reader._get_data()) # pylint: disable=protected-access
elif meta_data:
echo.echo_dictionary(dataclasses.asdict(reader.metadata))
else:
statistics = {
'Version aiida': reader.metadata.aiida_version,
'Version format': reader.metadata.export_version,
'Computers': reader.entity_count('Computer'),
'Groups': reader.entity_count('Group'),
'Links': reader.link_count,
'Nodes': reader.entity_count('Node'),
'Users': reader.entity_count('User'),
}
if reader.metadata.conversion_info:
statistics['Conversion info'] = '\n'.join(reader.metadata.conversion_info)
echo.echo(tabulate.tabulate(statistics.items()))
except CorruptArchive as exception:
echo.echo_critical(f'corrupt archive: {exception}')
@verdi_export.command('create')
@arguments.OUTPUT_FILE(type=click.Path(exists=False))
@options.CODES()
@options.COMPUTERS()
@options.GROUPS()
@options.NODES()
@options.ARCHIVE_FORMAT()
@options.FORCE(help='overwrite output file if it already exists')
@click.option(
'-v',
'--verbosity',
default='INFO',
type=click.Choice(['DEBUG', 'INFO', 'WARNING', 'CRITICAL']),
help='Control the verbosity of console logging'
)
@options.graph_traversal_rules(GraphTraversalRules.EXPORT.value)
@click.option(
'--include-logs/--exclude-logs',
default=True,
show_default=True,
help='Include or exclude logs for node(s) in export.'
)
@click.option(
'--include-comments/--exclude-comments',
default=True,
show_default=True,
help='Include or exclude comments for node(s) in export. (Will also export extra users who commented).'
)
@decorators.with_dbenv()
def create(
output_file, codes, computers, groups, nodes, archive_format, force, input_calc_forward, input_work_forward,
create_backward, return_backward, call_calc_backward, call_work_backward, include_comments, include_logs, verbosity
):
"""
Export subsets of the provenance graph to file for sharing.
Besides Nodes of the provenance graph, you can export Groups, Codes, Computers, Comments and Logs.
By default, the archive file will include not only the entities explicitly provided via the command line but also
their provenance, according to the rules outlined in the documentation.
You can modify some of those rules using options of this command.
"""
from aiida.common.log import override_log_formatter_context
from aiida.common.progress_reporter import set_progress_bar_tqdm, set_progress_reporter
from aiida.tools.importexport import export, ExportFileFormat, EXPORT_LOGGER
from aiida.tools.importexport.common.exceptions import ArchiveExportError
entities = []
if codes:
entities.extend(codes)
if computers:
entities.extend(computers)
if groups:
entities.extend(groups)
if nodes:
entities.extend(nodes)
kwargs = {
'input_calc_forward': input_calc_forward,
'input_work_forward': input_work_forward,
'create_backward': create_backward,
'return_backward': return_backward,
'call_calc_backward': call_calc_backward,
'call_work_backward': call_work_backward,
'include_comments': include_comments,
'include_logs': include_logs,
'overwrite': force
}
if archive_format == 'zip':
export_format = ExportFileFormat.ZIP
kwargs.update({'use_compression': True})
elif archive_format == 'zip-uncompressed':
export_format = ExportFileFormat.ZIP
kwargs.update({'use_compression': False})
elif archive_format == 'tar.gz':
export_format = ExportFileFormat.TAR_GZIPPED
if verbosity in ['DEBUG', 'INFO']:
set_progress_bar_tqdm(leave=(verbosity == 'DEBUG'))
else:
set_progress_reporter(None)
EXPORT_LOGGER.setLevel(verbosity)
try:
with override_log_formatter_context('%(message)s'):
export(entities, filename=output_file, file_format=export_format, **kwargs)
except ArchiveExportError as exception:
echo.echo_critical(f'failed to write the archive file. Exception: {exception}')
else:
echo.echo_success(f'wrote the export archive file to {output_file}')
@verdi_export.command('migrate')
@arguments.INPUT_FILE()
@arguments.OUTPUT_FILE(required=False)
@options.ARCHIVE_FORMAT()
@options.FORCE(help='overwrite output file if it already exists')
@click.option('-i', '--in-place', is_flag=True, help='Migrate the archive in place, overwriting the original file.')
@options.SILENT()
@click.option(
'-v',
'--version',
type=click.STRING,
required=False,
metavar='VERSION',
# Note: Adding aiida.tools.EXPORT_VERSION as a default value explicitly would result in a slow import of
# aiida.tools and, as a consequence, aiida.orm. As long as this is the case, better determine the latest export
# version inside the function when needed.
help='Archive format version to migrate to (defaults to latest version).',
)
@click.option(
'--verbosity',
default='INFO',
type=click.Choice(['DEBUG', 'INFO', 'WARNING', 'CRITICAL']),
help='Control the verbosity of console logging'
)
def migrate(input_file, output_file, force, silent, in_place, archive_format, version, verbosity):
"""Migrate an export archive to a more recent format version.
.. deprecated:: 1.5.0
Support for the --silent flag, replaced by --verbosity
"""
from aiida.common.log import override_log_formatter_context
from aiida.common.progress_reporter import set_progress_bar_tqdm, set_progress_reporter
from aiida.tools.importexport import detect_archive_type, EXPORT_VERSION
from aiida.tools.importexport.archive.migrators import get_migrator, MIGRATE_LOGGER
if silent is True:
echo.echo_deprecated('the --silent option is deprecated, use --verbosity')
if in_place:
if output_file:
echo.echo_critical('output file specified together with --in-place flag')
output_file = input_file
force = True
elif not output_file:
echo.echo_critical(
'no output file specified. Please add --in-place flag if you would like to migrate in place.'
)
if verbosity in ['DEBUG', 'INFO']:
set_progress_bar_tqdm(leave=(verbosity == 'DEBUG'))
else:
set_progress_reporter(None)
MIGRATE_LOGGER.setLevel(verbosity)
if version is None:
version = EXPORT_VERSION
migrator_cls = get_migrator(detect_archive_type(input_file))
migrator = migrator_cls(input_file)
try:
with override_log_formatter_context('%(message)s'):
migrator.migrate(version, output_file, force=force, out_compression=archive_format)
except Exception as error: # pylint: disable=broad-except
if verbosity == 'DEBUG':
raise
echo.echo_critical(f'failed to migrate the archive file (use `--verbosity DEBUG` to see traceback): {error}')
if verbosity in ['DEBUG', 'INFO']:
echo.echo_success(f'migrated the archive to version {version}')
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] | 2.614301 | 3,692 |
from datetime import datetime
import unittest
from emma.model import SERIALIZED_DATETIME_ALT_FORMAT
from emma import exceptions as ex
from emma.model.account import Account
from emma.model.automation import Workflow
from tests.model import MockAdapter
class WorkflowTest(unittest.TestCase):
"""
Tests for the Workflow model
"""
def setUp(self):
"""
Set up tasks for our tests
"""
Account.default_adapter = MockAdapter
self.workflow = Workflow(
Account(account_id="100", public_key="xxx", private_key="yyy"),
{
'workflow_id': '22048a49-9533-4014-ae03-2af3598ed9a7',
'status': 'active',
'name': 'Test',
'created_at': datetime.now().strftime(SERIALIZED_DATETIME_ALT_FORMAT),
'updated_at': datetime.now().strftime(SERIALIZED_DATETIME_ALT_FORMAT),
}
)
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] | 2.172494 | 429 |
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session
from sqlalchemy.orm import sessionmaker
from configparser import ConfigParser
config = ConfigParser()
config.read('config.ini')
mysql_username = config.get('DATABASE', 'MYSQL_USER')
mysql_pwd = config.get('DATABASE', 'MYSQL_PWD')
mysql_host = config.get('DATABASE', 'MYSQL_HOST')
mysql_port = config.get('DATABASE', 'MYSQL_PORT')
mysql_database_name = config.get('DATABASE', 'MYSQL_DATABASE_NAME')
DATABASE = "mysql://{}:{}@{}:{}/{}?charset=utf8".format(
mysql_username,
mysql_pwd,
mysql_host,
mysql_port,
mysql_database_name
)
engine = create_engine(
DATABASE,
encoding="utf-8",
echo=True,
pool_size=50
)
Session = scoped_session(sessionmaker(autocommit=False, autoflush=False, bind=engine))
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] | 2.474164 | 329 |
import numpy as np
import os
pattern = ['Frequent_Pattern_init.txt', 'Frequent_Pattern_sd.txt']
path_list = ['../../data/Geolife Trajectories 1.3/Trajectories7000/', '../../data/Geolife Trajectories '
'1.3/sd/sd_final_MDL1100_ep0.1/',
'../../data/Geolife '
'Trajectories '
'1.3/sd/sd_final_MDL1100_ep0.5/',
'../../data/Geolife Trajectories 1.3/sd/sd_final_MDL1100_ep1.0/', '../../data/Geolife Trajectories '
'1.3/sd/sd_final_MDL1100_ep2.0/']
path_test = ['../../data/Geolife Trajectories 1.3/Trajectories7000/', '../../data/Geolife Trajectories '
'1.3/test/0/', '../../data/Geolife '
'Trajectories '
'1.3/test/1/',
'../../data/Geolife Trajectories 1.3/test/2/', '../../data/Geolife Trajectories '
'1.3/test/3/']
def get_Frequent_Pattern(init_path='../../data/Geolife Trajectories 1.3/Trajectories/',
min_latitude=39.6,
min_longitude=115.8,
len_latitude=1.2,
len_longitude=1.6, para="init"):
"""
ๅญๅจๆฐๆฎ็้ข็นๆจกๅผ
:param init_path:ๆฐๆฎ่ทฏๅพ
:param min_latitude:ๆๅฐ็บฌๅบฆ
:param min_longitude:ๆๅฐ็ปๅบฆ
:param len_latitude:็บฌๅบฆๅทฎๅผ
:param len_longitude:็ปๅบฆๅทฎๅผ
:param para:่ฎก็ฎ้ข็นๆจกๅผ,้ๆฉๆฐๆฎ้
:return:ๆๅบๅฅฝ็้ข็นๆจกๅผ้
"""
lat_accu = len_latitude / 6 # ็ปดๅบฆ่พน็่ทจๅบฆ
lon_accu = len_longitude / 6 # ็ปๅบฆ่พน็่ทจๅบฆ
# ๅญ้ข็นๆจกๅผ
Frequent_Pattern = {}
base_path_list = os.listdir(init_path)
for path in base_path_list:
file_object = open(init_path + path, 'r') # ่ฏปๅ่ฝจ่ฟนๆฐๆฎๆไปถ
T0 = []
for line in file_object.readlines():
w = float(line.strip().split(',')[0].strip())
w = int((w - min_latitude) / lat_accu) # ็ปดๅบฆๅฏนๅบ็ฝๆ ผ็ไฝ็ฝฎ
j = float(line.strip().split(',')[1].strip())
j = int((j - min_longitude) / lon_accu) # ็ปๅบฆๅฏนๅบ็ฝๆ ผ็ไฝ็ฝฎ
if len(T0) > 0 and w * 6 + j == T0[-1]: # ๆ้ค่ฟ็ปญๅบ็ฐๅจไธไธชๆ ผๅญ้้ข็ๆ
ๅต
continue
T0.append(w * 6 + j) # ๆ ผๅญ็็ผๅท
# # ๅญๆจกๅผๆๅ
# for i in range(len(T0) - 2):
# for j in range(i + 2, len(T0)):
# P = tuple(T0[i: j + 1].copy()) # ๆพ้ข็นๆจกๅผ(่ณๅฐๅคงไบ็ญไบไธไธชๅๅ
็ฝๆ ผ)
# if P in Frequent_Pattern.keys():
# Frequent_Pattern[P] += 1
# else:
# Frequent_Pattern[P] = 1
P = tuple(T0.copy())
if len(P) >= 3:
# print(P)
if P in Frequent_Pattern.keys():
Frequent_Pattern[P] += 1
else:
Frequent_Pattern[P] = 1
if para == "init":
f = open(pattern[0], 'w')
else:
f = open(pattern[1], 'w')
for record in Frequent_Pattern.keys():
f.writelines(str(record) + ':' + str(Frequent_Pattern[record]) + '\n')
f.close()
return sorted(Frequent_Pattern.items(), key=lambda x: x[1], reverse=True)
def get_Fredata(para="init"):
"""
่ทๅ้ข็นๆจกๅผๆฐๆฎ
:param para:่ฎก็ฎ้ข็นๆจกๅผ,้ๆฉๆฐๆฎ้
:return:
"""
if para == "init":
f = open(pattern[0], 'r')
else:
f = open(pattern[1], 'r')
Fre_dict = {}
for line in f.readlines():
Fre_dict[tuple((line.split(':')[0].strip()[1:-1]).split(','))] = int(line.split(':')[1].strip())
dict_ = {}
for item in sorted(Fre_dict.items(), key=lambda x: x[1], reverse=True):
dict_[item[0]] = item[1]
return dict_
def get_FP(init_dict, sd_dict):
"""
่ฎก็ฎFPๆๆ
:param init_dict:ๅๅงๆฐๆฎ็้ข็นๆจกๅผๅญๅ
ธ
:param sd_dict:็ๆๆฐๆฎ็้ข็นๆจกๅผๅญๅ
ธ
:return:FPๆๆ
"""
# sum_D = sum(list(init_dict.values()))
# sum_SD = sum(list(sd_dict.values()))
FP = 0
# for index, key in init_dict.items():
# init_dict[index] = key / sum_D
#
# for index, key in sd_dict.items():
# sd_dict[index] = key / sum_SD
for p in list(init_dict.keys())[:50]:
if p in sd_dict.keys():
re = abs(init_dict[p] - sd_dict[p]) / init_dict[p]
# print(re)
FP += re
# print(init_dict[p], sd_dict[p])
return FP / 50
def get_KT(init_dict, sd_dict):
"""
่ฎก็ฎKTๆๆ
:param init_dict:ๅๅงๆฐๆฎ็้ข็นๆจกๅผๅญๅ
ธ
:param sd_dict:็ๆๆฐๆฎ็้ข็นๆจกๅผๅญๅ
ธ
:return:KTๆๆ
"""
concor_count = 0
discor_count = 0
k = 0
# ๅๅ50ๅ็ๆนๆณ
for i in range(len(list(init_dict.keys()))):
if k >= 50:
break
if list(init_dict.keys())[i] in sd_dict.keys():
k += 1
for i in range(len(list(init_dict.keys())[:k])):
if list(init_dict.keys())[i] in sd_dict.keys():
for j in range(i + 1, len(list(init_dict.keys())[:k])):
if list(init_dict.keys())[j] in sd_dict.keys():
if (init_dict[list(init_dict.keys())[i]] >= init_dict[list(init_dict.keys())[j]] and sd_dict[
list(init_dict.keys())[i]] > sd_dict[list(init_dict.keys())[j]]) or \
(init_dict[list(init_dict.keys())[i]] < init_dict[list(init_dict.keys())[j]] and sd_dict[
list(init_dict.keys())[i]] < sd_dict[list(init_dict.keys())[j]]):
concor_count += 1
else:
discor_count += 1
# ๅฏนไบๆๆๆฐๆฎ้
union_ = extra_same_elem(list(init_dict.keys()), list(sd_dict.keys()))
for key in range(len(union_)):
for key2 in range(key+1, len(union_)):
if (init_dict[union_[key]] >= init_dict[union_[key2]] and sd_dict[
union_[key]] > sd_dict[union_[key2]]) or \
(init_dict[union_[key]] < init_dict[union_[key2]] and sd_dict[
union_[key]] < sd_dict[union_[key2]]):
concor_count += 1
else:
discor_count += 1
# print("KTๅทฎๅผ๏ผ", (concor_count - discor_count))
KT = (concor_count - discor_count) / (len(union_)*len(union_)-1 / 2)
return KT
if __name__ == '__main__':
# get_Frequent_Pattern(path_list[0],
# min_latitude=39.6,
# min_longitude=115.8,
# len_latitude=1.2,
# len_longitude=1.6,
# para="init")
dict_init = get_Fredata(para="init")
for i in range(1, len(path_test)):
get_Frequent_Pattern(path_test[i],
min_latitude=39.6,
min_longitude=115.8,
len_latitude=1.2,
len_longitude=1.6,
para="sd")
dict_sd = get_Fredata(para="sd")
FP = get_FP(dict_init, dict_sd)
KT = get_KT(dict_init, dict_sd)
print(path_test[i], FP, KT)
# input()
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106,
94,
163,
106,
245,
165,
95,
239,
163,
117,
223,
162,
101,
94,
28156,
237,
11,
34460,
231,
162,
233,
102,
46763,
108,
162,
235,
106,
37239,
228,
201,
198,
220,
220,
220,
1058,
7783,
25,
201,
198,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
611,
31215,
6624,
366,
15003,
1298,
201,
198,
220,
220,
220,
220,
220,
220,
220,
277,
796,
1280,
7,
33279,
58,
15,
4357,
705,
81,
11537,
201,
198,
220,
220,
220,
2073,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
277,
796,
1280,
7,
33279,
58,
16,
4357,
705,
81,
11537,
201,
198,
201,
198,
220,
220,
220,
4848,
62,
11600,
796,
23884,
201,
198,
220,
220,
220,
329,
1627,
287,
277,
13,
961,
6615,
33529,
201,
198,
220,
220,
220,
220,
220,
220,
220,
4848,
62,
11600,
58,
83,
29291,
19510,
1370,
13,
35312,
7,
10354,
11537,
58,
15,
4083,
36311,
3419,
58,
16,
21912,
16,
35944,
35312,
7,
41707,
4008,
60,
796,
493,
7,
1370,
13,
35312,
7,
10354,
11537,
58,
16,
4083,
36311,
28955,
201,
198,
220,
220,
220,
8633,
62,
796,
23884,
201,
198,
220,
220,
220,
329,
2378,
287,
23243,
7,
20366,
62,
11600,
13,
23814,
22784,
1994,
28,
50033,
2124,
25,
2124,
58,
16,
4357,
9575,
28,
17821,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
8633,
62,
58,
9186,
58,
15,
11907,
796,
2378,
58,
16,
60,
201,
198,
220,
220,
220,
1441,
8633,
62,
201,
198,
201,
198,
201,
198,
4299,
651,
62,
5837,
7,
15003,
62,
11600,
11,
45647,
62,
11600,
2599,
201,
198,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
5525,
106,
94,
163,
106,
245,
5837,
162,
234,
229,
43718,
229,
201,
198,
220,
220,
220,
1058,
17143,
2315,
62,
11600,
25,
43889,
253,
34650,
233,
46763,
108,
162,
235,
106,
21410,
165,
95,
239,
163,
117,
223,
162,
101,
94,
28156,
237,
27764,
245,
17739,
116,
201,
198,
220,
220,
220,
1058,
17143,
45647,
62,
11600,
25,
37955,
22755,
238,
46763,
108,
162,
235,
106,
21410,
165,
95,
239,
163,
117,
223,
162,
101,
94,
28156,
237,
27764,
245,
17739,
116,
201,
198,
220,
220,
220,
1058,
7783,
25,
5837,
162,
234,
229,
43718,
229,
201,
198,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
1303,
2160,
62,
35,
796,
2160,
7,
4868,
7,
15003,
62,
11600,
13,
27160,
3419,
4008,
201,
198,
220,
220,
220,
1303,
2160,
62,
10305,
796,
2160,
7,
4868,
7,
21282,
62,
11600,
13,
27160,
3419,
4008,
201,
198,
220,
220,
220,
31459,
796,
657,
201,
198,
220,
220,
220,
1303,
329,
6376,
11,
1994,
287,
2315,
62,
11600,
13,
23814,
33529,
201,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
2315,
62,
11600,
58,
9630,
60,
796,
1994,
1220,
2160,
62,
35,
201,
198,
220,
220,
220,
1303,
201,
198,
220,
220,
220,
1303,
329,
6376,
11,
1994,
287,
45647,
62,
11600,
13,
23814,
33529,
201,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
45647,
62,
11600,
58,
9630,
60,
796,
1994,
1220,
2160,
62,
10305,
201,
198,
201,
198,
220,
220,
220,
329,
279,
287,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
25,
1120,
5974,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
279,
287,
45647,
62,
11600,
13,
13083,
33529,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
302,
796,
2352,
7,
15003,
62,
11600,
58,
79,
60,
532,
45647,
62,
11600,
58,
79,
12962,
1220,
2315,
62,
11600,
58,
79,
60,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3601,
7,
260,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31459,
15853,
302,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3601,
7,
15003,
62,
11600,
58,
79,
4357,
45647,
62,
11600,
58,
79,
12962,
201,
198,
220,
220,
220,
1441,
31459,
1220,
2026,
201,
198,
201,
198,
201,
198,
201,
198,
4299,
651,
62,
42176,
7,
15003,
62,
11600,
11,
45647,
62,
11600,
2599,
201,
198,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
5525,
106,
94,
163,
106,
245,
42176,
162,
234,
229,
43718,
229,
201,
198,
220,
220,
220,
1058,
17143,
2315,
62,
11600,
25,
43889,
253,
34650,
233,
46763,
108,
162,
235,
106,
21410,
165,
95,
239,
163,
117,
223,
162,
101,
94,
28156,
237,
27764,
245,
17739,
116,
201,
198,
220,
220,
220,
1058,
17143,
45647,
62,
11600,
25,
37955,
22755,
238,
46763,
108,
162,
235,
106,
21410,
165,
95,
239,
163,
117,
223,
162,
101,
94,
28156,
237,
27764,
245,
17739,
116,
201,
198,
220,
220,
220,
1058,
7783,
25,
42176,
162,
234,
229,
43718,
229,
201,
198,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
1673,
273,
62,
9127,
796,
657,
201,
198,
220,
220,
220,
1221,
273,
62,
9127,
796,
657,
201,
198,
220,
220,
220,
479,
796,
657,
201,
198,
201,
198,
220,
220,
220,
1303,
10263,
237,
244,
30298,
235,
1120,
28938,
226,
21410,
43095,
37345,
243,
201,
198,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
4868,
7,
15003,
62,
11600,
13,
13083,
3419,
4008,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
18189,
2026,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
60,
287,
45647,
62,
11600,
13,
13083,
33529,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
15853,
352,
201,
198,
201,
198,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
25,
74,
12962,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
60,
287,
45647,
62,
11600,
13,
13083,
33529,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
2837,
7,
72,
1343,
352,
11,
18896,
7,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
25,
74,
12962,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
73,
60,
287,
45647,
62,
11600,
13,
13083,
33529,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
15003,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
11907,
18189,
2315,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
73,
11907,
290,
45647,
62,
11600,
58,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
11907,
1875,
45647,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
73,
11907,
8,
393,
3467,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
15003,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
11907,
1279,
2315,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
73,
11907,
290,
45647,
62,
11600,
58,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1351,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
72,
11907,
1279,
45647,
62,
11600,
58,
4868,
7,
15003,
62,
11600,
13,
13083,
28955,
58,
73,
11907,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1673,
273,
62,
9127,
15853,
352,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1221,
273,
62,
9127,
15853,
352,
201,
198,
201,
198,
220,
220,
220,
1303,
10263,
107,
117,
12859,
236,
33699,
222,
17312,
231,
46763,
108,
162,
235,
106,
37239,
228,
201,
198,
220,
220,
220,
6441,
62,
796,
3131,
62,
31642,
62,
68,
10671,
7,
4868,
7,
15003,
62,
11600,
13,
13083,
3419,
828,
1351,
7,
21282,
62,
11600,
13,
13083,
3419,
4008,
201,
198,
220,
220,
220,
329,
1994,
287,
2837,
7,
11925,
7,
24592,
62,
8,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
17,
287,
2837,
7,
2539,
10,
16,
11,
18896,
7,
24592,
62,
8,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
15003,
62,
11600,
58,
24592,
62,
58,
2539,
11907,
18189,
2315,
62,
11600,
58,
24592,
62,
58,
2539,
17,
11907,
290,
45647,
62,
11600,
58,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6441,
62,
58,
2539,
11907,
1875,
45647,
62,
11600,
58,
24592,
62,
58,
2539,
17,
11907,
8,
393,
3467,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
15003,
62,
11600,
58,
24592,
62,
58,
2539,
11907,
1279,
2315,
62,
11600,
58,
24592,
62,
58,
2539,
17,
11907,
290,
45647,
62,
11600,
58,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6441,
62,
58,
2539,
11907,
1279,
45647,
62,
11600,
58,
24592,
62,
58,
2539,
17,
11907,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1673,
273,
62,
9127,
15853,
352,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1221,
273,
62,
9127,
15853,
352,
201,
198,
220,
220,
220,
1303,
3601,
7203,
42176,
32432,
106,
161,
222,
120,
171,
120,
248,
1600,
357,
1102,
10215,
62,
9127,
532,
1221,
273,
62,
9127,
4008,
201,
198,
220,
220,
220,
42293,
796,
357,
1102,
10215,
62,
9127,
532,
1221,
273,
62,
9127,
8,
1220,
357,
11925,
7,
24592,
62,
27493,
11925,
7,
24592,
62,
13219,
16,
1220,
362,
8,
201,
198,
220,
220,
220,
1441,
42293,
201,
198,
201,
198,
201,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
201,
198,
220,
220,
220,
1303,
651,
62,
37,
46018,
62,
47546,
7,
6978,
62,
4868,
58,
15,
4357,
201,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
62,
15460,
3984,
28,
2670,
13,
21,
11,
201,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
62,
6511,
3984,
28,
15363,
13,
23,
11,
201,
198,
220,
220,
220,
1303,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 1.559005 | 4,703 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pytest
from day_012_gold_mining_back_of_envelope import share_price_calc
@pytest.fixture
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] | 2.365079 | 63 |
#!/usr/bin/python
__author__ = 'smetta'
# Import the module
import sys
import os
import socket
import json
import httplib
import sys
import base64
import ssl
import sys
import xml.etree.ElementTree as ET
import xml.dom.minidom as minidom
DOCUMENTATION = '''
---
module: vra_configure.py
Short_description: Module for configuring vra post gres database,messaging and clustering
description:
- Provides an interface for configuring post gres database, messaging and clustering
versoin_added: "0.1"
options:
vra_instance:
description:
- DNS or Ip address of the instance.
required: True
default: Null
vra_port:
description:
- port where the VRA listens default 5480
required: True
default: Null
vra_user:
description:
- user for the vra
required: True
default: Null
vra_root_password:
description:
- password to be used with user root
required: True
default: Null
vra_postgres_db
description:
- post gres db information such as user, password, database etc. please look below
required: false
default: Nulll
vra_messaging
description:
- messaging information such as host, port, user, password
required: false
default: Nulll
vra_clustering
description:
- clustering information such as leading node, admin user, password
required: false
default: Nulll
'''
EXAMPLES = '''
- name: vra_configure
ignore_errors: no
vra_postgresdb_setup:
vra_instance: "{{vra_instance}}"
vra_port: "{{vra_port}}"
vra_user: "{{vra_user}}"
vra_root_password: "{{vra_root_password}}"
vra_postgres_db:
host: "{{vra_postgres_host}}"
port: "{{vra_postgres_port}}"
database: "{{vra_postgres_database}}"
user: "{{vra_postgres_user}}"
password: "{{vra_postgres_password}}"
vra_messaging:
host: "{{vra_messaging_host}}"
port: "{{vra_messaging_port}}"
user: "{{vra_messaging_user}}"
password: "{{vra_messaging_password}}"
vra_cluster:
host: "{{vra_cluster_host}}"
user: "{{vra_cluster_user}}"
password: "{{vra_cluster_password}}"
'''
from ansible.module_utils.basic import *
from ansible.module_utils.facts import *
main()
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21412,
62,
26791,
13,
37473,
1330,
1635,
198,
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3419,
198
] | 2.343195 | 1,014 |
from django.contrib.auth.base_user import AbstractBaseUser
from django.db import models
from django.contrib.auth.base_user import BaseUserManager
from django.contrib.auth.models import PermissionsMixin
| [
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] | 3.474576 | 59 |
# Copyright 2014 Facebook, Inc.
# You are hereby granted a non-exclusive, worldwide, royalty-free license to
# use, copy, modify, and distribute this software in source code or binary
# form for use in connection with the web services and APIs provided by
# Facebook.
# As with any software that integrates with the Facebook platform, your use
# of this software is subject to the Facebook Developer Principles and
# Policies [http://developers.facebook.com/policy/]. This copyright notice
# shall be included in all copies or substantial portions of the software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from facebook_business.adobjects.abstractobject import AbstractObject
from facebook_business.adobjects.abstractcrudobject import AbstractCrudObject
from facebook_business.adobjects.objectparser import ObjectParser
from facebook_business.api import FacebookRequest
from facebook_business.typechecker import TypeChecker
"""
This class is auto-generated.
For any issues or feature requests related to this class, please let us know on
github and we'll fix in our codegen framework. We'll not be able to accept
pull request for this class.
"""
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] | 3.97 | 400 |
# Copyright (C) 2021 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 dataclasses
from typing import Any, Dict, List, Optional
from core.data import codenet_paths
from core.data import error_kinds
DEFAULT_DATASET_PATH = codenet_paths.DEFAULT_DATASET_PATH
@dataclasses.dataclass
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] | 3.484716 | 229 |
import collections
import json
import re
import urllib
from bs4 import BeautifulSoup
import HTMLParser
def get_properties (message):
"""
Processes a given chat message to identify particular properties: mentions,
emoticons and links. See the README.md file for more detailed documentation.
Args:
message (str): the chat message to process
Returns:
str: json data describing the properties of the message
Raises:
TypeError: if message parameter is not a string
Considerations:
Two approaches involving a tradeoff between performance and readability:
1. Scan message *for each* property's regular expression (more readable code)
2. Scan message *once* using a more complex regular expression (better performance)
I choose to implement #2 so that the solution scales better
"""
# validate message input data
if type(message) != str:
raise TypeError("message parameter must be a string")
# data structure that we're going to return (JSON)
properties = collections.defaultdict(list)
# regular expressions used to extract properties from message
REG_EXP = {
"mentions" : r"@\w+",
"emoticons" : r"\(\w+?\)",
"links" : r"https?://[^\s]+"
}
"""
For simplicity, assume that 'links' use the http(s) URL scheme.
The format of a URL can be very involved. A more correct, general approach
would be to include multiple schemes, follow the RFC for URL formats (RFC 3986, 1808), etc.
This would yield a complex regular expression outside the scope of this exercise.
"""
# regular expression pattern used for search
pattern = r"%s|%s|%s" % (REG_EXP["mentions"], REG_EXP["emoticons"], REG_EXP["links"])
for match in re.findall(pattern, message):
# setup key/value variables for properties dictionary
key, value = None, None
if re.match(REG_EXP["mentions"], match):
key = "mentions"
value = match[1:] # "@eric"
elif re.match(REG_EXP["emoticons"], match):
key = "emoticons"
value = match[1:-1] # "(smiley)"
elif re.match(REG_EXP["links"], match):
url = match # "http://twitter.com/"
# get title of URL
title = None
try:
f = urllib.urlopen(url)
html = f.read()
title = BeautifulSoup(html).title.string
except IOError as e:
sys.stderr.write("I/O error({0}): {1}\n".format(e.errno, e.strerror))
continue
except HTMLParser.HTMLParseError as e:
sys.stderr.write("HTMLParseError error({0}): {1}\n".format(e.errno, e.strerror))
continue
finally:
f.close()
key = "links"
value = {
"url" : url,
"title" : title
}
else:
sys.stderr.write("warning: the following match wasn't processed: {0}\n".format(match))
continue
# store processed key/value variables in properties *defaultdict*
properties[key].append(value)
return json.dumps(properties)
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220,
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220,
1441,
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13,
67,
8142,
7,
48310,
8,
198
] | 2.43221 | 1,335 |
#!usr/bin/env python
__author__ = "Xiaoguang Zhang"
__email__ = "[email protected]"
__copyright__ = "Copyright 2020 Westwood Robotics"
__date__ = "Jan 8, 2021"
__version__ = "0.1.2"
__status__ = "Production"
# Ping and search for available BEARs
from pybear import Manager
# Define port and baud rate
bear_port = "COM7"
bear_baudrate = 8000000
# Define ID search range
id_range = range(0, 9)
bear = Manager.BEAR(port=bear_port, baudrate=bear_baudrate)
bear_list = []
found = False
for i in id_range:
m_id = i
print("Pinging BEAR with ID %d" % m_id)
data = bear.ping(m_id)
if data:
print("Found BEAR with ID %d." % m_id)
found = True
bear_list.append(m_id)
if found:
print("Search done. Total of %d BEARs found. And their IDs are:\n" % len(bear_list))
print(bear_list)
else:
print("Search done. No BEAR found.")
| [
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18243,
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9348,
1503,
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198
] | 2.464789 | 355 |
from django.test import TestCase
import seaborn
import pandas
from matplotlib import pyplot as plt
from belleflopt import benefit, models, load
| [
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] | 3.418605 | 43 |
import torch
import torchaudio
import torch.utils.data
import torch.nn.functional as F
from librosa.core import load, resample
from librosa.util import normalize
from pathlib import Path
import numpy as np
import random
from modules.helper_functions import files_to_list
from dataset.audio_augs import AudioAugs
class AudioDataset(torch.utils.data.Dataset):
"""
This is the main class that calculates the spectrogram and returns the
spectrogram, audio pair.
"""
def load_wav_to_torch(self, full_path):
"""
Loads wavdata into torch array
"""
data, sampling_rate = load(full_path, sr=self.sampling_rate)
data = 0.95 * normalize(data)
data = torch.from_numpy(data).float()
if self.augment:
data = self.augment(data)
return data, sampling_rate | [
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1441,
1366,
11,
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] | 2.545723 | 339 |
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
# This code is refer from: https://github.com/viig99/LS-ACELoss
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
| [
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198,
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198,
198,
11748,
39517,
198,
11748,
39517,
13,
20471,
355,
299,
77,
628
] | 3.736364 | 220 |
import lightbulb
from hikari.permissions import Permissions
from lightbulb import checks, context
from sqlalchemy import select
from remi.db.engine import async_config_session
from remi.db.schema.config import StaffRole
@lightbulb.Check
@lightbulb.Check
| [
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] | 3.363636 | 77 |
# -*- coding: utf-8 -*-
from mahjong.constants import EAST, SOUTH, WEST, NORTH
from mahjong.hand_calculating.yaku import Yaku
from mahjong.utils import is_pon, is_pair
class Shousuushii(Yaku):
"""
The hand contains three sets of winds and a pair of the remaining wind
"""
| [
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] | 2.75 | 104 |
from collections import deque
from typing import List
from functools import reduce
if __name__ == '__main__':
input = get_input()
print(part_one(input))
print(part_one_alt(input))
print(part_two(input))
print(part_two_alt(input))
print(part_two_ext(input))
| [
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] | 2.651376 | 109 |
"""Support for Xiaomi Mi Air Purifier and Xiaomi Mi Air Humidifier."""
import asyncio
from enum import Enum
from functools import partial
import logging
from miio import ( # pylint: disable=import-error
AirFresh,
AirHumidifier,
AirPurifier,
Device,
DeviceException,
)
from miio.airfresh import ( # pylint: disable=import-error, import-error
LedBrightness as AirfreshLedBrightness,
OperationMode as AirfreshOperationMode,
)
from miio.airhumidifier import ( # pylint: disable=import-error, import-error
LedBrightness as AirhumidifierLedBrightness,
OperationMode as AirhumidifierOperationMode,
)
from miio.airpurifier import ( # pylint: disable=import-error, import-error
LedBrightness as AirpurifierLedBrightness,
OperationMode as AirpurifierOperationMode,
)
import voluptuous as vol
from homeassistant.components.fan import PLATFORM_SCHEMA, SUPPORT_SET_SPEED, FanEntity
from homeassistant.const import (
ATTR_ENTITY_ID,
ATTR_MODE,
CONF_HOST,
CONF_NAME,
CONF_TOKEN,
)
from homeassistant.exceptions import PlatformNotReady
import homeassistant.helpers.config_validation as cv
from .const import (
DOMAIN,
SERVICE_RESET_FILTER,
SERVICE_SET_AUTO_DETECT_OFF,
SERVICE_SET_AUTO_DETECT_ON,
SERVICE_SET_BUZZER_OFF,
SERVICE_SET_BUZZER_ON,
SERVICE_SET_CHILD_LOCK_OFF,
SERVICE_SET_CHILD_LOCK_ON,
SERVICE_SET_DRY_OFF,
SERVICE_SET_DRY_ON,
SERVICE_SET_EXTRA_FEATURES,
SERVICE_SET_FAVORITE_LEVEL,
SERVICE_SET_LEARN_MODE_OFF,
SERVICE_SET_LEARN_MODE_ON,
SERVICE_SET_LED_BRIGHTNESS,
SERVICE_SET_LED_OFF,
SERVICE_SET_LED_ON,
SERVICE_SET_TARGET_HUMIDITY,
SERVICE_SET_VOLUME,
)
_LOGGER = logging.getLogger(__name__)
DEFAULT_NAME = "Xiaomi Miio Device"
DATA_KEY = "fan.xiaomi_miio"
CONF_MODEL = "model"
MODEL_AIRPURIFIER_V1 = "zhimi.airpurifier.v1"
MODEL_AIRPURIFIER_V2 = "zhimi.airpurifier.v2"
MODEL_AIRPURIFIER_V3 = "zhimi.airpurifier.v3"
MODEL_AIRPURIFIER_V5 = "zhimi.airpurifier.v5"
MODEL_AIRPURIFIER_PRO = "zhimi.airpurifier.v6"
MODEL_AIRPURIFIER_PRO_V7 = "zhimi.airpurifier.v7"
MODEL_AIRPURIFIER_M1 = "zhimi.airpurifier.m1"
MODEL_AIRPURIFIER_M2 = "zhimi.airpurifier.m2"
MODEL_AIRPURIFIER_MA1 = "zhimi.airpurifier.ma1"
MODEL_AIRPURIFIER_MA2 = "zhimi.airpurifier.ma2"
MODEL_AIRPURIFIER_SA1 = "zhimi.airpurifier.sa1"
MODEL_AIRPURIFIER_SA2 = "zhimi.airpurifier.sa2"
MODEL_AIRPURIFIER_2S = "zhimi.airpurifier.mc1"
MODEL_AIRHUMIDIFIER_V1 = "zhimi.humidifier.v1"
MODEL_AIRHUMIDIFIER_CA1 = "zhimi.humidifier.ca1"
MODEL_AIRHUMIDIFIER_CB1 = "zhimi.humidifier.cb1"
MODEL_AIRFRESH_VA2 = "zhimi.airfresh.va2"
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_HOST): cv.string,
vol.Required(CONF_TOKEN): vol.All(cv.string, vol.Length(min=32, max=32)),
vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,
vol.Optional(CONF_MODEL): vol.In(
[
MODEL_AIRPURIFIER_V1,
MODEL_AIRPURIFIER_V2,
MODEL_AIRPURIFIER_V3,
MODEL_AIRPURIFIER_V5,
MODEL_AIRPURIFIER_PRO,
MODEL_AIRPURIFIER_PRO_V7,
MODEL_AIRPURIFIER_M1,
MODEL_AIRPURIFIER_M2,
MODEL_AIRPURIFIER_MA1,
MODEL_AIRPURIFIER_MA2,
MODEL_AIRPURIFIER_SA1,
MODEL_AIRPURIFIER_SA2,
MODEL_AIRPURIFIER_2S,
MODEL_AIRHUMIDIFIER_V1,
MODEL_AIRHUMIDIFIER_CA1,
MODEL_AIRHUMIDIFIER_CB1,
MODEL_AIRFRESH_VA2,
]
),
}
)
ATTR_MODEL = "model"
# Air Purifier
ATTR_TEMPERATURE = "temperature"
ATTR_HUMIDITY = "humidity"
ATTR_AIR_QUALITY_INDEX = "aqi"
ATTR_FILTER_HOURS_USED = "filter_hours_used"
ATTR_FILTER_LIFE = "filter_life_remaining"
ATTR_FAVORITE_LEVEL = "favorite_level"
ATTR_BUZZER = "buzzer"
ATTR_CHILD_LOCK = "child_lock"
ATTR_LED = "led"
ATTR_LED_BRIGHTNESS = "led_brightness"
ATTR_MOTOR_SPEED = "motor_speed"
ATTR_AVERAGE_AIR_QUALITY_INDEX = "average_aqi"
ATTR_PURIFY_VOLUME = "purify_volume"
ATTR_BRIGHTNESS = "brightness"
ATTR_LEVEL = "level"
ATTR_MOTOR2_SPEED = "motor2_speed"
ATTR_ILLUMINANCE = "illuminance"
ATTR_FILTER_RFID_PRODUCT_ID = "filter_rfid_product_id"
ATTR_FILTER_RFID_TAG = "filter_rfid_tag"
ATTR_FILTER_TYPE = "filter_type"
ATTR_LEARN_MODE = "learn_mode"
ATTR_SLEEP_TIME = "sleep_time"
ATTR_SLEEP_LEARN_COUNT = "sleep_mode_learn_count"
ATTR_EXTRA_FEATURES = "extra_features"
ATTR_FEATURES = "features"
ATTR_TURBO_MODE_SUPPORTED = "turbo_mode_supported"
ATTR_AUTO_DETECT = "auto_detect"
ATTR_SLEEP_MODE = "sleep_mode"
ATTR_VOLUME = "volume"
ATTR_USE_TIME = "use_time"
ATTR_BUTTON_PRESSED = "button_pressed"
# Air Humidifier
ATTR_TARGET_HUMIDITY = "target_humidity"
ATTR_TRANS_LEVEL = "trans_level"
ATTR_HARDWARE_VERSION = "hardware_version"
# Air Humidifier CA
ATTR_MOTOR_SPEED = "motor_speed"
ATTR_DEPTH = "depth"
ATTR_DRY = "dry"
# Air Fresh
ATTR_CO2 = "co2"
# Map attributes to properties of the state object
AVAILABLE_ATTRIBUTES_AIRPURIFIER_COMMON = {
ATTR_TEMPERATURE: "temperature",
ATTR_HUMIDITY: "humidity",
ATTR_AIR_QUALITY_INDEX: "aqi",
ATTR_MODE: "mode",
ATTR_FILTER_HOURS_USED: "filter_hours_used",
ATTR_FILTER_LIFE: "filter_life_remaining",
ATTR_FAVORITE_LEVEL: "favorite_level",
ATTR_CHILD_LOCK: "child_lock",
ATTR_LED: "led",
ATTR_MOTOR_SPEED: "motor_speed",
ATTR_AVERAGE_AIR_QUALITY_INDEX: "average_aqi",
ATTR_LEARN_MODE: "learn_mode",
ATTR_EXTRA_FEATURES: "extra_features",
ATTR_TURBO_MODE_SUPPORTED: "turbo_mode_supported",
ATTR_BUTTON_PRESSED: "button_pressed",
}
AVAILABLE_ATTRIBUTES_AIRPURIFIER = {
**AVAILABLE_ATTRIBUTES_AIRPURIFIER_COMMON,
ATTR_PURIFY_VOLUME: "purify_volume",
ATTR_SLEEP_TIME: "sleep_time",
ATTR_SLEEP_LEARN_COUNT: "sleep_mode_learn_count",
ATTR_AUTO_DETECT: "auto_detect",
ATTR_USE_TIME: "use_time",
ATTR_BUZZER: "buzzer",
ATTR_LED_BRIGHTNESS: "led_brightness",
ATTR_SLEEP_MODE: "sleep_mode",
}
AVAILABLE_ATTRIBUTES_AIRPURIFIER_PRO = {
**AVAILABLE_ATTRIBUTES_AIRPURIFIER_COMMON,
ATTR_PURIFY_VOLUME: "purify_volume",
ATTR_USE_TIME: "use_time",
ATTR_FILTER_RFID_PRODUCT_ID: "filter_rfid_product_id",
ATTR_FILTER_RFID_TAG: "filter_rfid_tag",
ATTR_FILTER_TYPE: "filter_type",
ATTR_ILLUMINANCE: "illuminance",
ATTR_MOTOR2_SPEED: "motor2_speed",
ATTR_VOLUME: "volume",
# perhaps supported but unconfirmed
ATTR_AUTO_DETECT: "auto_detect",
ATTR_SLEEP_TIME: "sleep_time",
ATTR_SLEEP_LEARN_COUNT: "sleep_mode_learn_count",
}
AVAILABLE_ATTRIBUTES_AIRPURIFIER_PRO_V7 = {
**AVAILABLE_ATTRIBUTES_AIRPURIFIER_COMMON,
ATTR_FILTER_RFID_PRODUCT_ID: "filter_rfid_product_id",
ATTR_FILTER_RFID_TAG: "filter_rfid_tag",
ATTR_FILTER_TYPE: "filter_type",
ATTR_ILLUMINANCE: "illuminance",
ATTR_MOTOR2_SPEED: "motor2_speed",
ATTR_VOLUME: "volume",
}
AVAILABLE_ATTRIBUTES_AIRPURIFIER_2S = {
**AVAILABLE_ATTRIBUTES_AIRPURIFIER_COMMON,
ATTR_BUZZER: "buzzer",
ATTR_FILTER_RFID_PRODUCT_ID: "filter_rfid_product_id",
ATTR_FILTER_RFID_TAG: "filter_rfid_tag",
ATTR_FILTER_TYPE: "filter_type",
ATTR_ILLUMINANCE: "illuminance",
}
AVAILABLE_ATTRIBUTES_AIRPURIFIER_V3 = {
# Common set isn't used here. It's a very basic version of the device.
ATTR_AIR_QUALITY_INDEX: "aqi",
ATTR_MODE: "mode",
ATTR_LED: "led",
ATTR_BUZZER: "buzzer",
ATTR_CHILD_LOCK: "child_lock",
ATTR_ILLUMINANCE: "illuminance",
ATTR_FILTER_HOURS_USED: "filter_hours_used",
ATTR_FILTER_LIFE: "filter_life_remaining",
ATTR_MOTOR_SPEED: "motor_speed",
# perhaps supported but unconfirmed
ATTR_AVERAGE_AIR_QUALITY_INDEX: "average_aqi",
ATTR_VOLUME: "volume",
ATTR_MOTOR2_SPEED: "motor2_speed",
ATTR_FILTER_RFID_PRODUCT_ID: "filter_rfid_product_id",
ATTR_FILTER_RFID_TAG: "filter_rfid_tag",
ATTR_FILTER_TYPE: "filter_type",
ATTR_PURIFY_VOLUME: "purify_volume",
ATTR_LEARN_MODE: "learn_mode",
ATTR_SLEEP_TIME: "sleep_time",
ATTR_SLEEP_LEARN_COUNT: "sleep_mode_learn_count",
ATTR_EXTRA_FEATURES: "extra_features",
ATTR_AUTO_DETECT: "auto_detect",
ATTR_USE_TIME: "use_time",
ATTR_BUTTON_PRESSED: "button_pressed",
}
AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER_COMMON = {
ATTR_TEMPERATURE: "temperature",
ATTR_HUMIDITY: "humidity",
ATTR_MODE: "mode",
ATTR_BUZZER: "buzzer",
ATTR_CHILD_LOCK: "child_lock",
ATTR_TARGET_HUMIDITY: "target_humidity",
ATTR_LED_BRIGHTNESS: "led_brightness",
ATTR_USE_TIME: "use_time",
ATTR_HARDWARE_VERSION: "hardware_version",
}
AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER = {
**AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER_COMMON,
ATTR_TRANS_LEVEL: "trans_level",
ATTR_BUTTON_PRESSED: "button_pressed",
}
AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER_CA_AND_CB = {
**AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER_COMMON,
ATTR_MOTOR_SPEED: "motor_speed",
ATTR_DEPTH: "depth",
ATTR_DRY: "dry",
}
AVAILABLE_ATTRIBUTES_AIRFRESH = {
ATTR_TEMPERATURE: "temperature",
ATTR_AIR_QUALITY_INDEX: "aqi",
ATTR_AVERAGE_AIR_QUALITY_INDEX: "average_aqi",
ATTR_CO2: "co2",
ATTR_HUMIDITY: "humidity",
ATTR_MODE: "mode",
ATTR_LED: "led",
ATTR_LED_BRIGHTNESS: "led_brightness",
ATTR_BUZZER: "buzzer",
ATTR_CHILD_LOCK: "child_lock",
ATTR_FILTER_LIFE: "filter_life_remaining",
ATTR_FILTER_HOURS_USED: "filter_hours_used",
ATTR_USE_TIME: "use_time",
ATTR_MOTOR_SPEED: "motor_speed",
ATTR_EXTRA_FEATURES: "extra_features",
}
OPERATION_MODES_AIRPURIFIER = ["Auto", "Silent", "Favorite", "Idle"]
OPERATION_MODES_AIRPURIFIER_PRO = ["Auto", "Silent", "Favorite"]
OPERATION_MODES_AIRPURIFIER_PRO_V7 = OPERATION_MODES_AIRPURIFIER_PRO
OPERATION_MODES_AIRPURIFIER_2S = ["Auto", "Silent", "Favorite"]
OPERATION_MODES_AIRPURIFIER_V3 = [
"Auto",
"Silent",
"Favorite",
"Idle",
"Medium",
"High",
"Strong",
]
OPERATION_MODES_AIRFRESH = ["Auto", "Silent", "Interval", "Low", "Middle", "Strong"]
SUCCESS = ["ok"]
FEATURE_SET_BUZZER = 1
FEATURE_SET_LED = 2
FEATURE_SET_CHILD_LOCK = 4
FEATURE_SET_LED_BRIGHTNESS = 8
FEATURE_SET_FAVORITE_LEVEL = 16
FEATURE_SET_AUTO_DETECT = 32
FEATURE_SET_LEARN_MODE = 64
FEATURE_SET_VOLUME = 128
FEATURE_RESET_FILTER = 256
FEATURE_SET_EXTRA_FEATURES = 512
FEATURE_SET_TARGET_HUMIDITY = 1024
FEATURE_SET_DRY = 2048
FEATURE_FLAGS_AIRPURIFIER = (
FEATURE_SET_BUZZER
| FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_LED_BRIGHTNESS
| FEATURE_SET_FAVORITE_LEVEL
| FEATURE_SET_LEARN_MODE
| FEATURE_RESET_FILTER
| FEATURE_SET_EXTRA_FEATURES
)
FEATURE_FLAGS_AIRPURIFIER_PRO = (
FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_FAVORITE_LEVEL
| FEATURE_SET_AUTO_DETECT
| FEATURE_SET_VOLUME
)
FEATURE_FLAGS_AIRPURIFIER_PRO_V7 = (
FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_FAVORITE_LEVEL
| FEATURE_SET_VOLUME
)
FEATURE_FLAGS_AIRPURIFIER_2S = (
FEATURE_SET_BUZZER
| FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_FAVORITE_LEVEL
)
FEATURE_FLAGS_AIRPURIFIER_V3 = (
FEATURE_SET_BUZZER | FEATURE_SET_CHILD_LOCK | FEATURE_SET_LED
)
FEATURE_FLAGS_AIRHUMIDIFIER = (
FEATURE_SET_BUZZER
| FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_LED_BRIGHTNESS
| FEATURE_SET_TARGET_HUMIDITY
)
FEATURE_FLAGS_AIRHUMIDIFIER_CA_AND_CB = FEATURE_FLAGS_AIRHUMIDIFIER | FEATURE_SET_DRY
FEATURE_FLAGS_AIRFRESH = (
FEATURE_SET_BUZZER
| FEATURE_SET_CHILD_LOCK
| FEATURE_SET_LED
| FEATURE_SET_LED_BRIGHTNESS
| FEATURE_RESET_FILTER
| FEATURE_SET_EXTRA_FEATURES
)
AIRPURIFIER_SERVICE_SCHEMA = vol.Schema({vol.Optional(ATTR_ENTITY_ID): cv.entity_ids})
SERVICE_SCHEMA_LED_BRIGHTNESS = AIRPURIFIER_SERVICE_SCHEMA.extend(
{vol.Required(ATTR_BRIGHTNESS): vol.All(vol.Coerce(int), vol.Clamp(min=0, max=2))}
)
SERVICE_SCHEMA_FAVORITE_LEVEL = AIRPURIFIER_SERVICE_SCHEMA.extend(
{vol.Required(ATTR_LEVEL): vol.All(vol.Coerce(int), vol.Clamp(min=0, max=17))}
)
SERVICE_SCHEMA_VOLUME = AIRPURIFIER_SERVICE_SCHEMA.extend(
{vol.Required(ATTR_VOLUME): vol.All(vol.Coerce(int), vol.Clamp(min=0, max=100))}
)
SERVICE_SCHEMA_EXTRA_FEATURES = AIRPURIFIER_SERVICE_SCHEMA.extend(
{vol.Required(ATTR_FEATURES): vol.All(vol.Coerce(int), vol.Range(min=0))}
)
SERVICE_SCHEMA_TARGET_HUMIDITY = AIRPURIFIER_SERVICE_SCHEMA.extend(
{
vol.Required(ATTR_HUMIDITY): vol.All(
vol.Coerce(int), vol.In([30, 40, 50, 60, 70, 80])
)
}
)
SERVICE_TO_METHOD = {
SERVICE_SET_BUZZER_ON: {"method": "async_set_buzzer_on"},
SERVICE_SET_BUZZER_OFF: {"method": "async_set_buzzer_off"},
SERVICE_SET_LED_ON: {"method": "async_set_led_on"},
SERVICE_SET_LED_OFF: {"method": "async_set_led_off"},
SERVICE_SET_CHILD_LOCK_ON: {"method": "async_set_child_lock_on"},
SERVICE_SET_CHILD_LOCK_OFF: {"method": "async_set_child_lock_off"},
SERVICE_SET_AUTO_DETECT_ON: {"method": "async_set_auto_detect_on"},
SERVICE_SET_AUTO_DETECT_OFF: {"method": "async_set_auto_detect_off"},
SERVICE_SET_LEARN_MODE_ON: {"method": "async_set_learn_mode_on"},
SERVICE_SET_LEARN_MODE_OFF: {"method": "async_set_learn_mode_off"},
SERVICE_RESET_FILTER: {"method": "async_reset_filter"},
SERVICE_SET_LED_BRIGHTNESS: {
"method": "async_set_led_brightness",
"schema": SERVICE_SCHEMA_LED_BRIGHTNESS,
},
SERVICE_SET_FAVORITE_LEVEL: {
"method": "async_set_favorite_level",
"schema": SERVICE_SCHEMA_FAVORITE_LEVEL,
},
SERVICE_SET_VOLUME: {"method": "async_set_volume", "schema": SERVICE_SCHEMA_VOLUME},
SERVICE_SET_EXTRA_FEATURES: {
"method": "async_set_extra_features",
"schema": SERVICE_SCHEMA_EXTRA_FEATURES,
},
SERVICE_SET_TARGET_HUMIDITY: {
"method": "async_set_target_humidity",
"schema": SERVICE_SCHEMA_TARGET_HUMIDITY,
},
SERVICE_SET_DRY_ON: {"method": "async_set_dry_on"},
SERVICE_SET_DRY_OFF: {"method": "async_set_dry_off"},
}
async def async_setup_platform(hass, config, async_add_entities, discovery_info=None):
"""Set up the miio fan device from config."""
if DATA_KEY not in hass.data:
hass.data[DATA_KEY] = {}
host = config[CONF_HOST]
token = config[CONF_TOKEN]
name = config[CONF_NAME]
model = config.get(CONF_MODEL)
_LOGGER.info("Initializing with host %s (token %s...)", host, token[:5])
unique_id = None
if model is None:
try:
miio_device = Device(host, token)
device_info = await hass.async_add_executor_job(miio_device.info)
model = device_info.model
unique_id = f"{model}-{device_info.mac_address}"
_LOGGER.info(
"%s %s %s detected",
model,
device_info.firmware_version,
device_info.hardware_version,
)
except DeviceException:
raise PlatformNotReady
if model.startswith("zhimi.airpurifier."):
air_purifier = AirPurifier(host, token)
device = XiaomiAirPurifier(name, air_purifier, model, unique_id)
elif model.startswith("zhimi.humidifier."):
air_humidifier = AirHumidifier(host, token, model=model)
device = XiaomiAirHumidifier(name, air_humidifier, model, unique_id)
elif model.startswith("zhimi.airfresh."):
air_fresh = AirFresh(host, token)
device = XiaomiAirFresh(name, air_fresh, model, unique_id)
else:
_LOGGER.error(
"Unsupported device found! Please create an issue at "
"https://github.com/syssi/xiaomi_airpurifier/issues "
"and provide the following data: %s",
model,
)
return False
hass.data[DATA_KEY][host] = device
async_add_entities([device], update_before_add=True)
async def async_service_handler(service):
"""Map services to methods on XiaomiAirPurifier."""
method = SERVICE_TO_METHOD.get(service.service)
params = {
key: value for key, value in service.data.items() if key != ATTR_ENTITY_ID
}
entity_ids = service.data.get(ATTR_ENTITY_ID)
if entity_ids:
devices = [
device
for device in hass.data[DATA_KEY].values()
if device.entity_id in entity_ids
]
else:
devices = hass.data[DATA_KEY].values()
update_tasks = []
for device in devices:
if not hasattr(device, method["method"]):
continue
await getattr(device, method["method"])(**params)
update_tasks.append(device.async_update_ha_state(True))
if update_tasks:
await asyncio.wait(update_tasks)
for air_purifier_service in SERVICE_TO_METHOD:
schema = SERVICE_TO_METHOD[air_purifier_service].get(
"schema", AIRPURIFIER_SERVICE_SCHEMA
)
hass.services.async_register(
DOMAIN, air_purifier_service, async_service_handler, schema=schema
)
class XiaomiGenericDevice(FanEntity):
"""Representation of a generic Xiaomi device."""
def __init__(self, name, device, model, unique_id):
"""Initialize the generic Xiaomi device."""
self._name = name
self._device = device
self._model = model
self._unique_id = unique_id
self._available = False
self._state = None
self._state_attrs = {ATTR_MODEL: self._model}
self._device_features = FEATURE_SET_CHILD_LOCK
self._skip_update = False
@property
def supported_features(self):
"""Flag supported features."""
return SUPPORT_SET_SPEED
@property
def should_poll(self):
"""Poll the device."""
return True
@property
def unique_id(self):
"""Return an unique ID."""
return self._unique_id
@property
def name(self):
"""Return the name of the device if any."""
return self._name
@property
def available(self):
"""Return true when state is known."""
return self._available
@property
def device_state_attributes(self):
"""Return the state attributes of the device."""
return self._state_attrs
@property
def is_on(self):
"""Return true if device is on."""
return self._state
@staticmethod
async def _try_command(self, mask_error, func, *args, **kwargs):
"""Call a miio device command handling error messages."""
try:
result = await self.hass.async_add_executor_job(
partial(func, *args, **kwargs)
)
_LOGGER.debug("Response received from miio device: %s", result)
return result == SUCCESS
except DeviceException as exc:
_LOGGER.error(mask_error, exc)
self._available = False
return False
async def async_turn_on(self, speed: str = None, **kwargs) -> None:
"""Turn the device on."""
if speed:
# If operation mode was set the device must not be turned on.
result = await self.async_set_speed(speed)
else:
result = await self._try_command(
"Turning the miio device on failed.", self._device.on
)
if result:
self._state = True
self._skip_update = True
async def async_turn_off(self, **kwargs) -> None:
"""Turn the device off."""
result = await self._try_command(
"Turning the miio device off failed.", self._device.off
)
if result:
self._state = False
self._skip_update = True
async def async_set_buzzer_on(self):
"""Turn the buzzer on."""
if self._device_features & FEATURE_SET_BUZZER == 0:
return
await self._try_command(
"Turning the buzzer of the miio device on failed.",
self._device.set_buzzer,
True,
)
async def async_set_buzzer_off(self):
"""Turn the buzzer off."""
if self._device_features & FEATURE_SET_BUZZER == 0:
return
await self._try_command(
"Turning the buzzer of the miio device off failed.",
self._device.set_buzzer,
False,
)
async def async_set_child_lock_on(self):
"""Turn the child lock on."""
if self._device_features & FEATURE_SET_CHILD_LOCK == 0:
return
await self._try_command(
"Turning the child lock of the miio device on failed.",
self._device.set_child_lock,
True,
)
async def async_set_child_lock_off(self):
"""Turn the child lock off."""
if self._device_features & FEATURE_SET_CHILD_LOCK == 0:
return
await self._try_command(
"Turning the child lock of the miio device off failed.",
self._device.set_child_lock,
False,
)
class XiaomiAirPurifier(XiaomiGenericDevice):
"""Representation of a Xiaomi Air Purifier."""
def __init__(self, name, device, model, unique_id):
"""Initialize the plug switch."""
super().__init__(name, device, model, unique_id)
if self._model == MODEL_AIRPURIFIER_PRO:
self._device_features = FEATURE_FLAGS_AIRPURIFIER_PRO
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRPURIFIER_PRO
self._speed_list = OPERATION_MODES_AIRPURIFIER_PRO
elif self._model == MODEL_AIRPURIFIER_PRO_V7:
self._device_features = FEATURE_FLAGS_AIRPURIFIER_PRO_V7
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRPURIFIER_PRO_V7
self._speed_list = OPERATION_MODES_AIRPURIFIER_PRO_V7
elif self._model == MODEL_AIRPURIFIER_2S:
self._device_features = FEATURE_FLAGS_AIRPURIFIER_2S
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRPURIFIER_2S
self._speed_list = OPERATION_MODES_AIRPURIFIER_2S
elif self._model == MODEL_AIRPURIFIER_V3:
self._device_features = FEATURE_FLAGS_AIRPURIFIER_V3
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRPURIFIER_V3
self._speed_list = OPERATION_MODES_AIRPURIFIER_V3
else:
self._device_features = FEATURE_FLAGS_AIRPURIFIER
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRPURIFIER
self._speed_list = OPERATION_MODES_AIRPURIFIER
self._state_attrs.update(
{attribute: None for attribute in self._available_attributes}
)
async def async_update(self):
"""Fetch state from the device."""
# On state change the device doesn't provide the new state immediately.
if self._skip_update:
self._skip_update = False
return
try:
state = await self.hass.async_add_executor_job(self._device.status)
_LOGGER.debug("Got new state: %s", state)
self._available = True
self._state = state.is_on
self._state_attrs.update(
{
key: self._extract_value_from_attribute(state, value)
for key, value in self._available_attributes.items()
}
)
except DeviceException as ex:
self._available = False
_LOGGER.error("Got exception while fetching the state: %s", ex)
@property
def speed_list(self) -> list:
"""Get the list of available speeds."""
return self._speed_list
@property
def speed(self):
"""Return the current speed."""
if self._state:
return AirpurifierOperationMode(self._state_attrs[ATTR_MODE]).name
return None
async def async_set_speed(self, speed: str) -> None:
"""Set the speed of the fan."""
if self.supported_features & SUPPORT_SET_SPEED == 0:
return
_LOGGER.debug("Setting the operation mode to: %s", speed)
await self._try_command(
"Setting operation mode of the miio device failed.",
self._device.set_mode,
AirpurifierOperationMode[speed.title()],
)
async def async_set_led_on(self):
"""Turn the led on."""
if self._device_features & FEATURE_SET_LED == 0:
return
await self._try_command(
"Turning the led of the miio device off failed.", self._device.set_led, True
)
async def async_set_led_off(self):
"""Turn the led off."""
if self._device_features & FEATURE_SET_LED == 0:
return
await self._try_command(
"Turning the led of the miio device off failed.",
self._device.set_led,
False,
)
async def async_set_led_brightness(self, brightness: int = 2):
"""Set the led brightness."""
if self._device_features & FEATURE_SET_LED_BRIGHTNESS == 0:
return
await self._try_command(
"Setting the led brightness of the miio device failed.",
self._device.set_led_brightness,
AirpurifierLedBrightness(brightness),
)
async def async_set_favorite_level(self, level: int = 1):
"""Set the favorite level."""
if self._device_features & FEATURE_SET_FAVORITE_LEVEL == 0:
return
await self._try_command(
"Setting the favorite level of the miio device failed.",
self._device.set_favorite_level,
level,
)
async def async_set_auto_detect_on(self):
"""Turn the auto detect on."""
if self._device_features & FEATURE_SET_AUTO_DETECT == 0:
return
await self._try_command(
"Turning the auto detect of the miio device on failed.",
self._device.set_auto_detect,
True,
)
async def async_set_auto_detect_off(self):
"""Turn the auto detect off."""
if self._device_features & FEATURE_SET_AUTO_DETECT == 0:
return
await self._try_command(
"Turning the auto detect of the miio device off failed.",
self._device.set_auto_detect,
False,
)
async def async_set_learn_mode_on(self):
"""Turn the learn mode on."""
if self._device_features & FEATURE_SET_LEARN_MODE == 0:
return
await self._try_command(
"Turning the learn mode of the miio device on failed.",
self._device.set_learn_mode,
True,
)
async def async_set_learn_mode_off(self):
"""Turn the learn mode off."""
if self._device_features & FEATURE_SET_LEARN_MODE == 0:
return
await self._try_command(
"Turning the learn mode of the miio device off failed.",
self._device.set_learn_mode,
False,
)
async def async_set_volume(self, volume: int = 50):
"""Set the sound volume."""
if self._device_features & FEATURE_SET_VOLUME == 0:
return
await self._try_command(
"Setting the sound volume of the miio device failed.",
self._device.set_volume,
volume,
)
async def async_set_extra_features(self, features: int = 1):
"""Set the extra features."""
if self._device_features & FEATURE_SET_EXTRA_FEATURES == 0:
return
await self._try_command(
"Setting the extra features of the miio device failed.",
self._device.set_extra_features,
features,
)
async def async_reset_filter(self):
"""Reset the filter lifetime and usage."""
if self._device_features & FEATURE_RESET_FILTER == 0:
return
await self._try_command(
"Resetting the filter lifetime of the miio device failed.",
self._device.reset_filter,
)
class XiaomiAirHumidifier(XiaomiGenericDevice):
"""Representation of a Xiaomi Air Humidifier."""
def __init__(self, name, device, model, unique_id):
"""Initialize the plug switch."""
super().__init__(name, device, model, unique_id)
if self._model in [MODEL_AIRHUMIDIFIER_CA1, MODEL_AIRHUMIDIFIER_CB1]:
self._device_features = FEATURE_FLAGS_AIRHUMIDIFIER_CA_AND_CB
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER_CA_AND_CB
self._speed_list = [
mode.name
for mode in AirhumidifierOperationMode
if mode is not AirhumidifierOperationMode.Strong
]
else:
self._device_features = FEATURE_FLAGS_AIRHUMIDIFIER
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRHUMIDIFIER
self._speed_list = [
mode.name
for mode in AirhumidifierOperationMode
if mode is not AirhumidifierOperationMode.Auto
]
self._state_attrs.update(
{attribute: None for attribute in self._available_attributes}
)
async def async_update(self):
"""Fetch state from the device."""
# On state change the device doesn't provide the new state immediately.
if self._skip_update:
self._skip_update = False
return
try:
state = await self.hass.async_add_executor_job(self._device.status)
_LOGGER.debug("Got new state: %s", state)
self._available = True
self._state = state.is_on
self._state_attrs.update(
{
key: self._extract_value_from_attribute(state, value)
for key, value in self._available_attributes.items()
}
)
except DeviceException as ex:
self._available = False
_LOGGER.error("Got exception while fetching the state: %s", ex)
@property
def speed_list(self) -> list:
"""Get the list of available speeds."""
return self._speed_list
@property
def speed(self):
"""Return the current speed."""
if self._state:
return AirhumidifierOperationMode(self._state_attrs[ATTR_MODE]).name
return None
async def async_set_speed(self, speed: str) -> None:
"""Set the speed of the fan."""
if self.supported_features & SUPPORT_SET_SPEED == 0:
return
_LOGGER.debug("Setting the operation mode to: %s", speed)
await self._try_command(
"Setting operation mode of the miio device failed.",
self._device.set_mode,
AirhumidifierOperationMode[speed.title()],
)
async def async_set_led_brightness(self, brightness: int = 2):
"""Set the led brightness."""
if self._device_features & FEATURE_SET_LED_BRIGHTNESS == 0:
return
await self._try_command(
"Setting the led brightness of the miio device failed.",
self._device.set_led_brightness,
AirhumidifierLedBrightness(brightness),
)
async def async_set_target_humidity(self, humidity: int = 40):
"""Set the target humidity."""
if self._device_features & FEATURE_SET_TARGET_HUMIDITY == 0:
return
await self._try_command(
"Setting the target humidity of the miio device failed.",
self._device.set_target_humidity,
humidity,
)
async def async_set_dry_on(self):
"""Turn the dry mode on."""
if self._device_features & FEATURE_SET_DRY == 0:
return
await self._try_command(
"Turning the dry mode of the miio device off failed.",
self._device.set_dry,
True,
)
async def async_set_dry_off(self):
"""Turn the dry mode off."""
if self._device_features & FEATURE_SET_DRY == 0:
return
await self._try_command(
"Turning the dry mode of the miio device off failed.",
self._device.set_dry,
False,
)
class XiaomiAirFresh(XiaomiGenericDevice):
"""Representation of a Xiaomi Air Fresh."""
def __init__(self, name, device, model, unique_id):
"""Initialize the miio device."""
super().__init__(name, device, model, unique_id)
self._device_features = FEATURE_FLAGS_AIRFRESH
self._available_attributes = AVAILABLE_ATTRIBUTES_AIRFRESH
self._speed_list = OPERATION_MODES_AIRFRESH
self._state_attrs.update(
{attribute: None for attribute in self._available_attributes}
)
async def async_update(self):
"""Fetch state from the device."""
# On state change the device doesn't provide the new state immediately.
if self._skip_update:
self._skip_update = False
return
try:
state = await self.hass.async_add_executor_job(self._device.status)
_LOGGER.debug("Got new state: %s", state)
self._available = True
self._state = state.is_on
self._state_attrs.update(
{
key: self._extract_value_from_attribute(state, value)
for key, value in self._available_attributes.items()
}
)
except DeviceException as ex:
self._available = False
_LOGGER.error("Got exception while fetching the state: %s", ex)
@property
def speed_list(self) -> list:
"""Get the list of available speeds."""
return self._speed_list
@property
def speed(self):
"""Return the current speed."""
if self._state:
return AirfreshOperationMode(self._state_attrs[ATTR_MODE]).name
return None
async def async_set_speed(self, speed: str) -> None:
"""Set the speed of the fan."""
if self.supported_features & SUPPORT_SET_SPEED == 0:
return
_LOGGER.debug("Setting the operation mode to: %s", speed)
await self._try_command(
"Setting operation mode of the miio device failed.",
self._device.set_mode,
AirfreshOperationMode[speed.title()],
)
async def async_set_led_on(self):
"""Turn the led on."""
if self._device_features & FEATURE_SET_LED == 0:
return
await self._try_command(
"Turning the led of the miio device off failed.", self._device.set_led, True
)
async def async_set_led_off(self):
"""Turn the led off."""
if self._device_features & FEATURE_SET_LED == 0:
return
await self._try_command(
"Turning the led of the miio device off failed.",
self._device.set_led,
False,
)
async def async_set_led_brightness(self, brightness: int = 2):
"""Set the led brightness."""
if self._device_features & FEATURE_SET_LED_BRIGHTNESS == 0:
return
await self._try_command(
"Setting the led brightness of the miio device failed.",
self._device.set_led_brightness,
AirfreshLedBrightness(brightness),
)
async def async_set_extra_features(self, features: int = 1):
"""Set the extra features."""
if self._device_features & FEATURE_SET_EXTRA_FEATURES == 0:
return
await self._try_command(
"Setting the extra features of the miio device failed.",
self._device.set_extra_features,
features,
)
async def async_reset_filter(self):
"""Reset the filter lifetime and usage."""
if self._device_features & FEATURE_RESET_FILTER == 0:
return
await self._try_command(
"Resetting the filter lifetime of the miio device failed.",
self._device.reset_filter,
)
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25507,
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8973,
5769,
1174,
37266,
8,
198,
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220,
220,
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220,
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4296,
62,
83,
6791,
13,
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7,
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13,
292,
13361,
62,
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62,
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62,
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18630,
40086,
62,
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62,
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47,
4261,
5064,
38311,
62,
31190,
62,
53,
22,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12287,
62,
4868,
796,
43521,
6234,
62,
33365,
1546,
62,
42149,
47,
4261,
5064,
38311,
62,
31190,
62,
53,
22,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13557,
19849,
6624,
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62,
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62,
17,
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198,
220,
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220,
2116,
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25202,
62,
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796,
18630,
40086,
62,
38948,
50,
62,
42149,
47,
4261,
5064,
38311,
62,
17,
50,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
62,
1078,
7657,
796,
317,
11731,
4146,
17534,
62,
1404,
5446,
9865,
3843,
1546,
62,
42149,
47,
4261,
5064,
38311,
62,
17,
50,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12287,
62,
4868,
796,
43521,
6234,
62,
33365,
1546,
62,
42149,
47,
4261,
5064,
38311,
62,
17,
50,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13557,
19849,
6624,
19164,
3698,
62,
42149,
47,
4261,
5064,
38311,
62,
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18,
25,
198,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
2116,
13557,
25202,
62,
40890,
796,
18630,
40086,
62,
38948,
50,
62,
42149,
47,
4261,
5064,
38311,
62,
53,
18,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
62,
1078,
7657,
796,
317,
11731,
4146,
17534,
62,
1404,
5446,
9865,
3843,
1546,
62,
42149,
47,
4261,
5064,
38311,
62,
53,
18,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12287,
62,
4868,
796,
43521,
6234,
62,
33365,
1546,
62,
42149,
47,
4261,
5064,
38311,
62,
53,
18,
198,
220,
220,
220,
220,
220,
220,
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2073,
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220,
220,
220,
2116,
13557,
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40890,
796,
18630,
40086,
62,
38948,
50,
62,
42149,
47,
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198,
220,
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2116,
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62,
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317,
11731,
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17534,
62,
1404,
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198,
220,
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220,
2116,
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12287,
62,
4868,
796,
43521,
6234,
62,
33365,
1546,
62,
42149,
47,
4261,
5064,
38311,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
5219,
62,
1078,
3808,
13,
19119,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
42348,
25,
6045,
329,
11688,
287,
2116,
13557,
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62,
1078,
7657,
92,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
30351,
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62,
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198,
220,
220,
220,
220,
220,
220,
220,
37227,
37,
7569,
1181,
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262,
3335,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
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1181,
1487,
262,
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2148,
262,
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13,
198,
220,
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220,
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220,
611,
2116,
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48267,
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25,
198,
220,
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220,
220,
220,
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220,
2116,
13557,
48267,
62,
19119,
796,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
1949,
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198,
220,
220,
220,
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220,
220,
220,
220,
220,
1181,
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13,
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62,
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62,
21858,
7,
944,
13557,
25202,
13,
13376,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
24442,
7203,
30074,
649,
1181,
25,
4064,
82,
1600,
1181,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
5219,
796,
1181,
13,
271,
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261,
198,
220,
220,
220,
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220,
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220,
220,
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2116,
13557,
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62,
1078,
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13,
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198,
220,
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220,
220,
220,
220,
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1391,
198,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
2116,
13557,
2302,
974,
62,
8367,
62,
6738,
62,
42348,
7,
5219,
11,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
2116,
13557,
15182,
62,
1078,
7657,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
2845,
16232,
16922,
355,
409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
18224,
7203,
30074,
6631,
981,
21207,
278,
262,
1181,
25,
4064,
82,
1600,
409,
8,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
2866,
62,
4868,
7,
944,
8,
4613,
1351,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
262,
1351,
286,
1695,
12055,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
12287,
62,
4868,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
2866,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
13615,
262,
1459,
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526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
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25,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
1441,
3701,
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32180,
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7,
944,
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5219,
62,
1078,
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58,
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62,
49058,
35944,
3672,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
30351,
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62,
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7,
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11,
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25,
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25,
198,
220,
220,
220,
220,
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37227,
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262,
2866,
286,
262,
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526,
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198,
220,
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13,
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62,
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62,
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62,
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41841,
6624,
657,
25,
198,
220,
220,
220,
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220,
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1441,
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220,
220,
220,
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220,
220,
220,
4808,
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13,
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262,
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284,
25,
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82,
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8,
628,
220,
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198,
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220,
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2116,
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13,
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198,
220,
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58,
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13,
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198,
220,
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220,
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1267,
628,
220,
220,
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62,
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62,
261,
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37227,
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319,
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198,
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62,
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13,
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62,
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11,
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198,
220,
220,
220,
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220,
220,
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628,
220,
220,
220,
30351,
825,
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62,
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62,
992,
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198,
220,
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220,
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262,
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572,
526,
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198,
220,
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657,
25,
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220,
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220,
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62,
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198,
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572,
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13,
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198,
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198,
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526,
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657,
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11,
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11,
198,
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628,
220,
220,
220,
30351,
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62,
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478,
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261,
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198,
220,
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220,
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17278,
262,
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319,
526,
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198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
62,
40890,
1222,
18630,
40086,
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46,
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35,
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9782,
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657,
25,
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220,
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220,
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628,
220,
220,
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2116,
13557,
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198,
220,
220,
220,
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62,
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25,
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198,
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2116,
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13,
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62,
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62,
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11,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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11,
198,
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220,
220,
220,
220,
220,
220,
1267,
628,
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220,
220,
30351,
825,
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62,
2617,
62,
35720,
62,
14171,
62,
2364,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
17278,
262,
2193,
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572,
526,
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198,
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220,
220,
220,
220,
220,
220,
611,
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62,
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1222,
18630,
40086,
62,
28480,
62,
2538,
1503,
45,
62,
49058,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
25507,
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13557,
28311,
62,
21812,
7,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
366,
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572,
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198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
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25202,
13,
2617,
62,
35720,
62,
14171,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10352,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
30351,
825,
30351,
62,
2617,
62,
29048,
7,
944,
11,
6115,
25,
493,
796,
2026,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7248,
262,
2128,
6115,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
62,
40890,
1222,
18630,
40086,
62,
28480,
62,
44558,
38340,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
25507,
2116,
13557,
28311,
62,
21812,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
34149,
262,
2128,
6115,
286,
262,
21504,
952,
3335,
4054,
33283,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
25202,
13,
2617,
62,
29048,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6115,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
30351,
825,
30351,
62,
2617,
62,
26086,
62,
40890,
7,
944,
11,
3033,
25,
493,
796,
352,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7248,
262,
3131,
3033,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
62,
40890,
1222,
18630,
40086,
62,
28480,
62,
13918,
3861,
62,
15112,
47471,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
25507,
2116,
13557,
28311,
62,
21812,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
34149,
262,
3131,
3033,
286,
262,
21504,
952,
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4054,
33283,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
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13,
2617,
62,
26086,
62,
40890,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3033,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
30351,
825,
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62,
42503,
62,
24455,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
4965,
316,
262,
8106,
10869,
290,
8748,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
62,
40890,
1222,
18630,
40086,
62,
19535,
2767,
62,
46700,
5781,
6624,
657,
25,
198,
220,
220,
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220,
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220,
220,
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628,
220,
220,
220,
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62,
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198,
220,
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262,
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952,
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220,
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13,
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220,
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628,
198,
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55,
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198,
220,
220,
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312,
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526,
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628,
220,
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220,
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11,
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11,
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312,
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198,
220,
220,
220,
220,
220,
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262,
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5078,
526,
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198,
220,
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11,
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62,
312,
8,
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220,
220,
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220,
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19849,
287,
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62,
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62,
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16,
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198,
220,
220,
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220,
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220,
220,
220,
220,
2116,
13557,
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40890,
796,
18630,
40086,
62,
38948,
50,
62,
42149,
39,
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5064,
38311,
62,
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62,
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62,
23199,
198,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
2116,
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15182,
62,
1078,
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796,
317,
11731,
4146,
17534,
62,
1404,
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9865,
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1546,
62,
42149,
39,
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5064,
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62,
8141,
62,
6981,
62,
23199,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12287,
62,
4868,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
13,
3672,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4235,
287,
3701,
17047,
312,
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32180,
19076,
198,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
611,
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318,
407,
3701,
17047,
312,
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32180,
19076,
13,
33004,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
25202,
62,
40890,
796,
18630,
40086,
62,
38948,
50,
62,
42149,
39,
5883,
2389,
5064,
38311,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
62,
1078,
7657,
796,
317,
11731,
4146,
17534,
62,
1404,
5446,
9865,
3843,
1546,
62,
42149,
39,
5883,
2389,
5064,
38311,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12287,
62,
4868,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4235,
13,
3672,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4235,
287,
3701,
17047,
312,
7483,
32180,
19076,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4235,
318,
407,
3701,
17047,
312,
7483,
32180,
19076,
13,
27722,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
5219,
62,
1078,
3808,
13,
19119,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
42348,
25,
6045,
329,
11688,
287,
2116,
13557,
15182,
62,
1078,
7657,
92,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
30351,
825,
30351,
62,
19119,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
37,
7569,
1181,
422,
262,
3335,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1550,
1181,
1487,
262,
3335,
1595,
470,
2148,
262,
649,
1181,
3393,
13,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
48267,
62,
19119,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
48267,
62,
19119,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1181,
796,
25507,
2116,
13,
71,
562,
13,
292,
13361,
62,
2860,
62,
18558,
38409,
62,
21858,
7,
944,
13557,
25202,
13,
13376,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
24442,
7203,
30074,
649,
1181,
25,
4064,
82,
1600,
1181,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
5219,
796,
1181,
13,
271,
62,
261,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
5219,
62,
1078,
3808,
13,
19119,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
2116,
13557,
2302,
974,
62,
8367,
62,
6738,
62,
42348,
7,
5219,
11,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
2116,
13557,
15182,
62,
1078,
7657,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
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25,
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2116,
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220,
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220,
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13,
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278,
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25,
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82,
1600,
409,
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628,
220,
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198,
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25,
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262,
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286,
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526,
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198,
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628,
220,
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26745,
198,
220,
220,
220,
825,
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198,
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262,
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526,
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198,
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220,
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220,
611,
2116,
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25,
198,
220,
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312,
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62,
1078,
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58,
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49058,
35944,
3672,
628,
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220,
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7,
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11,
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25,
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6045,
25,
198,
220,
220,
220,
220,
220,
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262,
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286,
262,
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526,
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198,
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220,
220,
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220,
220,
220,
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13,
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62,
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62,
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6624,
657,
25,
198,
220,
220,
220,
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220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
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25294,
30373,
13,
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262,
4905,
4235,
284,
25,
4064,
82,
1600,
2866,
8,
628,
220,
220,
220,
220,
220,
220,
220,
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2116,
13557,
28311,
62,
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198,
220,
220,
220,
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220,
220,
220,
220,
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220,
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286,
262,
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952,
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4054,
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198,
220,
220,
220,
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220,
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220,
220,
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2116,
13557,
25202,
13,
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14171,
11,
198,
220,
220,
220,
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220,
220,
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220,
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220,
3701,
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312,
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32180,
19076,
58,
12287,
13,
7839,
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198,
220,
220,
220,
220,
220,
220,
220,
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628,
220,
220,
220,
30351,
825,
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11,
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25,
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796,
362,
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198,
220,
220,
220,
220,
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220,
220,
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262,
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22204,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
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40890,
1222,
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62,
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62,
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6624,
657,
25,
198,
220,
220,
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628,
220,
220,
220,
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62,
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198,
220,
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220,
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11,
198,
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198,
220,
220,
220,
220,
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628,
220,
220,
220,
30351,
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11,
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25,
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198,
220,
220,
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220,
220,
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262,
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27716,
526,
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198,
220,
220,
220,
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220,
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2116,
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220,
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198,
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220,
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220,
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198,
220,
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262,
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319,
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198,
220,
220,
220,
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220,
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2116,
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56,
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25,
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220,
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262,
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262,
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220,
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628,
220,
220,
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944,
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198,
220,
220,
220,
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220,
220,
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17278,
262,
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572,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13557,
25202,
62,
40890,
1222,
18630,
40086,
62,
28480,
62,
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56,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
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220,
220,
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2116,
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28311,
62,
21812,
7,
198,
220,
220,
220,
220,
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220,
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366,
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278,
262,
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286,
262,
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572,
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198,
220,
220,
220,
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13,
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11,
198,
220,
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220,
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220,
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220,
220,
220,
10352,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
198,
4871,
46726,
16170,
35857,
7,
55,
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12753,
46189,
24728,
2599,
198,
220,
220,
220,
37227,
40171,
341,
286,
257,
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3701,
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526,
15931,
628,
220,
220,
220,
825,
11593,
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11,
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11,
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11,
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11,
3748,
62,
312,
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198,
220,
220,
220,
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220,
220,
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526,
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8,
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220,
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50,
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220,
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220,
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198,
220,
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62,
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13,
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7,
198,
220,
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220,
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220,
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220,
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220,
1391,
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25,
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329,
11688,
287,
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92,
198,
220,
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220,
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1303,
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13,
198,
220,
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48267,
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25,
198,
220,
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220,
220,
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220,
2116,
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48267,
62,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
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220,
220,
220,
220,
220,
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220,
1949,
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198,
220,
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220,
220,
220,
220,
220,
1181,
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13,
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13,
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62,
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62,
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7,
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13557,
25202,
13,
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8,
198,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
24442,
7203,
30074,
649,
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25,
4064,
82,
1600,
1181,
8,
628,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
2116,
13557,
15182,
796,
6407,
198,
220,
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220,
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220,
220,
220,
220,
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220,
2116,
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5219,
796,
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13,
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198,
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2116,
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13,
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198,
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220,
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1391,
198,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
2116,
13557,
2302,
974,
62,
8367,
62,
6738,
62,
42348,
7,
5219,
11,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
2116,
13557,
15182,
62,
1078,
7657,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
2845,
16232,
16922,
355,
409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
15182,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
18224,
7203,
30074,
6631,
981,
21207,
278,
262,
1181,
25,
4064,
82,
1600,
409,
8,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
2866,
62,
4868,
7,
944,
8,
4613,
1351,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
262,
1351,
286,
1695,
12055,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
12287,
62,
4868,
628,
220,
220,
220,
2488,
26745,
198,
220,
220,
220,
825,
2866,
7,
944,
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198,
220,
220,
220,
220,
220,
220,
220,
37227,
13615,
262,
1459,
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526,
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198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
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48797,
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62,
1078,
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58,
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62,
49058,
35944,
3672,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
30351,
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62,
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7,
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11,
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25,
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25,
198,
220,
220,
220,
220,
220,
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262,
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286,
262,
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526,
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198,
220,
220,
220,
220,
220,
220,
220,
611,
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13,
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62,
40890,
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62,
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62,
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41841,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
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220,
220,
220,
220,
220,
220,
220,
4808,
25294,
30373,
13,
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262,
4905,
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284,
25,
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82,
1600,
2866,
8,
628,
220,
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62,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198
] | 2.146058 | 16,870 |
import os
from pyfiglet import Figlet
from flask import Flask, send_file, jsonify
from subprocess import call
from sys import platform as _platform
if _platform == 'win32' or _platform == 'win64':
call('cls', shell=True)
elif _platform == 'linux' or _platform == 'linux2':
call('clear', shell=True)
f = Figlet(font='stop')
print(f.renderText('Avatar-Server'))
app = Flask(__name__)
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 1
avatar_dir = "a" # no slash
# create avatars directory if it does not exist
if not os.path.exists(avatar_dir):
os.makedirs(avatar_dir)
@app.route("/status")
@app.route("/<int:uid>")
@app.errorhandler(404)
# Run the server
app.run(host="0.0.0.0", port=5000)
| [
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31,
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25,
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31,
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7,
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8,
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2,
5660,
262,
4382,
198,
1324,
13,
5143,
7,
4774,
2625,
15,
13,
15,
13,
15,
13,
15,
1600,
2493,
28,
27641,
8,
198
] | 2.650376 | 266 |
#!/usr/bin/env python3
import csv
import enum
import os
import pprint
import re
import sqlite3
import sys
from functools import partialmethod
if __name__ == '__main__':
sys.exit(app())
| [
2,
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14,
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28955,
201,
198
] | 2.373626 | 91 |
# Generated by Django 3.0.5 on 2020-04-11 00:33
from django.db import migrations, models
| [
2,
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515,
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13,
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13,
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319,
12131,
12,
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198,
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6738,
42625,
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13,
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1330,
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602,
11,
4981,
628
] | 2.84375 | 32 |
from collections import OrderedDict
from django.contrib.auth.models import User
from django.http import JsonResponse
from rest_framework_simplejwt.serializers import TokenObtainPairSerializer
| [
6738,
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13,
46911,
11341,
1330,
29130,
5944,
3153,
47,
958,
32634,
7509,
628
] | 3.730769 | 52 |
from render_engine.img_manager import img_manager
| [
6738,
8543,
62,
18392,
13,
9600,
62,
37153,
1330,
33705,
62,
37153,
198
] | 3.846154 | 13 |
import requests
import threading
import sys
"""Usage: python aniwatch_proxy_checker.py proxy_file max_timeout"""
if __name__ == "__main__":
good_proxies = []
threads = []
with open(sys.argv[1], "r", encoding="utf-8") as f:
lol = [x.replace("\n", "") for x in f.readlines()]
lol = list(dict.fromkeys(lol))
print(f"Removed duplicates - {len(lol)} left")
for proxy in lol:
threads.append(threading.Thread(target=check_proxy, args=(proxy, float(sys.argv[2]),)))
for thread in threads:
thread.start()
for thread in threads:
thread.join()
with open("aniwatch.proxies.txt", "w+") as f:
f.write("".join(f"{x}\n" for x in good_proxies))
print("".join(f"{x}\n" for x in good_proxies))
| [
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25,
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220,
220,
220,
220,
220,
220,
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13,
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13,
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366,
86,
10,
4943,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
277,
13,
13564,
7203,
1911,
22179,
7,
69,
1,
90,
87,
32239,
77,
1,
329,
2124,
287,
922,
62,
1676,
87,
444,
4008,
628,
220,
220,
220,
3601,
7203,
1911,
22179,
7,
69,
1,
90,
87,
32239,
77,
1,
329,
2124,
287,
922,
62,
1676,
87,
444,
4008,
198
] | 2.378125 | 320 |
# 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.
from functools import partial
import pytest
from hypothesis import given, strategies as st
from linehaul.protocol.line_receiver import (
LineReceiver,
BufferTooLargeError,
TruncatedLineError,
)
from ...strategies import line_delimited_data as _line_delimited_data, chunked
_max_line_size = st.integers(min_value=1, max_value=32768)
max_line_size = st.shared(_max_line_size, key="max-line-size")
line_delimited_data = partial(
_line_delimited_data, max_line_size=st.shared(_max_line_size, key="max-line-size")
)
@given(max_line_size, chunked(line_delimited_data()))
@given(max_line_size | st.none(), st.integers(min_value=1, max_value=20))
@given(st.binary(min_size=1, max_size=512).filter(lambda i: i[-1:] != b"\n"))
_lines_of_line_delimited_data = line_delimited_data(
max_line_size=st.just(512), min_lines=2
).map(lambda d: d.split(b"\n")[:-1])
lines_of_line_delimited_data = st.shared(
_lines_of_line_delimited_data, key="lines-of-line-delimited-data"
).map(lambda lst: b"\n".join(lst) + b"\n")
@given(
chunked(lines_of_line_delimited_data),
st.shared(
_lines_of_line_delimited_data, key="lines-of-line-delimited-data"
).flatmap(lambda lst: st.sampled_from(lst)),
)
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] | 2.716258 | 652 |
import numpy as np
import healpy as hp
import pyfits
from time import time
import matplotlib.pyplot as plt
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
import numpy.random
import os, errno
import subprocess
import astropy.wcs
import astropy.io.fits as pyfits
from astropy.coordinates import SkyCoord
import astropy.units as u
# ---------------------------------------------------------------------------------------- #
# Make directory
# Some unit definitions
arcsec_to_radians = 0.0000048481368111
degree_to_arcsec = 3600.0
# ---------------------------------------------------------------------------------------- #
# Write partial Healpix map to file
# indices are the indices of the pixels to be written
# values are the values to be written
# ---------------------------------------------------------------------------------------- #
# Find healpix ring number from z
# ---------------------------------------------------------------------------------------- #
# returns the z coordinate of ring ir for Nside
# ---------------------------------------------------------------------------------------- #
# gives the list of Healpix pixels contained in [phi_low, phi_hi]
# ---------------------------------------------------------------------------------------- #
# Linear interpolation
# ---------------------------------------------------------------------------------------- #
# Test if val beints to interval [b1, b2]
# ---------------------------------------------------------------------------------------- #
# Test if a list of (theta,phi) values below to a region defined by its corners (theta,phi) for Left, Right, Bottom, Upper
# ---------------------------------------------------------------------------------------- #
# Computes healpix pixels of propertyArray.
# pixoffset is the number of pixels to truncate on the edges of each ccd image.
# ratiores is the super-resolution factor, i.e. the edges of each ccd image are processed
# at resultion 4*nside and then averaged at resolution nside.
# ---------------------------------------------------------------------------------------- #
# ---------------------------------------------------------------------------------------- #
# Crucial routine: read properties of a ccd image and returns its corners in ra dec.
# pixoffset is the number of pixels to truncate on the edges of each ccd image.
# ---------------------------------------------------------------------------------------- #
# Performs WCS inverse projection to obtain ra dec from ccd image information.
# ---------------------------------------------------------------------------------------- #
# Deproject into ra dec values
# ---------------------------------------------------------------------------------------- #
# ---------------------------------------------------------------------------------------- #
# Class for a pixel of the map, containing trees of images and values
# Project NDpix into a single number
# for a given property and operation applied to its array of images
# ---------------------------------------------------------------------------------------- #
# Project NDpix into a value
# Create a "healtree", i.e. a set of pixels with trees of images in them.
# Create a "healtree", i.e. a set of pixels with trees of images in them.
# Create a "healtree", i.e. a set of pixels with trees of images in them.
# ---------------------------------------------------------------------------------------- #
# Class for multi-dimensional healpix map that can be
# created and processed in parallel.
# Initialise and create array of pixels
# Process image and absorb its properties
# Process image and absorb its properties
# Process image and absorb its properties
# Process image and absorb its properties
# Process image and absorb its properties
# Project HealTree into partial Healpix map
# for a given property and operation applied to its array of images
# Project HealTree into regular Healpix map
# for a given property and operation applied to its array of images
# ---------------------------------------------------------------------------------------- #
# ---------------------------------------------------------------------------------------- #
# ---------------------------------------------------------------------------------------- #
# ---------------------------------------------------------------------------------------- #
# Process image and absorb its properties
# ---------------------------------------------------------------------------------------- #
# Read and project a Healtree into Healpix maps, and write them.
# ---------------------------------------------------------------------------------------- #
if False:
fname = '/Users/bl/Downloads/OPS/finalcut/Y2A1-2mass/Y2-2368/20140828/D00352819/p01/red/immask/D00352819_g_c45_r2368p01_immasked.fits.fz'
hdulist = pyfits.open(fname)
#imgdata1 = hdulist[1].data
#imgdata2 = hdulist[2].data
#imgdata3 = hdulist[3].data
#hdulist.close()
header = hdulist['SCI'].header
flatmask = hdulist['MSK'].data.T.ravel()
hdulist.close()
binmask = np.zeros(flatmask.size, dtype=bool)
binmask[(flatmask & 2047) == 0] = True
ind = np.where((flatmask & 2047) != 0)[0]
print(binmask, binmask.sum(), binmask.size, np.sum((flatmask & 2047) != 0))
np.save('D00469620_Y_c01_r2371p01_mask.npy', binmask)
np.save('D00469620_Y_c01_r2371p01_mask2.npy', ~binmask)
np.save('D00469620_Y_c01_r2371p01_mask3.npy', ind)
np.save('D00469620_Y_c01_r2371p01_mask4.npy', np.packbits(binmask))
np.save('D00469620_Y_c01_r2371p01_mask5.npy', np.packbits(~binmask))
np.savez_compressed('D00469620_Y_c01_r2371p01_maskb.npy', binmask)
np.savez_compressed('D00469620_Y_c01_r2371p01_maskb2.npy', ~binmask)
np.savez_compressed('D00469620_Y_c01_r2371p01_maskb3.npy', ind)
np.savez_compressed('D00469620_Y_c01_r2371p01_maskb4.npy', np.packbits(binmask))
np.savez_compressed('D00469620_Y_c01_r2371p01_maskb5.npy', np.packbits(~binmask))
hdu = pyfits.BinTableHDU.from_columns([pyfits.Column(name='mask', array=binmask, format='L')])
hdu.writeto('D00469620_Y_c01_r2371p01_mask.fits')
hdu = pyfits.BinTableHDU.from_columns([pyfits.Column(name='ind', array=ind, format='I')])
hdu.writeto('D00469620_Y_c01_r2371p01_mask2.fits')
hdu = pyfits.BinTableHDU.from_columns([pyfits.Column(name='mask', array=np.packbits(binmask), format='B')])
hdu.writeto('D00469620_Y_c01_r2371p01_mask3.fits')
stop
#hdulist = pyfits.open('/Users/bl/Dropbox/Projects/quicksip/data/Y1A1_SPT_IMAGEINFO_and_COADDINFO.fits')
hdulist = pyfits.open('/Users/bl/Dropbox/repos/QuickSip/y3a2_quicksip_info_000004.fits')
tbdata = hdulist[1].data
hdulist.close()
iccd = 10
propertyArray = tbdata[iccd]
#coadd_ras = [propertyArray[v] for v in ['URAUL', 'URALL', 'URALR', 'URAUR']]
#coadd_decs = [propertyArray[v] for v in ['UDECUL', 'UDECLL', 'UDECLR', 'UDECUR']]
coadd_ras = [float(propertyArray[v]) for v in ['RAC3', 'RAC2', 'RAC1', 'RAC4']]
coadd_decs = [float(propertyArray[v]) for v in ['DECC3', 'DECC2', 'DECC1', 'DECC4']]
text = [str(i+1) for i in range(4)]
print(coadd_ras)
print(coadd_decs)
plt.plot(coadd_ras, coadd_decs)
for i in range(4):
plt.text(coadd_ras[i], coadd_decs[i], text[i])
plt.show()
stop
#pix_thetas, pix_phis = hp.pix2ang(nside, ipixs_ring, nest=False)
local_dir = '.'
#local_dir = '/archive_data/desarchive/'
fname_remote = tbdata[iccd]['basepath'] + '/' + tbdata[iccd]['path'] + '/' + tbdata[iccd]['filename']
fname_local = local_dir + '/' + tbdata[iccd]['path'] + '/' + tbdata[iccd]['filename']
password_manager = urllib2.HTTPPasswordMgrWithPriorAuth()
password_manager.add_password(None, fname_remote, user, password, is_authenticated=True)
auth_manager = urllib2.HTTPBasicAuthHandler(password_manager)
opener = urllib2.build_opener(auth_manager)
#os.makedirs(local_dir + tbdata[iccd]['path'])
if not os.path.isdir(local_dir + '/' + tbdata[iccd]['path']):
os.makedirs(local_dir + '/' + tbdata[iccd]['path'])
if not os.path.exists(fname_local):
with open(fname_local, 'wb') as fd:
with opener.open(fname_remote) as f:
fd.write(f.read())
undersample = 2
hdulist = pyfits.open(fname_local)
flatmask = hdulist[2].data.T[::undersample, ::undersample].ravel()
hdulist.close()
nside = 1024
ratiores = 2
out = computeHPXpix_CCDpixels(nside, tbdata[iccd], pixoffset=0, ratiores=ratiores, coadd_cut=False, ipixel_low=None, nside_low=None, undersample=undersample)
ipixs_ring, sweights, img_thetas, img_phis, resubweights, ccdmask = out
fig, axs = plt.subplots(4, 4)
axs = axs.ravel()
cols = ['red', 'green', 'yellow', 'blue', 'orange', 'purple']
for i1, ipix_ring in enumerate(ipixs_ring[:16]):
axs[i1].scatter(img_thetas, img_phis, marker='.', s=1, color='k', alpha=0.4)
for i2 in range(ccdmask.shape[1]):
mask = ccdmask[i1, i2, :]
axs[i1].scatter(img_thetas[mask], img_phis[mask], marker='.', s=3, color=cols[i2])
axs[i1].set_xlim([0.9999*np.min(img_thetas), 1.0001*np.max(img_thetas)])
axs[i1].set_ylim([0.9999*np.min(img_phis), 1.0001*np.max(img_phis)])
fig.tight_layout()
plt.show()
#TODO: USE https://github.com/kadrlica/healpixify/blob/master/bin/healpixify
#if __name__ == "__main__":
# test()
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] | 2.983607 | 3,233 |
# -*- coding: utf-8 -*-
"""Factories to help in tests."""
from factory import PostGenerationMethodCall, Sequence
from factory.alchemy import SQLAlchemyModelFactory
from my_flask_app.database import db
from my_flask_app.user.models import User
from my_flask_app.quiz.models import Quiz
import json
class BaseFactory(SQLAlchemyModelFactory):
"""Base factory."""
class Meta:
"""Factory configuration."""
abstract = True
sqlalchemy_session = db.session
class UserFactory(BaseFactory):
"""User factory."""
username = Sequence(lambda n: f"user{n}")
email = Sequence(lambda n: f"user{n}@example.com")
password = PostGenerationMethodCall("set_password", "example")
active = True
class Meta:
"""Factory configuration."""
model = User
class QuizFactory(BaseFactory):
""" Qiuz factory. """
questions = {
"response_code":0,
"results":[
{
"category":"Category 1",
"type":"multiple",
"difficulty":"medium",
"question": f"question {n}",
"correct_answer":"answer 1",
"incorrect_answers":[
"answer 2",
"answer 3",
"answer 4"
],
"answers": [
"answer 1",
"answer 2",
"answer 3",
"answer 4"
],
"answer": "answer_4",
} for n in range(5)
]
}
class Meta:
"""Factory configuration."""
model = Quiz | [
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628,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
2264,
528
] | 2.060302 | 796 |
#!/usr/bin/env python
from .Core import Core
| [
2,
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14,
8800,
14,
24330,
21015,
198,
198,
6738,
764,
14055,
1330,
7231,
628
] | 2.9375 | 16 |
# Port of the tssrp6a library used for authentification
# Kudos to Schmitty#5079 for doing a rough translation from Javascript to Python
# of the important functions
import hashlib
import secrets
from typing import Tuple
N = 21766174458617435773191008891802753781907668374255538511144643224689886235383840957210909013086056401571399717235807266581649606472148410291413364152197364477180887395655483738115072677402235101762521901569820740293149529620419333266262073471054548368736039519702486226506248861060256971802984953561121442680157668000761429988222457090413873973970171927093992114751765168063614761119615476233422096442783117971236371647333871414335895773474667308967050807005509320424799678417036867928316761272274230314067548291133582479583061439577559347101961771406173684378522703483495337037655006751328447510550299250924469288819
SALT_LEN = 128
PRIVATE_VALUE_LEN = 256
NG = 2048
NG_BYTES = (NG + 7) // 8
def create_verifier_and_salt(password: bytes) -> Tuple[int, int]:
"""
Create a verifier and a salt for a given password. Used for user registration.
:param password: Password of the user
:return: Salt and Verifier derived from the password
"""
s = random_bytes(SALT_LEN)
v = create_verifier(s, password)
return (s, v)
def process_challenge(password: bytes, salt: bytes, challenge: int) -> Tuple[int, int, int, int, int, int, int]:
"""
Process the challenge and returns (x, a, A, k, u, S, M1). Used for user authentification.
:param password: Password of the user
:param salt: Salt associated with the user's verifier
:param challenge: Challenge created by the server
:return: Values generated by the challenge
"""
x = compute_x(salt, password)
a = generate_private_value()
A = pow(2, a, N)
k = btoi(hash_padded(itob(N), b'\x02'))
u = btoi(hash_padded(itob(A), itob(challenge)))
S = compute_client_session_key(k, x, u, a, challenge)
M1 = compute_client_evidence(A, challenge, S)
return (x, a, A, k, u, S, M1) | [
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] | 2.617268 | 776 |
"""
mymemory translator API
"""
from typing import List, Optional, Union
import requests
from deep_translator.base import BaseTranslator
from deep_translator.constants import BASE_URLS
from deep_translator.exceptions import (
RequestError,
TooManyRequests,
TranslationNotFound,
)
from deep_translator.validate import is_empty, is_input_valid
class MyMemoryTranslator(BaseTranslator):
"""
class that uses the mymemory translator to translate texts
"""
def __init__(
self,
source: str = "auto",
target: str = "en",
proxies: Optional[dict] = None,
**kwargs,
):
"""
@param source: source language to translate from
@param target: target language to translate to
"""
self.proxies = proxies
self.email = kwargs.get("email", None)
super().__init__(
base_url=BASE_URLS.get("MYMEMORY"),
source=source,
target=target,
payload_key="q",
)
def translate(
self, text: str, return_all: bool = False, **kwargs
) -> Union[str, List[str]]:
"""
function that uses the mymemory translator to translate a text
@param text: desired text to translate
@type text: str
@param return_all: set to True to return all synonym/similars of the translated text
@return: str or list
"""
if is_input_valid(text, max_chars=500):
text = text.strip()
if self._same_source_target() or is_empty(text):
return text
self._url_params["langpair"] = f"{self._source}|{self._target}"
if self.payload_key:
self._url_params[self.payload_key] = text
if self.email:
self._url_params["de"] = self.email
response = requests.get(
self._base_url, params=self._url_params, proxies=self.proxies
)
if response.status_code == 429:
raise TooManyRequests()
if response.status_code != 200:
raise RequestError()
data = response.json()
if not data:
TranslationNotFound(text)
translation = data.get("responseData").get("translatedText")
if translation:
return translation
elif not translation:
all_matches = data.get("matches")
matches = (match["translation"] for match in all_matches)
next_match = next(matches)
return next_match if not return_all else list(all_matches)
def translate_file(self, path: str, **kwargs) -> str:
"""
translate directly from file
@param path: path to the target file
@type path: str
@param kwargs: additional args
@return: str
"""
return self._translate_file(path, **kwargs)
def translate_batch(self, batch: List[str], **kwargs) -> List[str]:
"""
translate a list of texts
@param batch: list of texts you want to translate
@return: list of translations
"""
return self._translate_batch(batch, **kwargs)
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] | 2.245651 | 1,437 |
from typing import Tuple
import torch
from torch import nn as nn, Tensor
from torch.nn.functional import softmax
from .. import utils
class Encoder(nn.Module):
"""Encoder module.
The encoder represents its inputs using fixed, pre-trained GloVe embeddings, which are passed through a
bidirectional GRU.
Attributes
----------
units: int
Number of hidden units in the GRU.
embed_dim: int
Embedding dimensionality.
embedding: Module
Embedding module.
gru: Module
Bidirectional GRU module.
fc: Module
Fully-connected layer. Used to perform dimensionality reduction on final (bidirectional) hidden state, which
will be used to initialize the (unidirectional) hidden state of the decoder.
dropout: Module
Dropout module for the fully-connected layer.
"""
def __init__(self, embed_weights: Tensor, units: int, dropout: float) -> None:
"""Initialize the encoder.
Constructs encoder sub-modules and initializes their weights using `utils.init_weights`.
:param embed_weights: Tensor
Embedding weights of shape (src_vocab_size, embed_dim).
:param units: int
Number of GRU hidden units.
:param dropout: float
Dropout probability.
"""
super().__init__()
self.units = units
self.embed_dim = embed_weights.shape[1]
self.embedding = nn.Embedding.from_pretrained(embed_weights)
self.gru = nn.GRU(self.embed_dim, units, bidirectional=True)
self.fc = nn.Linear(2 * units, units)
self.dropout = nn.Dropout(dropout)
utils.init_weights_(self.gru)
utils.init_weights_(self.fc)
def forward(self, source: Tensor) -> Tuple[Tensor, Tensor]:
"""Encoder a source sequence by forward propagation.
:param source: Tensor
Source sequence of shape (seq_len, batch_size)
:return: Tuple[Tensor, Tensor]
Outputs of shape (seq_len, batch_size, 2 * units) and state of shape (batch_size, units)
"""
embedded = self.embedding(source)
embedded = self.dropout(embedded)
outputs, state = self.gru(embedded)
outputs = self.dropout(outputs)
# combine and reshape bidirectional states for compatibility with (unidirectional) decoder
combined = torch.cat((state[-2, :, :], state[-1, :, :]), dim=1)
state = torch.tanh(self.fc(combined))
return outputs, state
class Attention(nn.Module):
"""Attention module.
Used to compute Bahdanau attention weights over encoder outputs.
Attributes
----------
attn_in: int
Dimensionality of attention input.
fc: Module
First fully-connected layer
fc2: Module
Second fully-connected layer.
dropout: Module
Dropout module
"""
def __init__(self, enc_units: int, dec_units: int, units: int) -> None:
"""Initializes the attention module.
:param enc_units: int
Encoder output dimensionality.
:param dec_units: int
Decoder state dimensionality.
:param units: int
Attention dimensionality.
"""
super().__init__()
self.attn_in = 2 * enc_units + dec_units
self.fc = nn.Linear(self.attn_in, units)
self.fc2 = nn.Linear(units, 1)
utils.init_weights_(self.fc)
utils.init_weights_(self.fc2)
def forward(self, dec_state: Tensor, enc_outputs: Tensor) -> Tensor:
"""Computes (Bahdanau) attention weights.
:param dec_state: Tensor
Previous or initial decoder state of shape (batch_size, dec_units).
:param enc_outputs: Tensor
Encoder outputs of shape (source_len, batch_size, 2 * enc_units).
:return:
Attention weights of shape (batch_size, source_len).
"""
source_len = enc_outputs.shape[0]
# vectorize computation of Bahdanau attention scores for all encoder outputs
dec_state = dec_state.unsqueeze(1).repeat(1, source_len, 1)
enc_outputs = enc_outputs.permute(1, 0, 2)
combined = torch.cat((dec_state, enc_outputs), dim=2)
scores = torch.tanh(self.fc(combined)) # (len, batch, units)
scores = self.fc2(scores).squeeze(-1)
weights = softmax(scores, dim=1)
return weights
class Decoder(nn.Module):
"""Decoder module.
Predicts next token probabilities using previous token and state and context vector.
Attributes
----------
vocab_size: int
Target vocabulary size.
embed_dim: int
Embedding dimension.
embedding: Module
Embedding layer.
rnn: Module
GRU.
fc: Module
Fully-connected layer that outputs scores over target vocabulary.
dropout: Module
Dropout module.
"""
def __init__(self, embed_weights: Tensor, units: int, dropout: float, enc_units: int) -> None:
"""Initializes the decoder module.
:param embed_weights: Tensor
Embedding weights of shape (trg_vocab_size, embed_dim).
:param units: int
Number of hidden units in the GRU.
:param dropout: float
Dropout probability.
"""
super().__init__()
self.vocab_size, embed_dim = embed_weights.shape
self.embedding = nn.Embedding.from_pretrained(embed_weights)
self.rnn = nn.GRU((units * 2) + embed_dim, units)
self.fc = nn.Linear(units + 2 * enc_units + embed_dim, self.vocab_size)
self.dropout = nn.Dropout(dropout)
self.ln = nn.LayerNorm(units)
utils.init_weights_(self.rnn)
utils.init_weights_(self.fc)
def forward(self, token: Tensor, dec_state: Tensor, context: Tensor) -> Tuple[Tensor, Tensor]:
"""Decodes the next token.
:param token: Tensor
Token of shape (batch_size,).
:param dec_state:
Decoder state of shape (batch_size, dec_units).
:param context:
Context vector of shape (1, batch_size, 2 * enc_units).
:return: Tuple[Tensor, Tensor]
Vocabulary scores of shape (batch_size, vocab_size) and decoder state of shape (batch_size, dec_units).
"""
token = token.unsqueeze(0)
embedded = self.embedding(token)
embedded = self.dropout(embedded)
rnn_input = torch.cat((embedded, context), dim=2)
output, dec_state = self.rnn(rnn_input, dec_state.unsqueeze(0))
embedded = embedded.squeeze(0)
output = output.squeeze(0)
context = context.squeeze(0)
# dropout already applied to embedded
output = self.dropout(output)
context = self.dropout(context)
# compute output using gru output, context vector, and embedding of previous output
combined = torch.cat((output, context, embedded), dim=1)
output = self.fc(combined)
output = self.dropout(output)
# return logits (rather than softmax activations) for compatibility with cross-entropy loss
dec_state = dec_state.squeeze(0)
dec_state = self.ln(dec_state)
return output, dec_state
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] | 2.387118 | 3,043 |
"""
constant
"""
class Addrs:
"""
Server address
"""
WS_ADDR = 'wss://www.streamr.com/api/v1/ws'
REST_ADDR = 'https://www.streamr.com/api/v1'
class RestfullConstant:
"""
Restful constants
"""
GET_SESSION_ADDR = Addrs.REST_ADDR + '/login/apikey'
GET_SESSION_PARAS = {"Content-Type": "application/json"}
GET_SESSION_BODY = '{"apiKey": "%s"}'
CREATE_ADDR = Addrs.REST_ADDR + '/streams'
CREATE_BODY = '{"name": %s, "description": %s, "config": {"fields":[]}}'
GET_BY_ID_ADDR = CREATE_ADDR + '/'
GET_BY_NAME_ADDR = CREATE_ADDR + '?name='
PARAS_KEY = "Authorization"
PARAS_VALUE = "Bearer %s"
SESSION_TOKEN = 'token'
class StreamMessageConstant:
"""
store the key of dict of StreamMessage
"""
STREAM_ID = 'streamId'
STREAM_PARTITION = 'streamPartition'
TIMESTAMP = 'timestamp'
TTL = 'ttl'
OFFSET = 'offset'
PREVIOUS_OFFSET = 'previousOffset'
CONTENT_TYPE = 'contentType'
CONTENT = 'content'
SIGNATURE_TYPE = 'signatureType'
PUBLISHER_ADDRESS = 'publisherAddress'
SIGNATURE = 'signature'
BYE = '_bye'
class StreamAndPartitionConstant:
"""
store the key of dict of StreamAndPartitionConstant
"""
STREAM_ID = 'stream'
STREAM_PARTITION = 'partition'
class ResendResponsePayloadConstant:
"""
store the key of dict of ResendResponsePayloadConstant
"""
STREAM_ID = 'stream'
STREAM_PARTITION = 'partition'
SUB_ID = 'sub'
class ErrorPayloadConstant:
"""
store the key of dict of ErrorPayloadConstant
"""
ERROR = 'error'
class RequestConstant:
"""
store the key of dict of RequestConstant
"""
TYPE = 'type'
STREAM_ID = 'stream'
API_KEY = 'authKey'
SESSION_TOKEN = 'sessionToken'
SERIALIZED_CONTENT = 'msg'
TIMESTAMP = 'ts'
PARTITION_KEY = 'pkey'
PUBLISHER_ADDRESS = 'addr'
SIGNATURE_TYPE = 'sigtype'
SIGNATURE = 'sig'
STREAM_PARTITION = 'partition'
SUB_ID = 'sub'
class AuthConstant:
"""
store the key of dict of Auth
"""
API_KEY = 'apiKey'
PRIVATE_KEY = 'privateKey'
PROVIDER = 'provider'
USER_NAME = 'username'
PASSWORD = 'password'
SESSION_TOKEN = 'sessionToken'
class OptionConstant:
"""
store the key of dict of Options
"""
API_KEY = 'apiKey'
URL = 'url'
REST_URL = 'restUrl'
AUTO_CONNECT = 'autoConnect'
AUTO_DISCONNECT = 'autoDisconnect'
SESSION_REFRESH_INTERVAL = 'interval'
AUTH = 'auth'
AUTH_KEY = 'authKey'
STREAM_ID = 'stream'
STREAM_PARTITION = 'partition'
SUB_ID = 'sub'
RESEND_LABEL = 'resend_'
RESEND_LAST = 'resend_last'
RESEND_FROM = 'resend_from'
RESEND_TO = 'resend_to'
RESEND_FROM_TIME = 'resend_from_time'
RESEND_ALL = 'resend_all'
class EventConstant:
"""
store the event name for client, connection and subscription
"""
CONNECTING = 'CONNECTING'
CONNECTED = 'CONNECTED'
DISCONNECTING = 'DISCONNECTING'
DISCONNECTED = 'DISCONNECTED'
SUBSCRIBING = 'SUBSCRIBING'
SUBSCRIBED = 'SUBSCRIBED'
UNSUBSCRIBING = 'UNSUBSCRIBING'
UNSUBSCRIBED = 'UNSUBSCRIBED'
ERROR = 'ERROR'
RESENDING = 'RESENDING'
RESENT = 'RESENT'
NO_RESEND = 'NO_RESEND'
GAP = 'GAP'
DONE = 'DONE'
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38,
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6,
198,
220,
220,
220,
360,
11651,
796,
705,
35,
11651,
6,
628
] | 2.272355 | 1,465 |
import numpy as np
import matplotlib.pyplot as plt
Ri = np.linspace(0, 0.25, 26)
f_long = 1 / (1 + 10 * Ri)
f_sharp = (1 - 5 * Ri)**2
f_sharp[11:] = (1 / (20 * Ri[11:]))**2
print(Ri[11])
plt.plot(Ri, f_long, '-g', label='Land')
plt.plot(Ri, f_sharp, '-b', label='Sea')
plt.xlabel('Ri')
plt.ylabel(r'$f(\mathrm{Ri})$')
plt.legend(loc='best')
plt.show()
| [
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13466,
11537,
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489,
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13,
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3419,
198
] | 1.924731 | 186 |
from django.core.urlresolvers import reverse
from django.contrib.auth.decorators import login_required
from django.shortcuts import get_object_or_404
from django.views.generic import TemplateView, CreateView, UpdateView, ListView, DeleteView
from .forms import ProjectForm, TicketForm
from .models import Project, Ticket
my_tickets_view = MyTicketsView.as_view()
project_list_view = ProjectListView.as_view()
create_project_view = login_required(CreateProjectView.as_view())
update_project_view = login_required(UpdateProjectView.as_view())
project_view = ProjectView.as_view()
create_ticket_view = login_required(CreateTicketView.as_view())
update_ticket_view = login_required(UpdateTicketView.as_view())
# Added Delete View, together with new delete-ticket template and URL
delete_ticket_view = login_required(DeleteTicketView.as_view()) | [
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7,
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62,
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2,
10687,
23520,
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11,
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796,
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62,
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7,
38727,
51,
9715,
7680,
13,
292,
62,
1177,
28955
] | 3.357977 | 257 |
from typing import Dict
string = input()
print('YES' if is_valid(string) else 'NO')
| [
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] | 3.034483 | 29 |
# Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import os
import unittest
from telemetry import test
from telemetry.core.platform import factory
from telemetry.core.platform import mac_platform_backend
| [
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437,
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] | 3.890244 | 82 |
from purity_fb import PurityFb, VersionResponse, rest
# Get Pure API version
fb = PurityFb("10.70.100.10") # assume the array IP is 10.255.9.28
fb.disable_verify_ssl()
try:
# no need to login to get API versions
res = fb.api_version.list_versions()
assert isinstance(res, VersionResponse)
print (res.versions) # ex: ["1.0", "1.1", "1.2"]
except rest.ApiException as e:
print("Exception when logging in to the array: %s\n" % e)
| [
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59,
77,
1,
4064,
304,
8,
198,
220,
220,
220,
220
] | 2.624277 | 173 |
# -*- coding: utf-8 -*-
import getopt
import sys
from torcms.model.post_model import MPost
from torcms.script.script_init_tabels import create_table
from torcms_maplet.model.map_tab import MabGson, MabPost2Gson, MabLayout
def run_update_lon():
'''
:return:
'''
map_recs = MPost.query_all(limit_num=5000, kind='2')
for map_rec in map_recs:
lon = float(map_rec.extinfo['ext_lon'])
if lon > -180 and lon < 180:
pass
else:
print('=' * 20)
print(map_rec.uid)
print(lon)
out_dic = {'ext_lon': fix_lon(lon)}
print(out_dic)
MPost.update_jsonb(map_rec.uid, out_dic)
if __name__ == '__main__':
if len(sys.argv) == 1:
print('run:')
print(' python helper_extor.py -h ')
print('for help')
else:
entry(sys.argv[1:])
| [
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62,
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8,
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361,
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834,
12417,
834,
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198,
220,
220,
220,
611,
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7,
17597,
13,
853,
85,
8,
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352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
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25,
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198,
220,
220,
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220,
220,
220,
3601,
10786,
220,
220,
220,
21015,
31904,
62,
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273,
13,
9078,
532,
71,
705,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
1640,
1037,
11537,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5726,
7,
17597,
13,
853,
85,
58,
16,
25,
12962,
198
] | 1.944812 | 453 |
from django.contrib.auth import get_user_model
from django.contrib.auth.password_validation import validate_password
from rest_framework import serializers
from api.models import UserModel, TuitModel
class TuitSerializer(serializers.ModelSerializer):
'''
Tuit serializer.
'''
class UserSerializer(serializers.ModelSerializer):
'''
User serializer.
'''
tuits = TuitSerializer(many=True, read_only=True)
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] | 2.993103 | 145 |
# -*- coding: utf-8 -*-
# Copyright 2017, IBM.
#
# This source code is licensed under the Apache License, Version 2.0 found in
# the LICENSE.txt file in the root directory of this source tree.
"""Utilities for File Input/Output."""
import copy
import datetime
import json
import os
import numpy
from sympy import Basic
import qiskit
from qiskit._qiskiterror import QISKitError
from qiskit.backends import BaseBackend
def convert_qobj_to_json(in_item):
"""
Combs recursively through a list/dictionary and finds any non-json
compatible elements and converts them. E.g. complex ndarray's are
converted to lists of strings. Assume that all such elements are
stored in dictionaries!
Arg:
in_item (dict or list): the input dict/list
"""
key_list = []
for (item_index, item_iter) in enumerate(in_item):
if isinstance(in_item, list):
curkey = item_index
else:
curkey = item_iter
if isinstance(in_item[curkey], (list, dict)):
# go recursively through nested list/dictionaries
convert_qobj_to_json(in_item[curkey])
elif isinstance(in_item[curkey], numpy.ndarray):
# ndarray's are not json compatible. Save the key.
key_list.append(curkey)
# convert ndarray's to lists
# split complex arrays into two lists because complex values are not
# json compatible
for curkey in key_list:
if in_item[curkey].dtype == 'complex':
in_item[curkey + '_ndarray_imag'] = numpy.imag(
in_item[curkey]).tolist()
in_item[curkey + '_ndarray_real'] = numpy.real(
in_item[curkey]).tolist()
in_item.pop(curkey)
else:
in_item[curkey] = in_item[curkey].tolist()
def convert_json_to_qobj(in_item):
"""Combs recursively through a list/dictionary that was loaded from json
and finds any lists that were converted from ndarray and converts them back
Arg:
in_item (dict or list): the input dict/list
"""
key_list = []
for (item_index, item_iter) in enumerate(in_item):
if isinstance(in_item, list):
curkey = item_index
else:
curkey = item_iter
# flat these lists so that we can recombine back into a complex
# number
if '_ndarray_real' in curkey:
key_list.append(curkey)
continue
if isinstance(in_item[curkey], (list, dict)):
convert_json_to_qobj(in_item[curkey])
for curkey in key_list:
curkey_root = curkey[0:-13]
in_item[curkey_root] = numpy.array(in_item[curkey])
in_item.pop(curkey)
if curkey_root + '_ndarray_imag' in in_item:
in_item[curkey_root] = in_item[curkey_root] + 1j * numpy.array(
in_item[curkey_root + '_ndarray_imag'])
in_item.pop(curkey_root + '_ndarray_imag')
def file_datestr(folder, fileroot):
"""Constructs a filename using the current date-time
Args:
folder (str): path to the save folder
fileroot (str): root string for the file
Returns:
String: full file path of the form
'folder/YYYY_MM_DD_HH_MM_fileroot.json'
"""
# if the fileroot has .json appended strip it off
if len(fileroot) > 4 and fileroot[-5:].lower() == '.json':
fileroot = fileroot[0:-5]
return os.path.join(
folder, ('{:%Y_%m_%d_%H_%M_}'.format(datetime.datetime.now()) +
fileroot + '.json'))
def load_result_from_file(filename):
"""Load a results dictionary file (.json) to a Result object.
Note: The json file may not load properly if it was saved with a previous
version of the SDK.
Args:
filename (str): filename of the dictionary
Returns:
tuple(Result, dict):
The new Results object
if the metadata exists it will get returned
Raises:
QISKitError: if the file does not exist or does not have the proper
dictionary structure.
"""
if not os.path.exists(filename):
raise QISKitError('File %s does not exist' % filename)
with open(filename, 'r') as load_file:
master_dict = json.load(load_file)
try:
qresult_dict = master_dict['result']
convert_json_to_qobj(qresult_dict)
metadata = master_dict['metadata']
except KeyError:
raise QISKitError('File %s does not have the proper dictionary '
'structure')
qresult = qiskit.Result(qresult_dict)
return qresult, metadata
class ResultEncoder(json.JSONEncoder):
"""
Custom JSON encoder for sympy types.
"""
def save_result_to_file(resultobj, filename, metadata=None):
"""Save a result and optional metatdata to a single dictionary file.
Args:
resultobj (Result): Result to save
filename (str): save path (with or without the json extension). If the
file already exists then numbers will be appended to the root to
generate a unique filename.
E.g. if filename=test.json and that file exists then the file will
be changed to test_1.json
metadata (dict): Add another dictionary with custom data for the
result (eg fit results)
Return:
String: full file path
"""
master_dict = {
'result': copy.deepcopy(resultobj._result)
}
if metadata is None:
master_dict['metadata'] = {}
else:
master_dict['metadata'] = copy.deepcopy(metadata)
# need to convert any ndarray variables to lists so that they can be
# exported to the json file
convert_qobj_to_json(master_dict['result'])
# if the filename has .json appended strip it off
if filename[-5:].lower() == '.json':
filename = filename[0:-5]
append_str = ''
append_num = 0
while os.path.exists(filename + append_str + '.json'):
append_num += 1
append_str = '_%d' % append_num
with open(filename + append_str + '.json', 'w') as save_file:
json.dump(master_dict, save_file, indent=1, cls=ResultEncoder)
return filename + append_str + '.json'
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198
] | 2.400154 | 2,594 |
import datetime
import time
import discord
import pytz
from discord.ext import commands
from Modules import CONSTANT
from Modules.CONSTANT import SHOWROOM_ALERT_ROLES
from Modules.Checks import check_if_bot_spam
JP_TZ: pytz.UTC = pytz.timezone('Asia/Tokyo')
UNIVERSAL_TZ: pytz.UTC = pytz.timezone('UTC')
PACIFIC_TZ: pytz.UTC = pytz.timezone('America/Los_Angeles')
CENTRAL_TZ: pytz.UTC = pytz.timezone('America/Chicago')
EASTERN_TZ: pytz.UTC = pytz.timezone('America/New_York')
TIMEZONES = [
("Japan Time", JP_TZ),
("Universal Time", UNIVERSAL_TZ),
("Pacific Time", PACIFIC_TZ),
("Central Time", CENTRAL_TZ),
("Eastern Time", EASTERN_TZ)]
TIME_FORMAT_STRING: str = "%Y-%m-%d %I:%M%p"
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] | 2.470383 | 287 |
import requests
from flask import Blueprint, Flask, redirect, render_template, request, url_for
from itertools import tee
from markupsafe import Markup, escape
hist = Blueprint('history', __name__, template_folder='templates', static_folder='static')
app = Flask(__name__)
API_URL = 'https://api.openstreetmap.org/api/0.6'
@app.template_filter('truncate')
@hist.errorhandler(ElementDoesntExistException)
@hist.route('/')
@hist.route('/node.php')
@hist.route('/way.php')
@hist.route('/relation.php')
@hist.route('/node/<int:id>')
@hist.route('/way/<int:id>')
@hist.route('/relation/<int:id>')
app.register_blueprint(hist, url_prefix='/history')
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] | 2.8159 | 239 |
"""
Run load tests:
locust -f load_test/locustfile.py --host http://127.0.0.1:3000
"""
from locust import HttpUser, task
import pandas as pd
import random
feature_columns = {
"fixed acidity": "fixed_acidity",
"volatile acidity": "volatile_acidity",
"citric acid": "citric_acid",
"residual sugar": "residual_sugar",
"chlorides": "chlorides",
"free sulfur dioxide": "free_sulfur_dioxide",
"total sulfur dioxide": "total_sulfur_dioxide",
"density": "density",
"pH": "ph",
"sulphates": "sulphates",
"alcohol": "alcohol_pct_vol",
}
dataset = (
pd.read_csv(
"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv",
delimiter=";",
)
.rename(columns=feature_columns)
.drop("quality", axis=1)
.to_dict(orient="records")
)
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] | 2.355932 | 354 |
# Import spezieller Python-Funktionalitรคten, damit Abstrakte Methoden
# definiert werden kรถnnen
from abc import ABCMeta, abstractmethod
# Konstanten, mit denen wir die Wetterlage beschreiben
SONNE = 0,
BEWOELKT = 1
REGEN = 2
# Abstrakte Klasse zur Reprรคsentation einer Stadt.
# Von dieser Klasse kann keine Instanz (= Objekt) erzeugt werden.
# รffentliche Klasse fรผr die Groรstadt leitet
# von der Klasse Stadt ab.
# Konstruktor erwartet ebenfalls einen Stรคdtenamen und das
# Wetter, aber zusรคtzlich noch Stadtteile
# Getter-Methode zur Rรผckgabe des Webseiten-Inhalts
# fรผr den Webseiten-Generator
# Getter-Methode zur Rรผckgabe der URL zur Wetterseite
# รffentliche Klasse fรผr die Groรstadt leitet
# von der Klasse Stadt ab.
# Konstruktor erwartet ebenfalls einen Stรคdtenamen und das
# Wetter
# Getter-Methode zur Rรผckgabe des Webseiten-Inhalts
# fรผr den Webseiten-Generator
# Getter-Methode zur Rรผckgabe der URL zur Wetterseite
# รffentliche Klasse fรผr den Webseitengenerator
# Startpunkt des Hauptprogramms
# Hier werden die implementierten Klassen zu Demonstrations- und
# Testzwecken instanziiert und verwendet.
| [
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] | 2.449791 | 478 |
__author__ = 'jdeu'
| [
834,
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] | 2 | 10 |
"""Signals for content syncing"""
from django.db.models.signals import post_save
from django.dispatch import receiver
from content_sync import api
from websites.models import WebsiteContent
@receiver(
post_save,
sender=WebsiteContent,
dispatch_uid="sync_state_website_content_upsert",
)
def upsert_content_sync_state(
sender, instance, created, **kwargs
): # pylint: disable=unused-argument
""" Create/update the sync state """
api.upsert_content_sync_state(instance)
| [
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# -*- coding: utf-8 -*-
"""Module providing an image scaling factory."""
import os
import json
import six
from plone import api
from plone.scale.interfaces import IScaledImageQuality
from zope.component import getMultiAdapter, queryUtility
from zope.globalrequest import getRequest
from ade25.base.utils import get_filesystem_template
IMG = 'data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACwAAAAAAQABAAACAkQBADs='
class ResponsiveImagesTool(object):
""" Factory providing rescaling of project images """
@staticmethod
@staticmethod
def get_quality():
"""Get plone.app.imaging's quality setting"""
default_scaled_image_quality = queryUtility(IScaledImageQuality)
if default_scaled_image_quality is None:
return None
return default_scaled_image_quality()
@staticmethod
@staticmethod
def generate_image(self,
item,
image_field,
scale_settings,
generate_lqip=False):
""" function used for generating (and potentially storing)
image scales on demand
"""
image_scales = getMultiAdapter((item, getRequest()),
name='images')
settings = scale_settings
if settings['quality'] == 'auto':
settings['quality'] = self.get_quality()
if isinstance(scale_settings['width'], tuple):
settings['width'] = scale_settings['width'][0]
if isinstance(scale_settings['height'], tuple):
settings['height'] = scale_settings['height'][0]
if hasattr(item, image_field):
stored_image = getattr(item, image_field)
if generate_lqip:
settings['width'] = stored_image.getImageSize()[0]
settings['height'] = stored_image.getImageSize()[1]
settings['direction'] = 'keep'
settings['quality'] = 10
# Generate scale
image_scale = image_scales.scale(
image_field,
width=int(settings['width']),
height=int(settings['height']),
direction=settings['direction'],
quality=int(settings['quality'])
)
image_data = {
'url': image_scale.url,
'width': image_scale.width,
'height': image_scale.height
}
else:
image_data = self.fallback_image_data()
return image_data
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] | 2.172502 | 1,171 |
__author__ = 'yinjun'
# initialize your data structure here.
# @param x, an integer
# @return nothing
# @return nothing
# @return an integer
# @return an boolean | [
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] | 2.278351 | 97 |
import discord
from discord.ext import commands
from discord.ext import vbu
from subprocess import check_output
import goslate
class Translator(vbu.Cog):
"""Translate text using google translate."""
@vbu.group(pass_context=True, invoke_without_command=True, aliases=["tr"])
async def translate(self, ctx, to_lang, *, text):
"""Translate text using google translate."""
if to_lang in self.gs.get_languages():
try:
lang_id = self.gs.detect(text)
lang = self.gs.get_languages()[lang_id]
await ctx.send(
"{} (detected as {} ({}))".format(
self.gs.translate(text, to_lang), lang, lang_id
)
)
except Exception as e:
await ctx.send("An error occured while translating. ({})".format(e))
else:
await ctx.send(
"That language could not be found in the list, for a list of supported languages do {}translate langlist".format(
ctx.prefix
)
)
@translate.command()
async def langlist(self, ctx):
"""Shows you a list of supported languages."""
msg = "```fix\nCurrent available languages are:\n"
for lang in self.gs.get_languages():
msg += "\t{}: {}\n".format(self.gs.get_languages()[lang], lang)
if len(msg) > 1750:
await ctx.send(msg + "```")
msg = "```fix\n"
await ctx.send(msg + "```")
| [
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1343,
366,
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63,
4943,
628
] | 2.067729 | 753 |
import os
DATA_DIR = os.path.dirname(os.path.abspath(__file__))
CARDS_DATABASE_FILE = os.path.join(DATA_DIR, 'database_cards.csv')
NOBLES_DATABASE_FILE = os.path.join(DATA_DIR, 'database_nobles.csv') | [
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] | 2.352941 | 85 |
from django.http import HttpResponse
from rest_framework import status
from rest_framework.renderers import JSONRenderer
from collectionjson.renderers import CollectionJsonRenderer
class RenderedResponse(HttpResponse):
"""
An HttpResponse that renders its content into Collection+JSON or JSON.
"""
| [
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] | 3.674419 | 86 |
import os
import argparse
import requests
apiHost = "https://api.github.com"
token = os.getenv("GITHUB_TOKEN")
if token == None:
print("ERROR: Specify GITHUB_TOKEN environment variable")
exit(1)
authHeader = { "Authorization": f"token {token}"}
parser = argparse.ArgumentParser()
parser.add_argument('--owner')
parser.add_argument('--repo')
parser.add_argument('--tag')
parser.add_argument('--release-name')
parser.add_argument('--body')
parser.add_argument('--prerelease')
parser.add_argument('action')
parser.add_argument('artifact', nargs='+')
if __name__ == "__main__":
main() | [
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] | 2.902913 | 206 |
from unittest import TestCase, mock
import layers
import numpy as np
import pytest
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"""Views for the D3M configuration module"""
import os
import json
import mimetypes
from django.views.decorators.csrf import csrf_exempt
from django.shortcuts import render
from django.http import FileResponse
from django.http import JsonResponse, HttpResponse, Http404
from tworaven_apps.configurations.models_d3m import D3MConfiguration,\
KEY_DATASET_SCHEMA, KEY_PROBLEM_SCHEMA, D3M_FILE_ATTRIBUTES
from tworaven_apps.configurations.utils import \
(get_latest_d3m_config,
get_d3m_filepath,
get_train_data_info)
from tworaven_apps.user_workspaces.utils import get_latest_user_workspace
from tworaven_apps.utils.json_helper import json_loads, json_dumps
from tworaven_apps.utils.view_helper import \
(get_json_error,
get_json_success)
# Create your views here.
@csrf_exempt
def view_d3m_list(request):
"""List the D3m configurations in the db"""
configs = D3MConfiguration.objects.all().order_by('-is_default', 'name')
tinfo = dict(title='D3M configurations',
configs=configs)
return render(request,
'd3m_config_list.html',
tinfo)
@csrf_exempt
def view_d3m_details_page(request, d3m_config_id):
"""Show the D3m configuration on a web page"""
return HttpResponse('view_d3m_details_page: %d (to do)' % d3m_config_id)
@csrf_exempt
def view_d3m_details_json(request, d3m_config_id):
"""Return the D3m configuration as JSON"""
is_pretty = request.GET.get('pretty', False)
# Is there a default config?
d3m_config = D3MConfiguration.objects.filter(id=d3m_config_id).first()
if not d3m_config:
raise Http404('no config with id: %s' % d3m_config_id)
if is_pretty is not False: # return this as a formatted string?
config_str = '<pre>%s<pre>' % \
(json.dumps(d3m_config.to_dict(),
indent=4))
return HttpResponse(config_str)
# return as JSON!
return JsonResponse(d3m_config.to_dict())
@csrf_exempt
def view_d3m_details_json_eval_latest(request):
"""For EVAL: Return the "latest" D3m configuration as JSON.
"latest" may be most recently added or a "default"
of some kind"""
return view_d3m_details_json_latest(request,
as_eval_dict=True)
@csrf_exempt
def view_d3m_details_json_latest(request, as_eval_dict=False):
"""Return the "latest" D3m configuration as JSON.
"latest" may be most recently added or a "default"
of some kind"""
is_pretty = request.GET.get('pretty', False)
# Is there a default config?
d3m_config = get_latest_d3m_config()
if not d3m_config:
raise Http404('no configs available')
if is_pretty is not False: # return this as a formatted string?
config_str = '<pre>%s<pre>' % \
(json.dumps(d3m_config.to_dict(as_eval_dict),
indent=4))
return HttpResponse(config_str)
# return as JSON!
return JsonResponse(d3m_config.to_dict(as_eval_dict))
@csrf_exempt
def view_get_problem_schema(request, d3m_config_id=None):
"""Return the problem_schema file"""
return view_get_config_file(request, KEY_PROBLEM_SCHEMA, d3m_config_id)
@csrf_exempt
def view_get_dataset_schema(request, d3m_config_id=None):
"""Return the dataset_schema file"""
return view_get_config_file(request, KEY_DATASET_SCHEMA, d3m_config_id)
# return view_get_config_file(request, 'KEY_D', d3m_config_id)
@csrf_exempt
def view_get_config_file(request, config_key, d3m_config_id=None):
"""Get contents of a file specified in the config"""
if not config_key in D3M_FILE_ATTRIBUTES:
user_msg = (f'Config key "{config_key}" not found!'
f' (view_get_config_file)')
return JsonResponse(get_json_error(user_msg))
if d3m_config_id is None:
d3m_config = get_latest_d3m_config()
else:
d3m_config = D3MConfiguration.objects.filter(id=d3m_config_id).first()
if d3m_config is None:
user_msg = 'Config not found!'
return JsonResponse(get_json_error(user_msg))
# Make sure the config has a value.
# For example the D3MPROBLEMPATH may be blank
#
if not getattr(d3m_config, config_key):
user_msg = f'Sorry! The config does not have a "{config_key}" value!'
return JsonResponse(get_json_error(user_msg))
filepath_info = get_d3m_filepath(d3m_config, config_key)
if not filepath_info.success:
user_msg = f'{filepath_info.err_msg} (view_get_config_file)'
return JsonResponse(get_json_error(user_msg))
# Relatively small files...
# response = FileResponse(open(filepath_info.result_obj, 'rb'))
# return response
fcontent = open(filepath_info.result_obj, 'r').read()
json_info = json_loads(fcontent)
if not json_info.success:
user_msg = f'{json_info.err_msg} (view_get_config_file)'
return JsonResponse(get_json_error(user_msg))
return JsonResponse(get_json_success(\
'Success!',
data=json_info.result_obj))
@csrf_exempt
def view_get_problem_data_info(request, d3m_config_id=None):
"""Get info on train data and target files, if they exist"""
if d3m_config_id is None:
d3m_config = get_latest_d3m_config()
else:
d3m_config = D3MConfiguration.objects.filter(id=d3m_config_id).first()
if d3m_config is None:
user_msg = 'Config not found! (view_get_problem_data_info)'
return JsonResponse(get_json_error(user_msg))
is_pretty = request.GET.get('pretty', False)
train_data_info = get_train_data_info(d3m_config)
if not train_data_info.success:
resp_dict = get_json_error(train_data_info.err_msg)
else:
resp_dict = get_json_success('It worked',
data=train_data_info.result_obj)
if is_pretty is not False: # return this as a formatted string?
config_str = '<pre>%s<pre>' % \
(json.dumps(resp_dict,
indent=4))
return HttpResponse(config_str)
return JsonResponse(resp_dict)
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] | 2.28703 | 2,714 |
# -*- coding: utf-8 -*-
#
# michael a.g. aรฏvรกzis
# orthologue
# (c) 1998-2018 all rights reserved
#
from math import pi as ฯ
from .SI import radian
degree = ฯ/180 * radian
arcminute = degree / 60
arcsecond = arcminute / 60
deg = degree
rad = radian
# end of file
| [
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] | 2.561905 | 105 |
test_cases = int(input())
for test in range(1,test_cases+1):
if test%2==1:
green, purple = input().split()
green = int(green)
purple = int(purple)
participants = int(input())
cost = 0
for participant in range(0,participants):
m, n = map(int,input().split())
if m==1 and n==1 :
cost = cost + green + purple
elif n==1 and m==0 :
cost = cost + purple
elif m==1 and n==0:
cost = cost + green
else:
cost = cost + 0
print(cost)
else:
green, purple = input().split()
green = int(green)
purple = int(purple)
participants = int(input())
cost = 0
for participant in range(0,participants):
m, n = map(int,input().split())
if m==1 and n==1 :
cost = cost + green + purple
elif n==1 and m==0 :
cost = cost + green
elif m==1 and n==0:
cost = cost + purple
else:
cost = cost + 0
print(cost)
#ideal code
# Write your code here
'''for i in range(int(input())):
cost_of_green,cost_of_blue = sorted(list(map(int,input().strip().split())))
m = 0
n = 0
for p in range(int(input())):
a,b = list(map(int,input().strip().split()))
if (a==1 and b==1) :
m += 1
n += 1
elif a==1 and b==0:
m += 1
elif b == 1 and a==0:
n +=1
if m > n or m == n:
print(m*cost_of_green + n*cost_of_blue)
elif m < n :
print(n*cost_of_green + m*cost_of_blue)''' | [
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] | 1.798774 | 979 |
import numpy as np
_loss_functions = {}
@_register_name("relu")
@_register_name("relu_margin")
@_register_name("margin")
@_register_name("margin_sq")
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] | 2.57377 | 61 |
import tomviz.operators
import time
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] | 3.363636 | 11 |
from __future__ import absolute_import
from __future__ import unicode_literals
import doctest
from collections import namedtuple
from testil import eq
from corehq.util import itertools
from corehq.util.itertools import zip_with_gaps
def test_zip_with_gaps_key_funcs():
"""
Specifying a keyfunc should use it to match items
"""
WithFirstName = namedtuple('WithFirstName', 'username first_name')
WithLastName = namedtuple('WithLastName', 'username last_name')
devs = [
('Biyeun', 'Buczyk', 'biyeun'),
('Cory', 'Zue', 'czue'),
('Danny', 'Roberts', 'dannyroberts'),
('Jenny', 'Schweers', 'orangejenny'),
('Nick', 'Pellegrino', 'nickpell'),
('Simon', 'Kelly', 'snopoke'),
]
some_first_names = [WithFirstName(u, f) for i, (f, l, u) in enumerate(devs) if i < 4]
all_last_names = [WithLastName(u, l) for f, l, u in devs]
zipped = zip_with_gaps(
sorted(all_last_names, key=get_username),
sorted(some_first_names, key=get_username),
all_items_key=get_username,
some_items_key=get_username
)
names = [(f.first_name, l.last_name) for l, f in zipped]
eq(names, [
('Biyeun', 'Buczyk'),
('Cory', 'Zue'),
('Danny', 'Roberts'),
('Jenny', 'Schweers'),
])
def test_zip_with_gaps_missing_from_all():
"""
Items missing from `all_items` will be missing from the result
"""
all_items = [1, 2, 2, 3, 3, 3, 5, 5, 5, 5, 5]
some_items = [3, 3, 3, 3, 3]
zipped = zip_with_gaps(all_items, some_items)
eq(list(zipped), [(3, 3), (3, 3), (3, 3)])
def test_zip_with_gaps_not_in_all():
"""
Items not found in `all_items` will skip all remaining items.
"""
all_items = [1, 2, 4]
some_items = [1, 3, 4]
zipped = zip_with_gaps(all_items, some_items)
eq(list(zipped), [(1, 1)])
def test_zip_with_gaps_unsorted():
"""
Unsorted `some_items` will return the first item from `all_items`
that matches and drop the skipped items.
"""
all_items = [1, 2, 3, 4, 5, 6, 7]
some_items = [6, 7, 3, 4]
zipped = zip_with_gaps(all_items, some_items)
eq(list(zipped), [(6, 6), (7, 7)])
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#!/usr/bin/env python
# Usage: python ingestion.py input_dir output_dir ingestion_program_dir submission_program_dir
# data result ingestion code of participants
# AS A PARTICIPANT, DO NOT MODIFY THIS CODE.
#
# This is the "ingestion program" written by the organizers.
# This program also runs on the challenge platform to test your code.
#
# The input directory input_dir (e.g. AutoDL_sample_data/) contains one dataset
# folder (e.g. adult.data/) with the training set (train/) and test set (test/),
# each containing an some tfrecords data with a `metadata.textproto` file of
# metadata on the dataset. So one AutoDL dataset will look like
#
# adult.data
# โโโ test
# โย ย โโโ metadata.textproto
# โย ย โโโ sample-adult-test.tfrecord
# โโโ train
# โโโ metadata.textproto
# โโโ sample-adult-train.tfrecord
#
# The output directory output_dir (e.g. AutoDL_sample_result_submission/)
# will receive all predictions made during the whole train/predict process
# (thus this directory is updated when a new prediction is made):
# adult.predict_0
# adult.predict_1
# adult.predict_2
# ...
#
# The code directory submission_program_dir (e.g. AutoDL_sample_code_submission/)
# should contain your code submission model.py (and possibly other functions
# it depends upon).
#
# We implemented several classes:
# 1) DATA LOADING:
# ------------
# dataset.py
# dataset.AutoDLMetadata: Read metadata in metadata.textproto
# dataset.AutoDLDataset: Read data and give tf.data.Dataset
# 2) LEARNING MACHINE:
# ----------------
# model.py
# model.Model.train
# model.Model.test
#
# ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS".
# UNIVERSITE PARIS SUD, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM
# ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE
# WARRANTY OF NON-INFRIGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS.
# IN NO EVENT SHALL UNIVERSITE PARIS SUD AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL,
# INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN
# CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS,
# PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE.
#
# Main contributors: Isabelle Guyon and Zhengying Liu
# =========================== BEGIN OPTIONS ==============================
# Verbose mode:
##############
# Recommended to keep verbose = True: shows various progression messages
verbose = True # outputs messages to stdout and stderr for debug purposes
# Debug level:
##############
# 0: run the code normally, using the time budget of the tasks
# 1: run the code normally, but limits the time to max_time
# 2: run everything, but do not train, generate random outputs in max_time
# 3: stop before the loop on datasets
# 4: just list the directories and program version
debug_mode = 0
# Time budget
#############
# Maximum time of training in seconds PER DATASET (there may be several datasets).
# The code should keep track of time spent and NOT exceed the time limit
# in the dataset "info" file, stored in D.info['time_budget'], see code below.
# If debug >=1, you can decrease the maximum time (in sec) with this variable:
max_time = 300
# Maximum number of cycles, number of samples, and estimators
#############################################################
# Your training algorithm may be fast, so you may want to limit anyways the
# number of points on your learning curve (this is on a log scale, so each
# point uses twice as many time than the previous one.)
# The original code was modified to do only a small "time probing" followed
# by one single cycle. We can now also give a maximum number of estimators
# (base learners).
max_cycle = 1
max_estimators = 1000
max_samples = float('Inf')
# Redirect stardant output to live results page (detailed_results.html)
# to have live output for debugging
REDIRECT_STDOUT = False
# I/O defaults
##############
# If true, the previous output directory is not overwritten, it changes name
save_previous_results = False
# Use default location for the input and output data:
# If no arguments to run.py are provided, this is where the data will be found
# and the results written to. Change the root_dir to your local directory.
import os
from os import getcwd as pwd
from os.path import join
import shutil # for deleting a whole directory
from functools import partial
import glob
# Default I/O directories:
# root_dir is the parent directory of the folder "AutoDL_ingestion_program"
root_dir = os.path.abspath(os.path.join(_HERE(), os.pardir))
default_input_dir = join(root_dir, "AutoDL_sample_data")
default_output_dir = join(root_dir, "AutoDL_sample_result_submission")
default_program_dir = join(root_dir, "AutoDL_ingestion_program")
default_submission_dir = join(root_dir, "AutoDL_sample_code_submission")
# =============================================================================
# =========================== END USER OPTIONS ================================
# =============================================================================
# Version of the sample code
version = 1
# General purpose functions
import time
import numpy as np
overall_start = time.time() # <== Mark starting time
import os
import sys
from sys import argv, path
import datetime
the_date = datetime.datetime.now().strftime("%y-%m-%d %H:%M:%S")
def print_log(*content):
"""Logging function. (could've also used `import logging`.)"""
if verbose:
now = datetime.datetime.now().strftime("%y-%m-%d %H:%M:%S")
print("INGESTION INFO: " + str(now)+ " ", end='')
print(*content)
def clean_last_output(output_dir):
"""Clean existing output_dir of possible last execution.
This includes directories such as:
AutoDL_sample_result_submission/
AutoDL_scoring_output/
checkpoints_adult/
/tmp/checkpoints_adult/
...
Using this function, the user doesn't need to worry about the generated
folders of last execution.
"""
if os.path.isdir(output_dir):
if verbose:
print_log("Cleaning existing output_dir: {}".format(output_dir))
shutil.rmtree(output_dir)
model_dir = os.path.join(os.path.dirname(os.path.realpath(model.__file__)),
os.pardir)
for parent_dir in ['/tmp/', os.path.join(output_dir, os.pardir), model_dir]:
# Clean existing checkpoints
checkpoints_glob =\
os.path.abspath(os.path.join(parent_dir, 'checkpoints*'))
checkpoints_dirs = glob.glob(checkpoints_glob)
for checkpoints_dir in checkpoints_dirs:
if verbose:
print_log("Cleaning existing checkpoints_dir: {}"\
.format(checkpoints_dir))
shutil.rmtree(checkpoints_dir)
def get_time_budget(autodl_dataset):
"""Time budget for a given AutoDLDataset."""
# TODO: decision to make on time budget.
# Now it's 2 hours for any dataset (to be discussed).
return 7200
# =========================== BEGIN PROGRAM ================================
if __name__=="__main__" and debug_mode<4:
#### Check whether everything went well (no time exceeded)
execution_success = True
#### INPUT/OUTPUT: Get input and output directory names
if len(argv)==1: # Use the default input and output directories if no arguments are provided
input_dir = default_input_dir
output_dir = default_output_dir
program_dir= default_program_dir
submission_dir= default_submission_dir
score_dir = join(root_dir, "AutoDL_scoring_output")
elif len(argv)==2: # the case for indicating special input_dir
input_dir = argv[1]
output_dir = default_output_dir
program_dir= default_program_dir
submission_dir= default_submission_dir
score_dir = join(root_dir, "AutoDL_scoring_output")
elif len(argv)==3: # the case for indicating special input_dir and submission_dir. The case for run_local_test.py
input_dir = argv[1]
output_dir = default_output_dir
program_dir= default_program_dir
submission_dir= argv[2]
score_dir = join(root_dir, "AutoDL_scoring_output")
else: # the case on CodaLab platform
input_dir = os.path.abspath(os.path.join(argv[1], '../input_data'))
output_dir = os.path.abspath(os.path.join(argv[1], 'res'))
program_dir = os.path.abspath(argv[3])
submission_dir = os.path.abspath(os.path.join(argv[4], '../submission'))
score_dir = os.path.abspath(os.path.join(argv[4], '../output'))
# if verbose: # For debugging
# print_log("sys.argv = ", sys.argv)
# print_log("Using input_dir: " + input_dir)
# print_log("Using output_dir: " + output_dir)
# print_log("Using program_dir: " + program_dir)
# print_log("Using submission_dir: " + submission_dir)
# Redirect standard output to have live debugging info (esp. on CodaLab)
if REDIRECT_STDOUT:
sys.stdout = open(os.path.join(score_dir, 'detailed_results.html'), 'a')
print = partial(print, flush=True)
# Our libraries
path.append(program_dir)
path.append(submission_dir)
#IG: to allow submitting the starting kit as sample submission
path.append(submission_dir + '/AutoDL_sample_code_submission')
import data_io
from data_io import vprint
import model # participants' model.py
from model import Model
from dataset import AutoDLDataset # THE class of AutoDL datasets
# Clear potentiablly results of previous execution (for local run)
clean_last_output(output_dir)
if debug_mode >= 4: # Show library version and directory structure
data_io.show_dir(".")
# Move old results and create a new output directory (useful if you run locally)
if save_previous_results:
data_io.mvdir(output_dir, output_dir+'_'+the_date)
data_io.mkdir(output_dir)
# Create start file to tell scoring program that submission has begin
start_filename = 'start.txt'
start_filepath = os.path.join(output_dir, start_filename)
with open(start_filepath, 'w') as f:
f.write('Started!')
#### INVENTORY DATA (and sort dataset names alphabetically)
datanames = data_io.inventory_data(input_dir)
#### Delete zip files and metadata file
datanames = [x for x in datanames if x.endswith('.data')]
#### DEBUG MODE: Show dataset list and STOP
if debug_mode>=3:
data_io.show_version()
data_io.show_io(input_dir, output_dir)
print_log('****** Ingestion program version ' + str(version) + ' ******\n\n' + '========== DATASETS ==========\n')
data_io.write_list(datanames)
datanames = [] # Do not proceed with learning and testing
#### MAIN LOOP OVER DATASETS:
overall_time_budget = 0
time_left_over = 0
# Loop over datasets (if several)
# For AutoDL challenge, there is only 1 dataset for each track, so this loop
# can actually be ignored. Here basename is e.g. 'adult.data'
for i, basename in enumerate(datanames):
print_log("========== Ingestion program version " + str(version) + " ==========")
print_log("************************************************")
print_log("******** Processing dataset " + basename[:-5].capitalize() + " ********")
print_log("************************************************")
# ======== Learning on a time budget:
# Keep track of time not to exceed your time budget. Time spent to inventory data neglected.
start = time.time()
# ======== Creating a data object with data, informations about it
print_log("Reading training set and test set...")
##### Begin creating training set and test set #####
D_train = AutoDLDataset(os.path.join(input_dir, basename, "train"))
D_test = AutoDLDataset(os.path.join(input_dir, basename, "test"))
##### End creating training set and test set #####
# ======== Keep track of time
if debug_mode<1:
time_budget = get_time_budget(D_train) # <== HERE IS THE TIME BUDGET!
else:
time_budget = max_time
# ========= Creating a model
print_log("Creating model...")
##### Begin creating model #####
M = Model(D_train.get_metadata()) # The metadata of D_train and D_test only differ in sample_count
###### End creating model ######
# Keeping track of how many predictions are made
prediction_order_number = 0
# Start the CORE PART: train/predict process
start = time.time()
while(True):
remaining_time_budget = start + time_budget - time.time()
print_log("Training the model...")
# Train the model
M.train(D_train.get_dataset(),
remaining_time_budget=remaining_time_budget)
remaining_time_budget = start + time_budget - time.time()
# Make predictions using the trained model
Y_test = M.test(D_test.get_dataset(),
remaining_time_budget=remaining_time_budget)
if Y_test is None: # Stop train/predict process if Y_test is None
break
# Prediction files: adult.predict_0, adult.predict_1, ...
filename_test = basename[:-5] + '.predict_' +\
str(prediction_order_number)
# Write predictions to output_dir
data_io.write(os.path.join(output_dir,filename_test), Y_test)
prediction_order_number += 1
print_log("[+] Prediction success, time spent so far %5.2f sec" % (time.time() - start))
remaining_time_budget = start + time_budget - time.time()
print_log( "[+] Time left %5.2f sec" % remaining_time_budget)
if remaining_time_budget<=0:
break
# Finishing ingestion program
overall_time_spent = time.time() - overall_start
# Delete start file to clean folder
if os.path.exists(start_filepath):
os.remove(start_filepath)
# Write overall_time_spent to a duration.txt file
duration_filename = 'duration.txt'
with open(os.path.join(output_dir, duration_filename), 'w') as f:
f.write(str(overall_time_spent))
if verbose:
print_log("Successfully write duration to {}.".format(duration_filename))
if execution_success:
print_log("[+] Done")
print_log("[+] Overall time spent %5.2f sec " % overall_time_spent)
else:
print_log("[-] Done, but some tasks aborted because time limit exceeded")
print_log("[-] Overall time spent %5.2f sec " % overall_time_spent)
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] | 2.70199 | 5,577 |
import datetime
from dateutil.parser import parse as date_parser
from mongoengine import Document, StringField, ListField
from django.conf import settings
from crits.core.crits_mongoengine import CritsBaseAttributes, CritsSourceDocument
from crits.core.crits_mongoengine import CritsActionsDocument
from crits.core.fields import CritsDateTimeField
from crits.emails.migrate import migrate_email
from crits.core.data_tools import convert_datetimes_to_string
class RawHeadersField(StringField):
"""
Raw Header class.
"""
def transform(self, value):
"""
If we receive a list instead of a string, convert it.
:param value: The raw headers.
:type value: str or list
:returns: str
"""
if isinstance(value, list):
tmp = ''
for v in value:
tmp = ' '.join([tmp, self.transform(v)])
return tmp
return value
class Email(CritsBaseAttributes, CritsSourceDocument, CritsActionsDocument,
Document):
"""
Email Class.
"""
meta = {
# mongoengine adds fields _cls and _types and uses them to filter database
# responses unless you disallow inheritance. In other words, we
# can't see any of our old data unless we add _cls and _types
# attributes to them or turn off inheritance.
#So we'll turn inheritance off.
# (See http://mongoengine-odm.readthedocs.org/en/latest/guide/defining-documents.html#working-with-existing-data)
"collection": settings.COL_EMAIL,
"crits_type": 'Email',
"latest_schema_version": 2,
"schema_doc": {
'boundary': 'Email boundary',
'campaign': 'List [] of campaigns attributed to this email',
'cc': 'List [] of CC recipients',
'date': 'String of date header field',
'from': 'From header field',
'helo': 'HELO',
'isodate': 'ISODate conversion of date header field',
'message_id': 'Message-ID header field',
'modified': 'When this object was last modified',
'objects': 'List of objects in this email',
'originating_ip': 'Originating-IP header field',
'raw_body': 'Email raw body',
'raw_header': 'Email raw headers',
'relationships': 'List of relationships with this email',
'reply_to': 'Reply-To header field',
'sender': 'Sender header field',
'shared_with': 'Dictionary of sources that this email may be shared with and whether it has been shared already',
'source': 'List [] of sources that provided information on this email',
'subject': 'Email subject',
'to': 'To header field',
'x_originating_ip': 'X-Originating-IP header field',
'x_mailer': 'X-Mailer header field',
},
"jtable_opts": {
'details_url': 'crits.emails.views.email_detail',
'details_url_key': 'id',
'default_sort': "isodate DESC",
'searchurl': 'crits.emails.views.emails_listing',
'fields': [ "from_address", "subject", "isodate",
"source", "campaign", "id", "to",
"status", "cc" ],
'jtopts_fields': [ "details",
"from",
"recip",
"subject",
"isodate",
"source",
"campaign",
"status",
"favorite",
"id"],
'hidden_fields': [],
'linked_fields': [ "source", "campaign",
"from", "subject" ],
'details_link': 'details',
'no_sort': ['recip', 'details']
}
}
boundary = StringField()
cc = ListField(StringField())
date = StringField(required=True)
from_address = StringField(db_field="from")
helo = StringField()
# isodate is an interally-set attribute and on save will be overwritten
# with the isodate version of the email's date attribute.
isodate = CritsDateTimeField()
message_id = StringField()
originating_ip = StringField()
raw_body = StringField()
raw_header = RawHeadersField(db_field="raw_headers")
reply_to = StringField()
sender = StringField()
subject = StringField()
to = ListField(StringField())
x_originating_ip = StringField()
x_mailer = StringField()
def migrate(self):
"""
Migrate to latest schema version.
"""
migrate_email(self)
def _custom_save(self, force_insert=False, validate=True, clean=False,
write_concern=None, cascade=None, cascade_kwargs=None,
_refs=None, username=None, **kwargs):
"""
Override our core custom save. This will ensure if there is a "date"
string available for the email that we generate a corresponding
"isodate" field which is more useful for database sorting/searching.
"""
if hasattr(self, 'date'):
if self.date:
if isinstance(self.date, datetime.datetime):
self.isodate = self.date
self.date = convert_datetimes_to_string(self.date)
else:
self.isodate = date_parser(self.date, fuzzy=True)
else:
if self.isodate:
if isinstance(self.isodate, datetime.datetime):
self.date = convert_datetimes_to_string(self.isodate)
else:
self.isodate = None
return super(self.__class__, self)._custom_save(force_insert, validate,
clean, write_concern, cascade, cascade_kwargs, _refs, username)
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] | 2.059583 | 3,021 |
#-*- coding: utf-8 -*-
""" StaSh input history
"""
import json
from stash.lib.libslog import slog
_pyfile_ = __file__.split("/")[-1]
slog(f'pyfile: {_pyfile_}')
from stash.system.shcommon import ShEventNotFound
class ShHistory(object):
"""
This class is responsible for input history.
:param stash: the StaSh core
:type stash: StaSh
"""
ENCODING = "utf-8"
DEFAULT = "_default"
@classmethod
def load(cls, path, stash):
"""
Load the history from a path.
:param path: path to load from.
:type path: str
:param config: the StaSh core
:type config: StaSh
:return: the history loaded from the file
:rtype: ShHistory
"""
shh = cls(stash)
try:
with open(path, "r", encoding=cls.ENCODING) as fin:
h = json.loads(u"" + fin.read())
except ValueError:
h = {"StaSh.runtime": cls.load_old_format(path)}
shh._histories = h
return shh
@classmethod
def load_old_format(cls, path):
"""
Load the content of an old-style history.
:param path: path to load from
:type path: str
:return: the lines loaded from the file
:rtype: list of str
"""
with open(path, "r", encoding=cls.ENCODING) as fin:
lines = [line.strip() for line in fin.readlines()]
return lines
def save(self, path):
"""
Save the history to a path.
:param path: path to save to.
:type path: str
"""
with open(path, "w", encoding=self.ENCODING) as fout:
s = json.dumps(self._histories)
fout.write(u"" + s) # ensure unicode
def clear(self, target=None):
"""
Clear the history
:param target: history to clear or None for current
:type history: str or None
"""
if target is None:
target = self._current
if target in self._histories:
del self._histories[target]
def clear_all(self):
"""
Clear all histories.
"""
self._histories = {}
def swap(self, target):
"""
Swap the history
:param target: identifier to get the history for
:type target: str or None
"""
self._current = target
def add(self, line, always=False):
"""
Add a line to the history.
:param line: line to add to history
:type line: str
:param always: always add this line, regardless of config
:type always: bool
"""
if self._current not in self._histories:
self._histories[self._current] = []
stripped = line.strip()
last_line = (self._histories[self._current][-1]
if len(self._histories[self._current]) > 0 else None)
if not always:
# check if this line should be added
if stripped == last_line and not self.allow_double:
# prevent double lines
return
if line.startswith(" ") and self.hide_whitespace:
# hide lines starting with a whitespace
return
self._histories[self._current].append(stripped)
# ensure maxsize
while len(self._histories[self._current]) > max(0, self.maxsize):
self._histories[self._current].pop(0)
# reset index
self.reset_idx()
def getlist(self):
"""
Return a list of the current history.
:return: list of current history entries
:rtype: list of str
"""
if self._current not in self._histories:
self._histories[self._current] = []
return self._histories[self._current][::-1]
def search(self, tok):
"""
Search the history.
:param tok:
:type tok:
:return: last entry in history matching the search
:rtype: str
"""
history = self.getlist()
search_string = tok[1:]
if search_string == '':
return ''
if search_string == '!':
return history[0]
try:
idx = int(search_string)
try:
return history[::-1][idx]
except IndexError:
raise ShEventNotFound(tok)
except ValueError:
for entry in history:
if entry.startswith(search_string):
return entry
raise ShEventNotFound(tok)
def reset_idx(self):
"""
Reset the index of the current position in the history
"""
self.idx = -1
def up(self):
"""
Move upwards in the history.
"""
# Save the unfinished line user is typing before showing entries from history
history = self.getlist()
if self.idx == -1:
self.templine = self.stash.mini_buffer.modifiable_string.rstrip()
self.idx += 1
if self.idx >= len(history):
self.idx = len(history) - 1
else:
entry = history[self.idx]
# If move up away from an unfinished input line, try search history for
# a line starts with the unfinished line
if self.idx == 0 and self.ipython_style_history_search:
for idx, hs in enumerate(history):
if hs.startswith(self.templine):
entry = hs
self.idx = idx
break
self.stash.mini_buffer.feed(None, entry)
def down(self):
"""
Move downwards in the history
"""
history = self.getlist()
self.idx -= 1
if self.idx < -1:
self.idx = -1
else:
if self.idx == -1:
entry = self.templine
else:
entry = history[self.idx]
self.stash.mini_buffer.feed(None, entry)
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5726,
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58,
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13,
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220,
220,
220,
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220,
220,
220,
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220,
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301,
1077,
13,
45313,
62,
22252,
13,
12363,
7,
14202,
11,
5726,
8,
198
] | 2.067726 | 2,894 |
from .gae import GAE, VGAE
| [
6738,
764,
25002,
1330,
402,
14242,
11,
569,
9273,
36,
201,
198
] | 2.333333 | 12 |
import re
from random import randrange
from model.contact import Contact
| [
11748,
302,
198,
6738,
4738,
1330,
43720,
9521,
198,
6738,
2746,
13,
32057,
1330,
14039,
628,
628,
198
] | 4.277778 | 18 |
import numpy
import heapq
import scipy.sparse
from apgl.graph.AbstractSingleGraph import AbstractSingleGraph
class DictGraph(AbstractSingleGraph):
"""
A graph with nodes stored in a dictionary. In particular the graph data structure is a
dict of dicts. Edges and vertices can be labeled with anything.
"""
def __init__(self, undirected=True):
"""
Create a new DictGraph.
:param undirected: Specify whether the graph has directed or undirected edges
:type undirected: `bool`
"""
self.undirected = undirected
self.adjacencies = {}
self.vertices = {}
def addEdge(self, vertex1, vertex2, value=1.0):
"""
Add an edge between two vertices.
:param vertex1: The name of the first vertex.
:param vertex2: The name of the second vertex.
:param value: The value of the edge.
"""
if value == None:
raise ValueError("Cannot have None as edge value")
self.__touchVertex(vertex1)
self.__touchVertex(vertex2)
self.adjacencies[vertex1][vertex2] = value
if self.undirected:
self.adjacencies[vertex2][vertex1] = value
def addEdges(self, edgeList, edgeValues=None):
"""
Add a set of edges to this graph given by a list of tuples of vertex ids.
The value of each edge is simply set to 1 if edgeValues=None otherwise
it is set to the corresponding entry of edgeValues.
:param edgeList: A list of pairs of verted ids
:param edgeValues: A corresponding list of vertex values.
"""
i = 0
for edge in edgeList:
(vertex1, vertex2) = edge
if edgeValues == None:
value = 1
else:
value = edgeValues[i]
self.__touchVertex(vertex1)
self.__touchVertex(vertex2)
self.adjacencies[vertex1][vertex2] = value
if self.undirected:
self.adjacencies[vertex2][vertex1] = value
i += 1
def __touchVertex(self, vertexId):
"""
If the vertex exists, do nothing. Otherwise add it to vertices and
adjacencies.
"""
if vertexId not in self.vertices:
self.vertices[vertexId] = None
if vertexId not in self.adjacencies:
self.adjacencies[vertexId] = {}
def removeEdge(self, vertex1, vertex2):
"""
Remove an edge. Does not remove the vertices.
:param vertex1: The name of the first vertex.
:param vertex2: The name of the second vertex.
"""
self.__removeDirectedEdge(vertex1, vertex2)
if self.undirected:
self.__removeDirectedEdge(vertex2, vertex1)
def isUndirected(self):
"""
Returns true if the current graph is undirected, otherwise false.
"""
return self.undirected
def getNumEdges(self):
"""
Returns the total number of edges in graph.
"""
numEdges = 0
for vertex1 in self.adjacencies.keys():
numEdges += len(self.adjacencies[vertex1])
if not self.undirected:
return numEdges
else:
#Count self edges again
for vertex1 in self.adjacencies.keys():
if vertex1 in self.adjacencies[vertex1]:
numEdges += 1
return numEdges/2
def getNumVertices(self):
"""
Returns the number of vertices in the graph.
"""
return len(self.adjacencies)
def getEdge(self, vertex1, vertex2):
"""
Returns the value of the edge between two vertices. If there is no edge
between the vertices None is returned.
:param vertex1: The name of the first vertex.
:param vertex2: The name of the second vertex.
"""
if vertex1 not in self.adjacencies:
raise ValueError("Vertex is not present in graph: " + str(vertex1))
if vertex2 not in self.adjacencies:
raise ValueError("Vertex is not present in graph: " + str(vertex2))
if vertex2 not in self.adjacencies[vertex1]:
return None
else:
return self.adjacencies[vertex1][vertex2]
def neighbours(self, vertexId):
"""
Find a list of neighbours of the current vertex. In a directed graph, it
is the list of all vertices with an edge from the current vertex.
:param vertexId: The id of the vertex.
"""
if vertexId not in self.adjacencies:
raise ValueError("Vertex is not present in graph: " + str(vertexId))
return list(self.adjacencies[vertexId].keys())
def getVertex(self, vertexId):
"""
Returns the label of the given vertex, or None if no label.
:param vertex: The name of the first vertex.
"""
if vertexId not in self.vertices:
raise ValueError("Vertex is not present in graph: " + str(vertexId))
return self.vertices[vertexId]
def setVertex(self, vertexId, vertex):
"""
Sets the vertexId with the value. Overwrites value if already present.
"""
self.__touchVertex(vertexId)
self.vertices[vertexId] = vertex
def getAllVertexIds(self):
"""
Returns a list of the vertex ids (or names) in this graph.
"""
return list(self.vertices.keys())
def getAllEdges(self):
"""
Returns a list of tuples of all the edges of this graph.
"""
edges = []
for vertex1 in list(self.vertices.keys()):
for vertex2 in self.neighbours(vertex1):
if self.undirected == True and (vertex2, vertex1) not in edges:
edges.append((vertex1, vertex2))
elif self.undirected == False:
edges.append((vertex1, vertex2))
return edges
def getWeightMatrix(self):
"""
Returns a weight matrix representation of the graph as a numpy array. The
indices in the matrix correspond to the keys returned by getAllVertexIds,
and edge labels are assigned to 1 for edges with non-numeric values.
"""
W = numpy.zeros((self.getNumVertices(), self.getNumVertices()))
return self.__populateWeightMatrix(W)
def getSparseWeightMatrix(self, format="lil"):
"""
Returns a weight matrix representation of the graph as a scipy sparse
lil_matrix by default. The indices in the matrix correspond to the keys
returned by getAllVertexIds. Edge labels are assigned to 1 for edges with
non-numeric values. Available formats are: lil for scipy.sparse.lil_matrix,
csr for scipy.sparse.csr_matrix, csc for scipy.sparse.csc_matrix, and
pysparse for pysparse's ll_mat.
:param format: The format of the sparse matrix.
"""
if format=="lil":
W = scipy.sparse.lil_matrix((self.size, self.size))
W = self.__populateWeightMatrix(W)
elif format=="csr":
W = scipy.sparse.lil_matrix((self.size, self.size))
W = self.__populateWeightMatrix(W)
W = W.tocsr()
elif format=="csc":
W = scipy.sparse.lil_matrix((self.size, self.size))
W = self.__populateWeightMatrix(W)
W = W.tocsc()
elif format=="pysparse":
from pysparse import spmatrix
W = spmatrix.ll_mat(self.size, self.size)
W = self.__populateWeightMatrix(W)
else:
raise ValueError("Invalid format: " + format)
return W
def __populateWeightMatrix(self, W):
"""
Fill the weight matrix W with edge weights according to this graph.
"""
keys = self.vertices.keys()
keyInds = {}
i = 0
for k in keys:
keyInds[k] = i
i += 1
for vertex1 in keys:
for vertex2 in self.neighbours(vertex1):
if self.undirected == True:
try:
edgeVal = self.adjacencies[vertex1][vertex2]
W[keyInds[vertex1], keyInds[vertex2]] = edgeVal
W[keyInds[vertex2], keyInds[vertex1]] = edgeVal
except:
W[keyInds[vertex1], keyInds[vertex2]] = 1
W[keyInds[vertex2], keyInds[vertex1]] = 1
elif self.undirected == False:
try:
W[keyInds[vertex1], keyInds[vertex2]] = self.adjacencies[vertex1][vertex2]
except:
W[keyInds[vertex1], keyInds[vertex2]] = 1
return W
def getAllEdgeIndices(self):
"""
Returns a numpy array of size (numEdges x 2) of edge index pairs V. The ith
row of V, V[i, :], corresponds to an edge from V[i, 0] to V[i, 1]. The corresponding
vertex names are found using getAllVertexIds().
"""
edges = numpy.zeros((self.getNumEdges(), 2), numpy.int)
keyInds = {}
i = 0
for k in self.vertices.keys():
keyInds[k] = i
i += 1
i = 0
for vertex1 in self.vertices.keys():
for vertex2 in self.neighbours(vertex1):
if self.undirected and keyInds[vertex2] >= keyInds[vertex1] or not self.undirected:
edges[i, :] = numpy.array([keyInds[vertex1], keyInds[vertex2]])
i += 1
return edges
def subgraph(self, vertexIds):
"""
Compute the subgraph containing only the corresponding vertexIds and
the edges between them.
"""
subgraph = DictGraph(self.undirected)
"""
edgeList = self.getAllEdges()
subgraphEdgeList = []
for edge in edgeList:
(vertex1, vertex2) = edge
if vertex1 in vertexIds and vertex2 in vertexIds:
subgraphEdgeList.append(edge)
subgraph.addEdges(subgraphEdgeList)
"""
vertexIds = set(vertexIds)
for vertexId in vertexIds:
if vertexId in self.adjacencies.keys():
subgraph.__touchVertex(vertexId)
subgraph.adjacencies[vertexId] = self.adjacencies[vertexId].copy()
subgraph.vertices[vertexId] = self.vertices[vertexId]
deleteIdList = []
#Now remove the elements in the adjacencies:
for vertex1 in subgraph.adjacencies.keys():
for vertex2 in subgraph.adjacencies[vertex1].keys():
if vertex2 not in vertexIds:
deleteIdList.append((vertex1, vertex2))
for vertex1, vertex2 in deleteIdList:
del subgraph.adjacencies[vertex1][vertex2]
return subgraph
def neighbourOf(self, vertex):
"""
Returns the list of neighbours of the current neighbour.
"""
lst = []
for (v1, adj) in self.adjacencies.items():
if vertex in adj.keys():
lst.append(v1)
return lst
def outDegreeSequence(self):
"""
Find the out degree sequence. Return the sequence as a vector along with
the corresponding vertices in a list.
"""
vertexList = []
degSeq = numpy.zeros(self.getNumVertices(), numpy.int)
vertexIds = self.getAllVertexIds()
for i in range(len(vertexIds)):
vertex = vertexIds[i]
degSeq[i] = len(self.adjacencies[vertex])
vertexList.append(vertex)
return degSeq, vertexList
def inDegreeSequence(self):
"""
Find the in degree sequence. Return the sequence as a vector along with
the corresponding vertices in a list.
"""
vertexList = []
degSeq = numpy.zeros(self.getNumVertices(), numpy.int)
vertexIds = self.getAllVertexIds()
for i in range(len(vertexIds)):
vertex = vertexIds[i]
for vertex2 in self.adjacencies[vertex].items():
degSeq[vertexIds.index(vertex2[0])] += 1
vertexList.append(vertex)
return degSeq, vertexList
def vertexExists(self, vertexId):
"""
Returns true if the vertex with the given name exists, otherwise false.
"""
return vertexId in self.vertices
def edgeExists(self, vertexId1, vertexId2):
"""
Return true if the edge exists between two vertices
"""
if not self.vertexExists(vertexId1):
return False
return vertexId2 in self.adjacencies[vertexId1]
def removeVertex(self, vertexId):
"""
Remove a vertex and all its edges.
:param vertexId: The id of the vertex to remove.
"""
neighbours = self.neighbours(vertexId)
del self.adjacencies[vertexId]
del self.vertices[vertexId]
if self.undirected:
for vertexId2 in neighbours:
del self.adjacencies[vertexId2][vertexId]
else:
for vertexId2 in self.getAllVertexIds():
if vertexId in self.adjacencies[vertexId2]:
del self.adjacencies[vertexId2][vertexId]
def toSparseGraph(self):
"""
Convert the current graph to a SparseGraph. Currently, vertex labels
are not converted.
"""
from apgl.graph import SparseGraph
W = self.getSparseWeightMatrix(format="csr")
graph = SparseGraph(W.shape[0], W=W, undirected=self.undirected)
return graph
def toPySparseGraph(self):
"""
Convert the current graph to a PySparseGraph. Currently, vertex labels
are not converted.
"""
from apgl.graph import PySparseGraph
W = self.getSparseWeightMatrix(format="pysparse")
graph = PySparseGraph(W.shape[0], W=W, undirected=self.undirected)
return graph
def degreeSequence(self):
"""
:returns: a vector of the degrees (including self edges) for each vertex for an undirected graph.
"""
if not self.isUndirected():
raise ValueError("degreeSequence is only for undirected graphs")
degSequence = self.outDegreeSequence()[0]
#A very slow method of adding diagonal entries
for j, i in enumerate(self.getAllVertexIds()):
if self.getEdge(i, i) != None:
degSequence[j] += 1
return degSequence
def getNumDirEdges(self):
"""
:returns: the number of edges, taking this graph as a directed graph.
"""
i = 0
for v in self.adjacencies.keys():
i += len(self.adjacencies[v])
return i
def dijkstrasAlgorithm(self, vertexId, neighbourLists=None):
"""
Run Dijkstras Algorithm on the graph for a given source vertex. Returns
an array with the distance to all vertices (including itself).
:param vertexId: the index of the source vertex.
:returns: An array whose ith element is the distance to vertex i.
"""
if neighbourLists!=None:
neighbourIndices, neighbourWeights = neighbourLists
if len(neighbourIndices) != self.getNumVertices() or len(neighbourWeights) != self.getNumVertices():
raise ValueError("Adjacency lists must be of same size as graph")
else:
neighbourIndices, neighbourWeights = self.adjacencyList()
vertexIds = self.getAllVertexIds()
previous = numpy.zeros(self.size)
#This is an array of distances so far with first col as distance, and 2nd cols
distance = numpy.ones((self.size, 2))*numpy.inf
distance[vertexIds.index(vertexId), 0] = 0
distance[:, 1] = numpy.arange(self.size)
distance = distance.tolist()
heapq.heapify(distance)
#Dictionary of the tuples indexed by the vertex index returned at the end
distanceDict = {}
for i in distance:
distanceDict[i[1]] = i
INVALID = -1
#distanceArray is distance for each vertex
distanceArray = numpy.ones(self.size)*numpy.inf
notVisited = numpy.ones(self.size, numpy.bool)
while len(distanceDict) != 0:
minVertexIndex = INVALID
while minVertexIndex == INVALID:
(minVertexDistance, minVertexIndex) = heapq.heappop(distance)
distanceArray[minVertexIndex] = minVertexDistance
del(distanceDict[minVertexIndex])
notVisited[minVertexIndex] = False
if minVertexDistance == numpy.inf:
break
minVertexIndex = int(minVertexIndex)
cols = numpy.array(neighbourIndices[minVertexIndex])
weights = numpy.array(neighbourWeights[minVertexIndex])
#updateDistances(cols, weights, minVertexDistance, distanceDict, previous, distanceArray)
#If no neighbours then move onto next vertex
if cols.size == 0:
break
newDistances = weights + minVertexDistance
isBetter = numpy.logical_and(newDistances < distanceArray[cols], notVisited[cols])
for i in range(cols[isBetter].shape[0]):
j = cols[isBetter][i]
distanceDict[j][1] = INVALID
distanceDict[j] = [newDistances[isBetter][i], j]
heapq.heappush(distance, distanceDict[j])
distanceArray[j] = newDistances[isBetter][i]
return distanceArray
def adjacencyList(self):
"""
Returns an adjacency list representation L of the graph, in which L[i]
is the list of all neighbour *indices* of vertex index i. Furthermore, the method
returns W in which W[i] which is the corresponding set of weights. In essence,
this method is a way to map vertex ids to indices.
:returns L: A list whose ith element is a list of neighbours for vertex i.
:returns W: A list whose ith element is a list of neighbour weights for vertex i.
"""
neighbourIndices = []
neighbourWeights = []
vertexIds = self.getAllVertexIds()
for i in vertexIds:
neighbours = list(map(vertexIds.index, self.adjacencies[i].keys()))
neighbourIndices.append(neighbours)
neighbourWeights.append(list(self.adjacencies[i].values()))
return neighbourIndices, neighbourWeights
def findAllDistances(self, useWeights=True):
"""
Use the repeated calls to Dijkstra' algorithm to find the shortest path
between all pairs of vertices. Note that the shortest path of a vertex
to itself is always zero. Returns a matrix whose ij th entry is the
shortest path between vertices i and j.
:returns: A matrix of shortest paths between all vertices.
"""
neighbourLists = self.adjacencyList()
P = numpy.zeros((self.size, self.size))
for i, vertexId in enumerate(self.getAllVertexIds()):
P[i, :] = self.dijkstrasAlgorithm(vertexId, neighbourLists)
return P
def toIGraph(self):
"""
Convert this graph into a igraph Graph object, which requires igraph to be
installed. Edge values are stored under the "value" index. Vertices
are stored as indices with a "label" value being the corresponding vertex value.
:returns: An igraph Graph object.
"""
try:
import igraph
except ImportError:
raise ImportError("toIGraph() requires igraph")
newGraph = igraph.Graph(self.getNumVertices(), directed= not self.isUndirected())
#Add all vertices
newGraph.vs["label"] = self.getVertices(self.getAllVertexIds())
vertices = self.getAllVertexIds()
allEdges = self.getAllEdges()
for i in range(len(allEdges)):
vertexId1 = vertices.index(allEdges[i][0])
vertexId2 = vertices.index(allEdges[i][1])
newGraph.add_edge(vertexId1, vertexId2)
newGraph.es[i]["value"] = self.getEdge(allEdges[i][0], allEdges[i][1])
return newGraph
vertices = None
adjacencies = None
undirected = None
size = property(getNumVertices, doc="The number of vertices in the graph") | [
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13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
13015,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
329,
37423,
16,
287,
1351,
7,
944,
13,
1851,
1063,
13,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
37423,
17,
287,
2116,
13,
710,
394,
65,
4662,
7,
332,
16886,
16,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
917,
1060,
276,
6624,
6407,
290,
357,
332,
16886,
17,
11,
37423,
16,
8,
407,
287,
13015,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13015,
13,
33295,
19510,
332,
16886,
16,
11,
37423,
17,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
917,
1060,
276,
6624,
10352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13015,
13,
33295,
19510,
332,
16886,
16,
11,
37423,
17,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
13015,
628,
220,
220,
220,
825,
651,
25844,
46912,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
3463,
17593,
10552,
286,
262,
4823,
355,
257,
299,
32152,
7177,
13,
383,
198,
220,
220,
220,
220,
220,
220,
220,
36525,
287,
262,
17593,
6053,
284,
262,
8251,
4504,
416,
651,
3237,
13414,
16886,
7390,
82,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
290,
5743,
14722,
389,
8686,
284,
352,
329,
13015,
351,
1729,
12,
77,
39223,
3815,
13,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
370,
796,
299,
32152,
13,
9107,
418,
19510,
944,
13,
1136,
33111,
42369,
1063,
22784,
2116,
13,
1136,
33111,
42369,
1063,
3419,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
834,
12924,
5039,
25844,
46912,
7,
54,
8,
628,
198,
220,
220,
220,
825,
651,
50,
29572,
25844,
46912,
7,
944,
11,
5794,
2625,
75,
346,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
3463,
17593,
10552,
286,
262,
4823,
355,
257,
629,
541,
88,
29877,
220,
198,
220,
220,
220,
220,
220,
220,
220,
42280,
62,
6759,
8609,
416,
4277,
13,
383,
36525,
287,
262,
17593,
6053,
284,
262,
8251,
220,
198,
220,
220,
220,
220,
220,
220,
220,
4504,
416,
651,
3237,
13414,
16886,
7390,
82,
13,
13113,
14722,
389,
8686,
284,
352,
329,
13015,
351,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1729,
12,
77,
39223,
3815,
13,
14898,
17519,
389,
25,
42280,
329,
629,
541,
88,
13,
82,
29572,
13,
75,
346,
62,
6759,
8609,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
269,
27891,
329,
629,
541,
88,
13,
82,
29572,
13,
6359,
81,
62,
6759,
8609,
11,
269,
1416,
329,
629,
541,
88,
13,
82,
29572,
13,
66,
1416,
62,
6759,
8609,
11,
290,
220,
198,
220,
220,
220,
220,
220,
220,
220,
279,
893,
29572,
329,
279,
893,
29572,
338,
32660,
62,
6759,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
5794,
25,
383,
5794,
286,
262,
29877,
17593,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5794,
855,
1,
75,
346,
1298,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
629,
541,
88,
13,
82,
29572,
13,
75,
346,
62,
6759,
8609,
19510,
944,
13,
7857,
11,
2116,
13,
7857,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
2116,
13,
834,
12924,
5039,
25844,
46912,
7,
54,
8,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
5794,
855,
1,
6359,
81,
1298,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
629,
541,
88,
13,
82,
29572,
13,
75,
346,
62,
6759,
8609,
19510,
944,
13,
7857,
11,
2116,
13,
7857,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
2116,
13,
834,
12924,
5039,
25844,
46912,
7,
54,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
370,
13,
40301,
27891,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
5794,
855,
1,
66,
1416,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
629,
541,
88,
13,
82,
29572,
13,
75,
346,
62,
6759,
8609,
19510,
944,
13,
7857,
11,
2116,
13,
7857,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
2116,
13,
834,
12924,
5039,
25844,
46912,
7,
54,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
370,
13,
40301,
1416,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
5794,
855,
1,
79,
893,
29572,
1298,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
279,
893,
29572,
1330,
599,
6759,
8609,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
599,
6759,
8609,
13,
297,
62,
6759,
7,
944,
13,
7857,
11,
2116,
13,
7857,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
796,
2116,
13,
834,
12924,
5039,
25844,
46912,
7,
54,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
44651,
5794,
25,
366,
1343,
5794,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
370,
220,
628,
220,
220,
220,
825,
11593,
12924,
5039,
25844,
46912,
7,
944,
11,
370,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
27845,
262,
3463,
17593,
370,
351,
5743,
19590,
1864,
284,
428,
4823,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
796,
2116,
13,
1851,
1063,
13,
13083,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
5497,
82,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
1312,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
329,
479,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
5497,
82,
58,
74,
60,
796,
1312,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
15853,
352,
628,
220,
220,
220,
220,
220,
220,
220,
329,
37423,
16,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
37423,
17,
287,
2116,
13,
710,
394,
65,
4662,
7,
332,
16886,
16,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
917,
1060,
276,
6624,
6407,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5743,
7762,
796,
2116,
13,
324,
30482,
3976,
58,
332,
16886,
16,
7131,
332,
16886,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
16,
4357,
1994,
5497,
82,
58,
332,
16886,
17,
11907,
796,
5743,
7762,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
17,
4357,
1994,
5497,
82,
58,
332,
16886,
16,
11907,
796,
5743,
7762,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
16,
4357,
1994,
5497,
82,
58,
332,
16886,
17,
11907,
796,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
17,
4357,
1994,
5497,
82,
58,
332,
16886,
16,
11907,
796,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
2116,
13,
917,
1060,
276,
6624,
10352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
16,
4357,
1994,
5497,
82,
58,
332,
16886,
17,
11907,
796,
2116,
13,
324,
30482,
3976,
58,
332,
16886,
16,
7131,
332,
16886,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
370,
58,
2539,
5497,
82,
58,
332,
16886,
16,
4357,
1994,
5497,
82,
58,
332,
16886,
17,
11907,
796,
352,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
370,
628,
220,
220,
220,
825,
651,
3237,
37021,
5497,
1063,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
299,
32152,
7177,
286,
2546,
357,
22510,
7407,
3212,
2124,
362,
8,
286,
5743,
6376,
14729,
569,
13,
383,
340,
71,
198,
220,
220,
220,
220,
220,
220,
220,
5752,
286,
569,
11,
569,
58,
72,
11,
1058,
4357,
24866,
284,
281,
5743,
422,
569,
58,
72,
11,
657,
60,
284,
569,
58,
72,
11,
352,
4083,
383,
11188,
198,
220,
220,
220,
220,
220,
220,
220,
37423,
3891,
389,
1043,
1262,
651,
3237,
13414,
16886,
7390,
82,
22446,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
13015,
796,
299,
32152,
13,
9107,
418,
19510,
944,
13,
1136,
33111,
7407,
3212,
22784,
362,
828,
299,
32152,
13,
600,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
5497,
82,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
1312,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
329,
479,
287,
2116,
13,
1851,
1063,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
5497,
82,
58,
74,
60,
796,
1312,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
15853,
352,
628,
220,
220,
220,
220,
220,
220,
220,
1312,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
329,
37423,
16,
287,
2116,
13,
1851,
1063,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.166823 | 9,597 |
#!/usr/bin/env python3
#
# Copyright 2014 Simone Campagna
#
# 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.
#
__author__ = "Simone Campagna"
from .py23 import BASE_STRING
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] | 3.596774 | 186 |
from math import *
from decimal import *
getcontext().prec = 30
log_infinity = 9999
global_log2_eps = 0
## The root hermite factor delta of BKZ-b
## log_2 of best plausible Quantum Cost of SVP in dimension b
## log_2 of best plausible Quantum Cost of SVP in dimension b
## log_2 of best known Quantum Cost of SVP in dimension b
## Return the cost of the primal attack using m samples and blocksize b (infinity = fail)
## Return the cost of the dual attack using m samples and blocksize b (infinity = fail)
## Find optimal parameters for a given attack
## Create a report on the best primal and dual BKZ attacks
#print "### BCNS ###"
#summarize_params(2**32,1024,3.192, 2**32 /8)
#print
q = 12289
k = 24.0
print "### NewHope-512k24 ###"
summarize_params(q,512,sqrt(k/2), 3.*q/(4 * sqrt(2)), False)
print
q = 12289
k = 16.0
print "### NewHope-512k16 ###"
summarize_params(q,512,sqrt(k/2), 3.*q/(4 * sqrt(2)), False)
print
q = 12289
k = 8.0
error_tolerance = 3.*q/(4 * sqrt(2))
print "### NewHope-512k8 ###"
summarize_params(q,512,sqrt(k/2), error_tolerance, False)
print
print "### NewHope-1024k16 ###"
k = 16.0
#error_tolerance = 3.*q/4
error_tolerance = 4*(q/4)-3076.25
summarize_params(q,1024,sqrt(k/2), error_tolerance, False)
print
print "### NewHope-1024k8 ###"
k = 8.0
summarize_params(q,1024,sqrt(k/2), 3.*q/4, False)
print
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] | 2.615385 | 520 |
#!/usr/bin/env python3
import nibabel as nib
import nilearn
#from nilearn import masking
#from nilearn.plotting import plot_stat_map, plot_anat, plot_img, plot_epi
#from nilearn.image import concat_imgs, mean_img
#from nilearn.input_data import NiftiMasker
import numpy as np
import os
from typing import Union
import pandas as pd
from pandas import DataFrame as df
from os.path import basename as bname
from os.path import expanduser as xpu
from os.path import join as pjoin
from tqdm import tqdm
import loadutils as lu
from cimaqprep.fetch_events_behav import fetch_events_behav
from cimaqprep.fetch_scans_infos import fetch_scans_infos
from cimaqprep.fetch_participant import fetch_participant
from cimaqprep.get_tr_nscans_frametimes import get_tr_nscans_frametimes
#from cimaqprep.get_epi_mask_fromdata import get_epi_mask_fromdata
#from cimaqprep.resample_fmri_to_events import resample_fmri_to_events
class participant_data:
'''
Description
-----------
Fetch and load a single participant's data (scans, infos,
events and behavioural) in an object similar
to a sklearn.utils.Bunch object (the way nilearn datasets are fetched)
Parameters
----------
cimaq_nov_dir: Path to CIMA-Q november 2019 dataset
cimaq_mar_dir: Path to CIMA-Q march 2019 dataset
events_dir: Path to events (in-scan trials) directory
behav_dir: Path to behavioural (out-scan trials) directory
participants_path: Path to directory containing participant's
BIDS information and list of participant's for which
quality assessment was successful
Returns
-------
participant_data: object similar to a sklearn.utils.Bunch
object containing a single participant's data
(scans, infos, events and behavioural)
'''
# self.resample_fmri_to_events = resample_fmri_to_events
# self.common_masker_params = lu.read_json(self.masker_params_dir)
# self.nifti_masker_params = dict(mask_img=self.mar_epi_mask,
# runs=None,
# target_affine=self.mar_epi_mask.affine,
# target_shape=self.mar_epi_mask.shape,
# mask_strategy='epi',
# mask_args=None,
# sample_mask=None,
# reports=True,
# **self.common_masker_params)
# self.nifti_masker = \
# nilearn.input_data.NiftiMasker(**self.nifti_masker_params)
if __name__ == "__main__":
main()
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] | 2.170111 | 1,258 |
import pickle
from numpy.lib.function_base import append
import pandas as pd
import argparse
from torch.utils import data
from torch.utils.data import dataset
from src.tensorflow.model import TfModel
from config.tensorflow.text_config import Config
from src.pytorch.label_encoding import get_encoding
from src.tensorflow.dataloader import DatasetGeneratorTF
import numpy as np
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
if __name__=="__main__":
target, path=argp()
if target != "all":
column_list = [target]
else:
column_list = ["action", "object", "location"]
for column_name in column_list:
valid_data = pd.read_csv(path)
valid_data = get_encoding(valid_data, column_name)
vocab_list = vocab()
n_classes=len(list(set(valid_data[column_name])))
model = TfModel(input=vocab_list.__len__(), n_classes=n_classes).get_model()
model.load_weights(f"{Config.globals_['model_save_dir']}/{target}_{Config.globals_['version']}.ckpt")
pred_0, true_0 = perform_for_fold_test(valid_data, target, vocab_list, model)
print(f1_score(true_0, pred_0, average='micro'))
print(classification_report(true_0, pred_0)) | [
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62,
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7,
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62,
15,
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62,
15,
4008
] | 2.540161 | 498 |
from rest_framework.exceptions import ValidationError
from rest_framework.fields import UUIDField
from open.core.betterself.models.food import Food
from open.core.betterself.models.food_logs import FoodLog
from open.core.betterself.serializers.mixins import (
BaseCreateUpdateSerializer,
BaseModelReadSerializer,
)
from open.core.betterself.serializers.simple_generic_serializer import (
create_name_uuid_serializer,
)
from open.core.betterself.serializers.validators import ModelValidatorsMixin
| [
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628,
198
] | 3.384106 | 151 |
""" packet.py
"""
import abc
import binascii
import calendar
import copy
import hashlib
import os
import re
from datetime import datetime
import six
from cryptography.hazmat.primitives import constant_time
from cryptography.hazmat.primitives.asymmetric import padding
from .fields import DSAPriv, DSAPub, DSASignature
from .fields import ECDSAPub, ECDSAPriv, ECDSASignature
from .fields import ECDHPub, ECDHPriv, ECDHCipherText
from .fields import ElGCipherText, ElGPriv, ElGPub
from .fields import OpaquePubKey
from .fields import OpaquePrivKey
from .fields import RSACipherText, RSAPriv, RSAPub, RSASignature
from .fields import String2Key
from .fields import SubPackets
from .fields import UserAttributeSubPackets
from .types import Packet
from .types import Primary
from .types import Private
from .types import Public
from .types import Sub
from .types import VersionedPacket
from ..constants import CompressionAlgorithm
from ..constants import HashAlgorithm
from ..constants import PubKeyAlgorithm
from ..constants import SignatureType
from ..constants import SymmetricKeyAlgorithm
from ..constants import TrustFlags
from ..constants import TrustLevel
from ..decorators import sdproperty
from ..errors import PGPDecryptionError
from ..symenc import _decrypt
from ..symenc import _encrypt
from ..types import Fingerprint
__all__ = ['PKESessionKey',
'PKESessionKeyV3',
'Signature',
'SignatureV4',
'SKESessionKey',
'SKESessionKeyV4',
'OnePassSignature',
'OnePassSignatureV3',
'PrivKey',
'PubKey',
'PubKeyV4',
'PrivKeyV4',
'PrivSubKey',
'PrivSubKeyV4',
'CompressedData',
'SKEData',
'Marker',
'LiteralData',
'Trust',
'UserID',
'PubSubKey',
'PubSubKeyV4',
'UserAttribute',
'IntegrityProtectedSKEData',
'IntegrityProtectedSKEDataV1',
'MDC']
class PKESessionKeyV3(PKESessionKey):
"""
5.1. Public-Key Encrypted Session Key Packets (Tag 1)
A Public-Key Encrypted Session Key packet holds the session key used
to encrypt a message. Zero or more Public-Key Encrypted Session Key
packets and/or Symmetric-Key Encrypted Session Key packets may
precede a Symmetrically Encrypted Data Packet, which holds an
encrypted message. The message is encrypted with the session key,
and the session key is itself encrypted and stored in the Encrypted
Session Key packet(s). The Symmetrically Encrypted Data Packet is
preceded by one Public-Key Encrypted Session Key packet for each
OpenPGP key to which the message is encrypted. The recipient of the
message finds a session key that is encrypted to their public key,
decrypts the session key, and then uses the session key to decrypt
the message.
The body of this packet consists of:
- A one-octet number giving the version number of the packet type.
The currently defined value for packet version is 3.
- An eight-octet number that gives the Key ID of the public key to
which the session key is encrypted. If the session key is
encrypted to a subkey, then the Key ID of this subkey is used
here instead of the Key ID of the primary key.
- A one-octet number giving the public-key algorithm used.
- A string of octets that is the encrypted session key. This
string takes up the remainder of the packet, and its contents are
dependent on the public-key algorithm used.
Algorithm Specific Fields for RSA encryption
- multiprecision integer (MPI) of RSA encrypted value m**e mod n.
Algorithm Specific Fields for Elgamal encryption:
- MPI of Elgamal (Diffie-Hellman) value g**k mod p.
- MPI of Elgamal (Diffie-Hellman) value m * y**k mod p.
The value "m" in the above formulas is derived from the session key
as follows. First, the session key is prefixed with a one-octet
algorithm identifier that specifies the symmetric encryption
algorithm used to encrypt the following Symmetrically Encrypted Data
Packet. Then a two-octet checksum is appended, which is equal to the
sum of the preceding session key octets, not including the algorithm
identifier, modulo 65536. This value is then encoded as described in
PKCS#1 block encoding EME-PKCS1-v1_5 in Section 7.2.1 of [RFC3447] to
form the "m" value used in the formulas above. See Section 13.1 of
this document for notes on OpenPGP's use of PKCS#1.
Note that when an implementation forms several PKESKs with one
session key, forming a message that can be decrypted by several keys,
the implementation MUST make a new PKCS#1 encoding for each key.
An implementation MAY accept or use a Key ID of zero as a "wild card"
or "speculative" Key ID. In this case, the receiving implementation
would try all available private keys, checking for a valid decrypted
session key. This format helps reduce traffic analysis of messages.
"""
__ver__ = 3
@sdproperty
@encrypter.register(bytearray)
@sdproperty
@pkalg.register(int)
@pkalg.register(PubKeyAlgorithm)
class SignatureV4(Signature):
"""
5.2.3. Version 4 Signature Packet Format
The body of a version 4 Signature packet contains:
- One-octet version number (4).
- One-octet signature type.
- One-octet public-key algorithm.
- One-octet hash algorithm.
- Two-octet scalar octet count for following hashed subpacket data.
Note that this is the length in octets of all of the hashed
subpackets; a pointer incremented by this number will skip over
the hashed subpackets.
- Hashed subpacket data set (zero or more subpackets).
- Two-octet scalar octet count for the following unhashed subpacket
data. Note that this is the length in octets of all of the
unhashed subpackets; a pointer incremented by this number will
skip over the unhashed subpackets.
- Unhashed subpacket data set (zero or more subpackets).
- Two-octet field holding the left 16 bits of the signed hash
value.
- One or more multiprecision integers comprising the signature.
This portion is algorithm specific, as described above.
The concatenation of the data being signed and the signature data
from the version number through the hashed subpacket data (inclusive)
is hashed. The resulting hash value is what is signed. The left 16
bits of the hash are included in the Signature packet to provide a
quick test to reject some invalid signatures.
There are two fields consisting of Signature subpackets. The first
field is hashed with the rest of the signature data, while the second
is unhashed. The second set of subpackets is not cryptographically
protected by the signature and should include only advisory
information.
The algorithms for converting the hash function result to a signature
are described in a section below.
"""
__ver__ = 4
@sdproperty
@sigtype.register(int)
@sigtype.register(SignatureType)
@sdproperty
@pubalg.register(int)
@pubalg.register(PubKeyAlgorithm)
@sdproperty
@halg.register(int)
@halg.register(HashAlgorithm)
@property
@signature.setter
@property
class SKESessionKeyV4(SKESessionKey):
"""
5.3. Symmetric-Key Encrypted Session Key Packets (Tag 3)
The Symmetric-Key Encrypted Session Key packet holds the
symmetric-key encryption of a session key used to encrypt a message.
Zero or more Public-Key Encrypted Session Key packets and/or
Symmetric-Key Encrypted Session Key packets may precede a
Symmetrically Encrypted Data packet that holds an encrypted message.
The message is encrypted with a session key, and the session key is
itself encrypted and stored in the Encrypted Session Key packet or
the Symmetric-Key Encrypted Session Key packet.
If the Symmetrically Encrypted Data packet is preceded by one or
more Symmetric-Key Encrypted Session Key packets, each specifies a
passphrase that may be used to decrypt the message. This allows a
message to be encrypted to a number of public keys, and also to one
or more passphrases. This packet type is new and is not generated
by PGP 2.x or PGP 5.0.
The body of this packet consists of:
- A one-octet version number. The only currently defined version
is 4.
- A one-octet number describing the symmetric algorithm used.
- A string-to-key (S2K) specifier, length as defined above.
- Optionally, the encrypted session key itself, which is decrypted
with the string-to-key object.
If the encrypted session key is not present (which can be detected
on the basis of packet length and S2K specifier size), then the S2K
algorithm applied to the passphrase produces the session key for
decrypting the file, using the symmetric cipher algorithm from the
Symmetric-Key Encrypted Session Key packet.
If the encrypted session key is present, the result of applying the
S2K algorithm to the passphrase is used to decrypt just that
encrypted session key field, using CFB mode with an IV of all zeros.
The decryption result consists of a one-octet algorithm identifier
that specifies the symmetric-key encryption algorithm used to
encrypt the following Symmetrically Encrypted Data packet, followed
by the session key octets themselves.
Note: because an all-zero IV is used for this decryption, the S2K
specifier MUST use a salt value, either a Salted S2K or an
Iterated-Salted S2K. The salt value will ensure that the decryption
key is not repeated even if the passphrase is reused.
"""
__ver__ = 4
@property
class OnePassSignatureV3(OnePassSignature):
"""
5.4. One-Pass Signature Packets (Tag 4)
The One-Pass Signature packet precedes the signed data and contains
enough information to allow the receiver to begin calculating any
hashes needed to verify the signature. It allows the Signature
packet to be placed at the end of the message, so that the signer
can compute the entire signed message in one pass.
A One-Pass Signature does not interoperate with PGP 2.6.x or
earlier.
The body of this packet consists of:
- A one-octet version number. The current version is 3.
- A one-octet signature type. Signature types are described in
Section 5.2.1.
- A one-octet number describing the hash algorithm used.
- A one-octet number describing the public-key algorithm used.
- An eight-octet number holding the Key ID of the signing key.
- A one-octet number holding a flag showing whether the signature
is nested. A zero value indicates that the next packet is
another One-Pass Signature packet that describes another
signature to be applied to the same message data.
Note that if a message contains more than one one-pass signature,
then the Signature packets bracket the message; that is, the first
Signature packet after the message corresponds to the last one-pass
packet and the final Signature packet corresponds to the first
one-pass packet.
"""
__ver__ = 3
@sdproperty
@sigtype.register(int)
@sigtype.register(SignatureType)
@sdproperty
@pubalg.register(int)
@pubalg.register(PubKeyAlgorithm)
@sdproperty
@halg.register(int)
@halg.register(HashAlgorithm)
@sdproperty
@signer.register(str)
@signer.register(six.text_type)
@signer.register(bytearray)
class CompressedData(Packet):
"""
5.6. Compressed Data Packet (Tag 8)
The Compressed Data packet contains compressed data. Typically, this
packet is found as the contents of an encrypted packet, or following
a Signature or One-Pass Signature packet, and contains a literal data
packet.
The body of this packet consists of:
- One octet that gives the algorithm used to compress the packet.
- Compressed data, which makes up the remainder of the packet.
A Compressed Data Packet's body contains an block that compresses
some set of packets. See section "Packet Composition" for details on
how messages are formed.
ZIP-compressed packets are compressed with raw RFC 1951 [RFC1951]
DEFLATE blocks. Note that PGP V2.6 uses 13 bits of compression. If
an implementation uses more bits of compression, PGP V2.6 cannot
decompress it.
ZLIB-compressed packets are compressed with RFC 1950 [RFC1950] ZLIB-
style blocks.
BZip2-compressed packets are compressed using the BZip2 [BZ2]
algorithm.
"""
__typeid__ = 0x08
@sdproperty
@calg.register(int)
@calg.register(CompressionAlgorithm)
class SKEData(Packet):
"""
5.7. Symmetrically Encrypted Data Packet (Tag 9)
The Symmetrically Encrypted Data packet contains data encrypted with
a symmetric-key algorithm. When it has been decrypted, it contains
other packets (usually a literal data packet or compressed data
packet, but in theory other Symmetrically Encrypted Data packets or
sequences of packets that form whole OpenPGP messages).
The body of this packet consists of:
- Encrypted data, the output of the selected symmetric-key cipher
operating in OpenPGP's variant of Cipher Feedback (CFB) mode.
The symmetric cipher used may be specified in a Public-Key or
Symmetric-Key Encrypted Session Key packet that precedes the
Symmetrically Encrypted Data packet. In that case, the cipher
algorithm octet is prefixed to the session key before it is
encrypted. If no packets of these types precede the encrypted data,
the IDEA algorithm is used with the session key calculated as the MD5
hash of the passphrase, though this use is deprecated.
The data is encrypted in CFB mode, with a CFB shift size equal to the
cipher's block size. The Initial Vector (IV) is specified as all
zeros. Instead of using an IV, OpenPGP prefixes a string of length
equal to the block size of the cipher plus two to the data before it
is encrypted. The first block-size octets (for example, 8 octets for
a 64-bit block length) are random, and the following two octets are
copies of the last two octets of the IV. For example, in an 8-octet
block, octet 9 is a repeat of octet 7, and octet 10 is a repeat of
octet 8. In a cipher of length 16, octet 17 is a repeat of octet 15
and octet 18 is a repeat of octet 16. As a pedantic clarification,
in both these examples, we consider the first octet to be numbered 1.
After encrypting the first block-size-plus-two octets, the CFB state
is resynchronized. The last block-size octets of ciphertext are
passed through the cipher and the block boundary is reset.
The repetition of 16 bits in the random data prefixed to the message
allows the receiver to immediately check whether the session key is
incorrect. See the "Security Considerations" section for hints on
the proper use of this "quick check".
"""
__typeid__ = 0x09
class LiteralData(Packet):
"""
5.9. Literal Data Packet (Tag 11)
A Literal Data packet contains the body of a message; data that is
not to be further interpreted.
The body of this packet consists of:
- A one-octet field that describes how the data is formatted.
If it is a 'b' (0x62), then the Literal packet contains binary data.
If it is a 't' (0x74), then it contains text data, and thus may need
line ends converted to local form, or other text-mode changes. The
tag 'u' (0x75) means the same as 't', but also indicates that
implementation believes that the literal data contains UTF-8 text.
Early versions of PGP also defined a value of 'l' as a 'local' mode
for machine-local conversions. RFC 1991 [RFC1991] incorrectly stated
this local mode flag as '1' (ASCII numeral one). Both of these local
modes are deprecated.
- File name as a string (one-octet length, followed by a file
name). This may be a zero-length string. Commonly, if the
source of the encrypted data is a file, this will be the name of
the encrypted file. An implementation MAY consider the file name
in the Literal packet to be a more authoritative name than the
actual file name.
If the special name "_CONSOLE" is used, the message is considered to
be "for your eyes only". This advises that the message data is
unusually sensitive, and the receiving program should process it more
carefully, perhaps avoiding storing the received data to disk, for
example.
- A four-octet number that indicates a date associated with the
literal data. Commonly, the date might be the modification date
of a file, or the time the packet was created, or a zero that
indicates no specific time.
- The remainder of the packet is literal data.
Text data is stored with <CR><LF> text endings (i.e., network-
normal line endings). These should be converted to native line
endings by the receiving software.
"""
__typeid__ = 0x0B
@sdproperty
@mtime.register(datetime)
@mtime.register(int)
@mtime.register(bytes)
@mtime.register(bytearray)
@property
class Trust(Packet):
"""
5.10. Trust Packet (Tag 12)
The Trust packet is used only within keyrings and is not normally
exported. Trust packets contain data that record the user's
specifications of which key holders are trustworthy introducers,
along with other information that implementing software uses for
trust information. The format of Trust packets is defined by a given
implementation.
Trust packets SHOULD NOT be emitted to output streams that are
transferred to other users, and they SHOULD be ignored on any input
other than local keyring files.
"""
__typeid__ = 0x0C
@sdproperty
@trustlevel.register(int)
@trustlevel.register(TrustLevel)
@sdproperty
@trustflags.register(list)
@trustflags.register(int)
class UserID(Packet):
"""
5.11. User ID Packet (Tag 13)
A User ID packet consists of UTF-8 text that is intended to represent
the name and email address of the key holder. By convention, it
includes an RFC 2822 [RFC2822] mail name-addr, but there are no
restrictions on its content. The packet length in the header
specifies the length of the User ID.
"""
__typeid__ = 0x0D
class UserAttribute(Packet):
"""
5.12. User Attribute Packet (Tag 17)
The User Attribute packet is a variation of the User ID packet. It
is capable of storing more types of data than the User ID packet,
which is limited to text. Like the User ID packet, a User Attribute
packet may be certified by the key owner ("self-signed") or any other
key owner who cares to certify it. Except as noted, a User Attribute
packet may be used anywhere that a User ID packet may be used.
While User Attribute packets are not a required part of the OpenPGP
standard, implementations SHOULD provide at least enough
compatibility to properly handle a certification signature on the
User Attribute packet. A simple way to do this is by treating the
User Attribute packet as a User ID packet with opaque contents, but
an implementation may use any method desired.
The User Attribute packet is made up of one or more attribute
subpackets. Each subpacket consists of a subpacket header and a
body. The header consists of:
- the subpacket length (1, 2, or 5 octets)
- the subpacket type (1 octet)
and is followed by the subpacket specific data.
The only currently defined subpacket type is 1, signifying an image.
An implementation SHOULD ignore any subpacket of a type that it does
not recognize. Subpacket types 100 through 110 are reserved for
private or experimental use.
"""
__typeid__ = 0x11
@property
class IntegrityProtectedSKEDataV1(IntegrityProtectedSKEData):
"""
5.13. Sym. Encrypted Integrity Protected Data Packet (Tag 18)
The Symmetrically Encrypted Integrity Protected Data packet is a
variant of the Symmetrically Encrypted Data packet. It is a new
feature created for OpenPGP that addresses the problem of detecting a
modification to encrypted data. It is used in combination with a
Modification Detection Code packet.
There is a corresponding feature in the features Signature subpacket
that denotes that an implementation can properly use this packet
type. An implementation MUST support decrypting these packets and
SHOULD prefer generating them to the older Symmetrically Encrypted
Data packet when possible. Since this data packet protects against
modification attacks, this standard encourages its proliferation.
While blanket adoption of this data packet would create
interoperability problems, rapid adoption is nevertheless important.
An implementation SHOULD specifically denote support for this packet,
but it MAY infer it from other mechanisms.
For example, an implementation might infer from the use of a cipher
such as Advanced Encryption Standard (AES) or Twofish that a user
supports this feature. It might place in the unhashed portion of
another user's key signature a Features subpacket. It might also
present a user with an opportunity to regenerate their own self-
signature with a Features subpacket.
This packet contains data encrypted with a symmetric-key algorithm
and protected against modification by the SHA-1 hash algorithm. When
it has been decrypted, it will typically contain other packets (often
a Literal Data packet or Compressed Data packet). The last decrypted
packet in this packet's payload MUST be a Modification Detection Code
packet.
The body of this packet consists of:
- A one-octet version number. The only currently defined value is
1.
- Encrypted data, the output of the selected symmetric-key cipher
operating in Cipher Feedback mode with shift amount equal to the
block size of the cipher (CFB-n where n is the block size).
The symmetric cipher used MUST be specified in a Public-Key or
Symmetric-Key Encrypted Session Key packet that precedes the
Symmetrically Encrypted Data packet. In either case, the cipher
algorithm octet is prefixed to the session key before it is
encrypted.
The data is encrypted in CFB mode, with a CFB shift size equal to the
cipher's block size. The Initial Vector (IV) is specified as all
zeros. Instead of using an IV, OpenPGP prefixes an octet string to
the data before it is encrypted. The length of the octet string
equals the block size of the cipher in octets, plus two. The first
octets in the group, of length equal to the block size of the cipher,
are random; the last two octets are each copies of their 2nd
preceding octet. For example, with a cipher whose block size is 128
bits or 16 octets, the prefix data will contain 16 random octets,
then two more octets, which are copies of the 15th and 16th octets,
respectively. Unlike the Symmetrically Encrypted Data Packet, no
special CFB resynchronization is done after encrypting this prefix
data. See "OpenPGP CFB Mode" below for more details.
The repetition of 16 bits in the random data prefixed to the message
allows the receiver to immediately check whether the session key is
incorrect.
The plaintext of the data to be encrypted is passed through the SHA-1
hash function, and the result of the hash is appended to the
plaintext in a Modification Detection Code packet. The input to the
hash function includes the prefix data described above; it includes
all of the plaintext, and then also includes two octets of values
0xD3, 0x14. These represent the encoding of a Modification Detection
Code packet tag and length field of 20 octets.
The resulting hash value is stored in a Modification Detection Code
(MDC) packet, which MUST use the two octet encoding just given to
represent its tag and length field. The body of the MDC packet is
the 20-octet output of the SHA-1 hash.
The Modification Detection Code packet is appended to the plaintext
and encrypted along with the plaintext using the same CFB context.
During decryption, the plaintext data should be hashed with SHA-1,
including the prefix data as well as the packet tag and length field
of the Modification Detection Code packet. The body of the MDC
packet, upon decryption, is compared with the result of the SHA-1
hash.
Any failure of the MDC indicates that the message has been modified
and MUST be treated as a security problem. Failures include a
difference in the hash values, but also the absence of an MDC packet,
or an MDC packet in any position other than the end of the plaintext.
Any failure SHOULD be reported to the user.
Note: future designs of new versions of this packet should consider
rollback attacks since it will be possible for an attacker to change
the version back to 1.
"""
__ver__ = 1
class MDC(Packet):
"""
5.14. Modification Detection Code Packet (Tag 19)
The Modification Detection Code packet contains a SHA-1 hash of
plaintext data, which is used to detect message modification. It is
only used with a Symmetrically Encrypted Integrity Protected Data
packet. The Modification Detection Code packet MUST be the last
packet in the plaintext data that is encrypted in the Symmetrically
Encrypted Integrity Protected Data packet, and MUST appear in no
other place.
A Modification Detection Code packet MUST have a length of 20 octets.
The body of this packet consists of:
- A 20-octet SHA-1 hash of the preceding plaintext data of the
Symmetrically Encrypted Integrity Protected Data packet,
including prefix data, the tag octet, and length octet of the
Modification Detection Code packet.
Note that the Modification Detection Code packet MUST always use a
new format encoding of the packet tag, and a one-octet encoding of
the packet length. The reason for this is that the hashing rules for
modification detection include a one-octet tag and one-octet length
in the data hash. While this is a bit restrictive, it reduces
complexity.
"""
__typeid__ = 0x13
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407,
1390,
262,
11862,
198,
220,
220,
220,
27421,
11,
953,
43348,
45021,
2623,
13,
220,
770,
1988,
318,
788,
30240,
355,
3417,
287,
198,
220,
220,
220,
29673,
7902,
2,
16,
2512,
21004,
412,
11682,
12,
40492,
7902,
16,
12,
85,
16,
62,
20,
287,
7275,
767,
13,
17,
13,
16,
286,
685,
41150,
33535,
22,
60,
284,
198,
220,
220,
220,
1296,
262,
366,
76,
1,
1988,
973,
287,
262,
32126,
2029,
13,
220,
4091,
7275,
1511,
13,
16,
286,
198,
220,
220,
220,
428,
3188,
329,
4710,
319,
4946,
6968,
47,
338,
779,
286,
29673,
7902,
2,
16,
13,
628,
220,
220,
220,
5740,
326,
618,
281,
7822,
5107,
1811,
29673,
1546,
42,
82,
351,
530,
198,
220,
220,
220,
6246,
1994,
11,
14583,
257,
3275,
326,
460,
307,
875,
15109,
416,
1811,
8251,
11,
198,
220,
220,
220,
262,
7822,
17191,
787,
257,
649,
29673,
7902,
2,
16,
21004,
329,
1123,
1994,
13,
628,
220,
220,
220,
1052,
7822,
26720,
2453,
393,
779,
257,
7383,
4522,
286,
6632,
355,
257,
366,
21992,
2657,
1,
198,
220,
220,
220,
393,
366,
4125,
3129,
876,
1,
7383,
4522,
13,
220,
554,
428,
1339,
11,
262,
6464,
7822,
198,
220,
220,
220,
561,
1949,
477,
1695,
2839,
8251,
11,
10627,
329,
257,
4938,
875,
15109,
198,
220,
220,
220,
6246,
1994,
13,
220,
770,
5794,
5419,
4646,
4979,
3781,
286,
6218,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
332,
834,
796,
513,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
12685,
563,
42104,
13,
30238,
7,
1525,
83,
451,
2433,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
79,
74,
14016,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
79,
74,
14016,
13,
30238,
7,
14876,
9218,
2348,
42289,
8,
628,
198,
198,
4871,
34894,
53,
19,
7,
11712,
1300,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
17,
13,
18,
13,
220,
10628,
604,
34894,
6400,
316,
18980,
628,
220,
220,
220,
383,
1767,
286,
257,
2196,
604,
34894,
19638,
4909,
25,
628,
220,
220,
220,
220,
532,
1881,
12,
38441,
316,
2196,
1271,
357,
19,
737,
628,
220,
220,
220,
220,
532,
1881,
12,
38441,
316,
9877,
2099,
13,
628,
220,
220,
220,
220,
532,
1881,
12,
38441,
316,
1171,
12,
2539,
11862,
13,
628,
220,
220,
220,
220,
532,
1881,
12,
38441,
316,
12234,
11862,
13,
628,
220,
220,
220,
220,
532,
4930,
12,
38441,
316,
16578,
283,
19318,
316,
954,
329,
1708,
468,
704,
850,
8002,
316,
1366,
13,
198,
220,
220,
220,
220,
220,
220,
5740,
326,
428,
318,
262,
4129,
287,
19318,
1039,
286,
477,
286,
262,
468,
704,
198,
220,
220,
220,
220,
220,
220,
850,
8002,
1039,
26,
257,
17562,
1253,
12061,
416,
428,
1271,
481,
14267,
625,
198,
220,
220,
220,
220,
220,
220,
262,
468,
704,
850,
8002,
1039,
13,
628,
220,
220,
220,
220,
532,
367,
5263,
850,
8002,
316,
1366,
900,
357,
22570,
393,
517,
850,
8002,
1039,
737,
628,
220,
220,
220,
220,
532,
4930,
12,
38441,
316,
16578,
283,
19318,
316,
954,
329,
262,
1708,
14274,
5263,
850,
8002,
316,
198,
220,
220,
220,
220,
220,
220,
1366,
13,
220,
5740,
326,
428,
318,
262,
4129,
287,
19318,
1039,
286,
477,
286,
262,
198,
220,
220,
220,
220,
220,
220,
14274,
5263,
850,
8002,
1039,
26,
257,
17562,
1253,
12061,
416,
428,
1271,
481,
198,
220,
220,
220,
220,
220,
220,
14267,
625,
262,
14274,
5263,
850,
8002,
1039,
13,
628,
220,
220,
220,
220,
532,
791,
71,
5263,
850,
8002,
316,
1366,
900,
357,
22570,
393,
517,
850,
8002,
1039,
737,
628,
220,
220,
220,
220,
532,
4930,
12,
38441,
316,
2214,
4769,
262,
1364,
1467,
10340,
286,
262,
4488,
12234,
198,
220,
220,
220,
220,
220,
220,
1988,
13,
628,
220,
220,
220,
220,
532,
1881,
393,
517,
18540,
8344,
1166,
37014,
27918,
262,
9877,
13,
198,
220,
220,
220,
220,
220,
220,
770,
6903,
318,
11862,
2176,
11,
355,
3417,
2029,
13,
628,
220,
220,
220,
383,
1673,
36686,
341,
286,
262,
1366,
852,
4488,
290,
262,
9877,
1366,
198,
220,
220,
220,
422,
262,
2196,
1271,
832,
262,
468,
704,
850,
8002,
316,
1366,
357,
259,
5731,
8,
198,
220,
220,
220,
318,
468,
704,
13,
220,
383,
7186,
12234,
1988,
318,
644,
318,
4488,
13,
220,
383,
1364,
1467,
198,
220,
220,
220,
10340,
286,
262,
12234,
389,
3017,
287,
262,
34894,
19638,
284,
2148,
257,
198,
220,
220,
220,
2068,
1332,
284,
4968,
617,
12515,
17239,
13,
628,
220,
220,
220,
1318,
389,
734,
7032,
17747,
286,
34894,
850,
8002,
1039,
13,
220,
383,
717,
198,
220,
220,
220,
2214,
318,
468,
704,
351,
262,
1334,
286,
262,
9877,
1366,
11,
981,
262,
1218,
198,
220,
220,
220,
318,
14274,
5263,
13,
220,
383,
1218,
900,
286,
850,
8002,
1039,
318,
407,
8194,
33145,
198,
220,
220,
220,
6861,
416,
262,
9877,
290,
815,
2291,
691,
20852,
198,
220,
220,
220,
1321,
13,
628,
220,
220,
220,
383,
16113,
329,
23202,
262,
12234,
2163,
1255,
284,
257,
9877,
198,
220,
220,
220,
389,
3417,
287,
257,
2665,
2174,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
332,
834,
796,
604,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
82,
328,
4906,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
82,
328,
4906,
13,
30238,
7,
11712,
1300,
6030,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
12984,
14016,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
12984,
14016,
13,
30238,
7,
14876,
9218,
2348,
42289,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
71,
14016,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
71,
14016,
13,
30238,
7,
26257,
2348,
42289,
8,
628,
220,
220,
220,
2488,
26745,
628,
220,
220,
220,
2488,
12683,
1300,
13,
2617,
353,
628,
220,
220,
220,
2488,
26745,
628,
198,
198,
4871,
14277,
1546,
2521,
9218,
53,
19,
7,
18831,
1546,
2521,
9218,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
18,
13,
220,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
6400,
1039,
357,
24835,
513,
8,
628,
220,
220,
220,
383,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
19638,
6622,
262,
198,
220,
220,
220,
23606,
19482,
12,
2539,
15835,
286,
257,
6246,
1994,
973,
284,
34117,
257,
3275,
13,
198,
220,
220,
220,
12169,
393,
517,
5094,
12,
9218,
14711,
15109,
23575,
7383,
24624,
290,
14,
273,
198,
220,
220,
220,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
24624,
743,
8555,
68,
257,
198,
220,
220,
220,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
326,
6622,
281,
19365,
3275,
13,
198,
220,
220,
220,
383,
3275,
318,
19365,
351,
257,
6246,
1994,
11,
290,
262,
6246,
1994,
318,
198,
220,
220,
220,
2346,
19365,
290,
8574,
287,
262,
14711,
15109,
23575,
7383,
19638,
393,
198,
220,
220,
220,
262,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
19638,
13,
628,
220,
220,
220,
1002,
262,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
318,
27165,
416,
530,
393,
198,
220,
220,
220,
517,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
24624,
11,
1123,
26052,
257,
198,
220,
220,
220,
1208,
34675,
326,
743,
307,
973,
284,
42797,
262,
3275,
13,
220,
770,
3578,
257,
198,
220,
220,
220,
3275,
284,
307,
19365,
284,
257,
1271,
286,
1171,
8251,
11,
290,
635,
284,
530,
198,
220,
220,
220,
393,
517,
1208,
746,
81,
1386,
13,
220,
770,
19638,
2099,
318,
649,
290,
318,
407,
7560,
198,
220,
220,
220,
416,
350,
16960,
362,
13,
87,
393,
350,
16960,
642,
13,
15,
13,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
2196,
1271,
13,
220,
383,
691,
3058,
5447,
2196,
198,
220,
220,
220,
220,
220,
220,
318,
604,
13,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
1271,
12059,
262,
23606,
19482,
11862,
973,
13,
628,
220,
220,
220,
220,
532,
317,
4731,
12,
1462,
12,
2539,
357,
50,
17,
42,
8,
1020,
7483,
11,
4129,
355,
5447,
2029,
13,
628,
220,
220,
220,
220,
532,
16018,
453,
11,
262,
19365,
6246,
1994,
2346,
11,
543,
318,
875,
15109,
198,
220,
220,
220,
220,
220,
220,
351,
262,
4731,
12,
1462,
12,
2539,
2134,
13,
628,
220,
220,
220,
1002,
262,
19365,
6246,
1994,
318,
407,
1944,
357,
4758,
460,
307,
12326,
198,
220,
220,
220,
319,
262,
4308,
286,
19638,
4129,
290,
311,
17,
42,
1020,
7483,
2546,
828,
788,
262,
311,
17,
42,
198,
220,
220,
220,
11862,
5625,
284,
262,
1208,
34675,
11073,
262,
6246,
1994,
329,
198,
220,
220,
220,
42797,
278,
262,
2393,
11,
1262,
262,
23606,
19482,
38012,
11862,
422,
262,
198,
220,
220,
220,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
19638,
13,
628,
220,
220,
220,
1002,
262,
19365,
6246,
1994,
318,
1944,
11,
262,
1255,
286,
11524,
262,
198,
220,
220,
220,
311,
17,
42,
11862,
284,
262,
1208,
34675,
318,
973,
284,
42797,
655,
326,
198,
220,
220,
220,
19365,
6246,
1994,
2214,
11,
1262,
18551,
33,
4235,
351,
281,
8363,
286,
477,
1976,
27498,
13,
198,
220,
220,
220,
383,
875,
13168,
1255,
10874,
286,
257,
530,
12,
38441,
316,
11862,
27421,
198,
220,
220,
220,
326,
26052,
262,
23606,
19482,
12,
2539,
15835,
11862,
973,
284,
198,
220,
220,
220,
34117,
262,
1708,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
11,
3940,
198,
220,
220,
220,
416,
262,
6246,
1994,
19318,
1039,
2405,
13,
628,
220,
220,
220,
5740,
25,
780,
281,
477,
12,
22570,
8363,
318,
973,
329,
428,
875,
13168,
11,
262,
311,
17,
42,
198,
220,
220,
220,
1020,
7483,
17191,
779,
257,
8268,
1988,
11,
2035,
257,
4849,
1513,
311,
17,
42,
393,
281,
198,
220,
220,
220,
40806,
515,
12,
19221,
1513,
311,
17,
42,
13,
220,
383,
8268,
1988,
481,
4155,
326,
262,
875,
13168,
198,
220,
220,
220,
1994,
318,
407,
5100,
772,
611,
262,
1208,
34675,
318,
46823,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
332,
834,
796,
604,
628,
220,
220,
220,
2488,
26745,
628,
198,
198,
4871,
1881,
14478,
11712,
1300,
53,
18,
7,
3198,
14478,
11712,
1300,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
19,
13,
220,
1881,
12,
14478,
34894,
6400,
1039,
357,
24835,
604,
8,
628,
220,
220,
220,
383,
1881,
12,
14478,
34894,
19638,
8555,
274,
262,
4488,
1366,
290,
4909,
198,
220,
220,
220,
1576,
1321,
284,
1249,
262,
9733,
284,
2221,
26019,
597,
198,
220,
220,
220,
46621,
2622,
284,
11767,
262,
9877,
13,
220,
632,
3578,
262,
34894,
198,
220,
220,
220,
19638,
284,
307,
4624,
379,
262,
886,
286,
262,
3275,
11,
523,
326,
262,
1051,
263,
198,
220,
220,
220,
460,
24061,
262,
2104,
4488,
3275,
287,
530,
1208,
13,
628,
220,
220,
220,
317,
1881,
12,
14478,
34894,
857,
407,
48817,
378,
351,
350,
16960,
362,
13,
21,
13,
87,
393,
198,
220,
220,
220,
2961,
13,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
2196,
1271,
13,
220,
383,
1459,
2196,
318,
513,
13,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
9877,
2099,
13,
220,
34894,
3858,
389,
3417,
287,
198,
220,
220,
220,
220,
220,
220,
7275,
642,
13,
17,
13,
16,
13,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
1271,
12059,
262,
12234,
11862,
973,
13,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
1271,
12059,
262,
1171,
12,
2539,
11862,
973,
13,
628,
220,
220,
220,
220,
532,
1052,
3624,
12,
38441,
316,
1271,
4769,
262,
7383,
4522,
286,
262,
8415,
1994,
13,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
1271,
4769,
257,
6056,
4478,
1771,
262,
9877,
198,
220,
220,
220,
220,
220,
220,
318,
28376,
13,
220,
317,
6632,
1988,
9217,
326,
262,
1306,
19638,
318,
198,
220,
220,
220,
220,
220,
220,
1194,
1881,
12,
14478,
34894,
19638,
326,
8477,
1194,
198,
220,
220,
220,
220,
220,
220,
9877,
284,
307,
5625,
284,
262,
976,
3275,
1366,
13,
628,
220,
220,
220,
5740,
326,
611,
257,
3275,
4909,
517,
621,
530,
530,
12,
6603,
9877,
11,
198,
220,
220,
220,
788,
262,
34894,
24624,
19096,
262,
3275,
26,
326,
318,
11,
262,
717,
198,
220,
220,
220,
34894,
19638,
706,
262,
3275,
24866,
284,
262,
938,
530,
12,
6603,
198,
220,
220,
220,
19638,
290,
262,
2457,
34894,
19638,
24866,
284,
262,
717,
198,
220,
220,
220,
530,
12,
6603,
19638,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
332,
834,
796,
513,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
82,
328,
4906,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
82,
328,
4906,
13,
30238,
7,
11712,
1300,
6030,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
12984,
14016,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
12984,
14016,
13,
30238,
7,
14876,
9218,
2348,
42289,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
71,
14016,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
71,
14016,
13,
30238,
7,
26257,
2348,
42289,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
12683,
263,
13,
30238,
7,
2536,
8,
198,
220,
220,
220,
2488,
12683,
263,
13,
30238,
7,
19412,
13,
5239,
62,
4906,
8,
628,
220,
220,
220,
2488,
12683,
263,
13,
30238,
7,
1525,
83,
451,
2433,
8,
628,
628,
628,
628,
198,
4871,
3082,
2790,
6601,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
21,
13,
220,
3082,
2790,
6060,
6400,
316,
357,
24835,
807,
8,
628,
220,
220,
220,
383,
3082,
2790,
6060,
19638,
4909,
25388,
1366,
13,
220,
27095,
11,
428,
198,
220,
220,
220,
19638,
318,
1043,
355,
262,
10154,
286,
281,
19365,
19638,
11,
393,
1708,
198,
220,
220,
220,
257,
34894,
393,
1881,
12,
14478,
34894,
19638,
11,
290,
4909,
257,
18875,
1366,
198,
220,
220,
220,
19638,
13,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
1881,
19318,
316,
326,
3607,
262,
11862,
973,
284,
27413,
262,
19638,
13,
628,
220,
220,
220,
220,
532,
3082,
2790,
1366,
11,
543,
1838,
510,
262,
17675,
286,
262,
19638,
13,
628,
220,
220,
220,
317,
3082,
2790,
6060,
6400,
316,
338,
1767,
4909,
281,
2512,
326,
552,
16746,
198,
220,
220,
220,
617,
900,
286,
24624,
13,
220,
4091,
2665,
366,
47,
8317,
955,
9150,
1,
329,
3307,
319,
198,
220,
220,
220,
703,
6218,
389,
7042,
13,
628,
220,
220,
220,
42977,
12,
5589,
2790,
24624,
389,
25388,
351,
8246,
30978,
27937,
685,
41150,
1129,
4349,
60,
198,
220,
220,
220,
5550,
3697,
6158,
7021,
13,
220,
5740,
326,
350,
16960,
569,
17,
13,
21,
3544,
1511,
10340,
286,
19794,
13,
220,
1002,
198,
220,
220,
220,
281,
7822,
3544,
517,
10340,
286,
19794,
11,
350,
16960,
569,
17,
13,
21,
2314,
198,
220,
220,
220,
38237,
601,
340,
13,
628,
220,
220,
220,
1168,
40347,
12,
5589,
2790,
24624,
389,
25388,
351,
30978,
11445,
685,
41150,
42751,
60,
1168,
40347,
12,
198,
220,
220,
220,
3918,
7021,
13,
628,
220,
220,
220,
347,
41729,
17,
12,
5589,
2790,
24624,
389,
25388,
1262,
262,
347,
41729,
17,
685,
33,
57,
17,
60,
198,
220,
220,
220,
11862,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
2919,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
9948,
70,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
9948,
70,
13,
30238,
7,
7293,
2234,
2348,
42289,
8,
628,
198,
4871,
14277,
1961,
1045,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
22,
13,
220,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
6400,
316,
357,
24835,
860,
8,
628,
220,
220,
220,
383,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
4909,
1366,
19365,
351,
198,
220,
220,
220,
257,
23606,
19482,
12,
2539,
11862,
13,
220,
1649,
340,
468,
587,
875,
15109,
11,
340,
4909,
198,
220,
220,
220,
584,
24624,
357,
23073,
257,
18875,
1366,
19638,
393,
25388,
1366,
198,
220,
220,
220,
19638,
11,
475,
287,
4583,
584,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
24624,
393,
198,
220,
220,
220,
16311,
286,
24624,
326,
1296,
2187,
4946,
6968,
47,
6218,
737,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
14711,
15109,
1366,
11,
262,
5072,
286,
262,
6163,
23606,
19482,
12,
2539,
38012,
198,
220,
220,
220,
220,
220,
220,
5361,
287,
4946,
6968,
47,
338,
15304,
286,
44334,
37774,
357,
22495,
33,
8,
4235,
13,
628,
220,
220,
220,
383,
23606,
19482,
38012,
973,
743,
307,
7368,
287,
257,
5094,
12,
9218,
393,
198,
220,
220,
220,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
19638,
326,
8555,
274,
262,
198,
220,
220,
220,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
13,
220,
554,
326,
1339,
11,
262,
38012,
198,
220,
220,
220,
11862,
19318,
316,
318,
7694,
2966,
284,
262,
6246,
1994,
878,
340,
318,
198,
220,
220,
220,
19365,
13,
220,
1002,
645,
24624,
286,
777,
3858,
8555,
68,
262,
19365,
1366,
11,
198,
220,
220,
220,
262,
4522,
16412,
11862,
318,
973,
351,
262,
6246,
1994,
10488,
355,
262,
10670,
20,
198,
220,
220,
220,
12234,
286,
262,
1208,
34675,
11,
996,
428,
779,
318,
39224,
13,
628,
220,
220,
220,
383,
1366,
318,
19365,
287,
18551,
33,
4235,
11,
351,
257,
18551,
33,
6482,
2546,
4961,
284,
262,
198,
220,
220,
220,
38012,
338,
2512,
2546,
13,
220,
383,
20768,
20650,
357,
3824,
8,
318,
7368,
355,
477,
198,
220,
220,
220,
1976,
27498,
13,
220,
5455,
286,
1262,
281,
8363,
11,
4946,
6968,
47,
21231,
274,
257,
4731,
286,
4129,
198,
220,
220,
220,
4961,
284,
262,
2512,
2546,
286,
262,
38012,
5556,
734,
284,
262,
1366,
878,
340,
198,
220,
220,
220,
318,
19365,
13,
220,
383,
717,
2512,
12,
7857,
19318,
1039,
357,
1640,
1672,
11,
807,
19318,
1039,
329,
198,
220,
220,
220,
257,
5598,
12,
2545,
2512,
4129,
8,
389,
4738,
11,
290,
262,
1708,
734,
19318,
1039,
389,
198,
220,
220,
220,
9088,
286,
262,
938,
734,
19318,
1039,
286,
262,
8363,
13,
220,
1114,
1672,
11,
287,
281,
807,
12,
38441,
316,
198,
220,
220,
220,
2512,
11,
19318,
316,
860,
318,
257,
9585,
286,
19318,
316,
767,
11,
290,
19318,
316,
838,
318,
257,
9585,
286,
198,
220,
220,
220,
19318,
316,
807,
13,
220,
554,
257,
38012,
286,
4129,
1467,
11,
19318,
316,
1596,
318,
257,
9585,
286,
19318,
316,
1315,
198,
220,
220,
220,
290,
19318,
316,
1248,
318,
257,
9585,
286,
19318,
316,
1467,
13,
220,
1081,
257,
7190,
5109,
31321,
11,
198,
220,
220,
220,
287,
1111,
777,
6096,
11,
356,
2074,
262,
717,
19318,
316,
284,
307,
25840,
352,
13,
628,
220,
220,
220,
2293,
34117,
278,
262,
717,
2512,
12,
7857,
12,
9541,
12,
11545,
19318,
1039,
11,
262,
18551,
33,
1181,
198,
220,
220,
220,
318,
581,
24871,
1143,
13,
220,
383,
938,
2512,
12,
7857,
19318,
1039,
286,
38012,
5239,
389,
198,
220,
220,
220,
3804,
832,
262,
38012,
290,
262,
2512,
18645,
318,
13259,
13,
628,
220,
220,
220,
383,
29693,
286,
1467,
10340,
287,
262,
4738,
1366,
7694,
2966,
284,
262,
3275,
198,
220,
220,
220,
3578,
262,
9733,
284,
3393,
2198,
1771,
262,
6246,
1994,
318,
198,
220,
220,
220,
11491,
13,
220,
4091,
262,
366,
24074,
12642,
602,
1,
2665,
329,
20269,
319,
198,
220,
220,
220,
262,
1774,
779,
286,
428,
366,
24209,
2198,
1911,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
2931,
628,
198,
198,
4871,
25659,
1691,
6601,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
24,
13,
220,
25659,
1691,
6060,
6400,
316,
357,
24835,
1367,
8,
628,
220,
220,
220,
317,
25659,
1691,
6060,
19638,
4909,
262,
1767,
286,
257,
3275,
26,
1366,
326,
318,
198,
220,
220,
220,
407,
284,
307,
2252,
16173,
13,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
2214,
326,
8477,
703,
262,
1366,
318,
39559,
13,
628,
220,
220,
220,
1002,
340,
318,
257,
705,
65,
6,
357,
15,
87,
5237,
828,
788,
262,
25659,
1691,
19638,
4909,
13934,
1366,
13,
198,
220,
220,
220,
1002,
340,
318,
257,
705,
83,
6,
357,
15,
87,
4524,
828,
788,
340,
4909,
2420,
1366,
11,
290,
4145,
743,
761,
198,
220,
220,
220,
1627,
5645,
11513,
284,
1957,
1296,
11,
393,
584,
2420,
12,
14171,
2458,
13,
220,
383,
198,
220,
220,
220,
7621,
705,
84,
6,
357,
15,
87,
2425,
8,
1724,
262,
976,
355,
705,
83,
3256,
475,
635,
9217,
326,
198,
220,
220,
220,
7822,
5804,
326,
262,
18875,
1366,
4909,
41002,
12,
23,
2420,
13,
628,
220,
220,
220,
12556,
6300,
286,
350,
16960,
635,
5447,
257,
1988,
286,
705,
75,
6,
355,
257,
705,
12001,
6,
4235,
198,
220,
220,
220,
329,
4572,
12,
12001,
32626,
13,
220,
30978,
10249,
685,
41150,
24529,
60,
23175,
5081,
198,
220,
220,
220,
428,
1957,
4235,
6056,
355,
705,
16,
6,
357,
42643,
3978,
997,
1691,
530,
737,
220,
5747,
286,
777,
1957,
198,
220,
220,
220,
12881,
389,
39224,
13,
628,
220,
220,
220,
220,
532,
9220,
1438,
355,
257,
4731,
357,
505,
12,
38441,
316,
4129,
11,
3940,
416,
257,
2393,
198,
220,
220,
220,
220,
220,
220,
1438,
737,
220,
770,
743,
307,
257,
6632,
12,
13664,
4731,
13,
220,
8070,
306,
11,
611,
262,
198,
220,
220,
220,
220,
220,
220,
2723,
286,
262,
19365,
1366,
318,
257,
2393,
11,
428,
481,
307,
262,
1438,
286,
198,
220,
220,
220,
220,
220,
220,
262,
19365,
2393,
13,
220,
1052,
7822,
26720,
2074,
262,
2393,
1438,
198,
220,
220,
220,
220,
220,
220,
287,
262,
25659,
1691,
19638,
284,
307,
257,
517,
32042,
1438,
621,
262,
198,
220,
220,
220,
220,
220,
220,
4036,
2393,
1438,
13,
628,
220,
220,
220,
1002,
262,
2041,
1438,
45434,
10943,
15821,
2538,
1,
318,
973,
11,
262,
3275,
318,
3177,
284,
198,
220,
220,
220,
307,
366,
1640,
534,
2951,
691,
1911,
220,
770,
34138,
326,
262,
3275,
1366,
318,
198,
220,
220,
220,
23708,
8564,
11,
290,
262,
6464,
1430,
815,
1429,
340,
517,
198,
220,
220,
220,
7773,
11,
3737,
14928,
23069,
262,
2722,
1366,
284,
11898,
11,
329,
198,
220,
220,
220,
1672,
13,
628,
220,
220,
220,
220,
532,
317,
1440,
12,
38441,
316,
1271,
326,
9217,
257,
3128,
3917,
351,
262,
198,
220,
220,
220,
220,
220,
220,
18875,
1366,
13,
220,
8070,
306,
11,
262,
3128,
1244,
307,
262,
17613,
3128,
198,
220,
220,
220,
220,
220,
220,
286,
257,
2393,
11,
393,
262,
640,
262,
19638,
373,
2727,
11,
393,
257,
6632,
326,
198,
220,
220,
220,
220,
220,
220,
9217,
645,
2176,
640,
13,
628,
220,
220,
220,
220,
532,
383,
17675,
286,
262,
19638,
318,
18875,
1366,
13,
628,
220,
220,
220,
220,
220,
220,
8255,
1366,
318,
8574,
351,
1279,
9419,
6927,
43,
37,
29,
2420,
38168,
357,
72,
13,
68,
1539,
3127,
12,
198,
220,
220,
220,
220,
220,
220,
3487,
1627,
38168,
737,
220,
2312,
815,
307,
11513,
284,
6868,
1627,
198,
220,
220,
220,
220,
220,
220,
38168,
416,
262,
6464,
3788,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
15,
33,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
76,
2435,
13,
30238,
7,
19608,
8079,
8,
628,
220,
220,
220,
2488,
76,
2435,
13,
30238,
7,
600,
8,
628,
220,
220,
220,
2488,
76,
2435,
13,
30238,
7,
33661,
8,
198,
220,
220,
220,
2488,
76,
2435,
13,
30238,
7,
1525,
83,
451,
2433,
8,
628,
220,
220,
220,
2488,
26745,
628,
198,
4871,
9870,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
940,
13,
220,
9870,
6400,
316,
357,
24835,
1105,
8,
628,
220,
220,
220,
383,
9870,
19638,
318,
973,
691,
1626,
1994,
33173,
290,
318,
407,
7685,
198,
220,
220,
220,
29050,
13,
220,
9870,
24624,
3994,
1366,
326,
1700,
262,
2836,
338,
198,
220,
220,
220,
20640,
286,
543,
1994,
16392,
389,
34412,
3120,
7999,
11,
198,
220,
220,
220,
1863,
351,
584,
1321,
326,
15427,
3788,
3544,
329,
198,
220,
220,
220,
3774,
1321,
13,
220,
383,
5794,
286,
9870,
24624,
318,
5447,
416,
257,
1813,
198,
220,
220,
220,
7822,
13,
628,
220,
220,
220,
9870,
24624,
40312,
5626,
307,
31234,
284,
5072,
15190,
326,
389,
198,
220,
220,
220,
11172,
284,
584,
2985,
11,
290,
484,
40312,
307,
9514,
319,
597,
5128,
198,
220,
220,
220,
584,
621,
1957,
1994,
1806,
3696,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
15,
34,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
38087,
5715,
13,
30238,
7,
600,
8,
198,
220,
220,
220,
2488,
38087,
5715,
13,
30238,
7,
33814,
4971,
8,
628,
220,
220,
220,
2488,
21282,
26745,
628,
220,
220,
220,
2488,
38087,
33152,
13,
30238,
7,
4868,
8,
628,
220,
220,
220,
2488,
38087,
33152,
13,
30238,
7,
600,
8,
628,
198,
4871,
11787,
2389,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
1157,
13,
220,
11787,
4522,
6400,
316,
357,
24835,
1511,
8,
628,
220,
220,
220,
317,
11787,
4522,
19638,
10874,
286,
41002,
12,
23,
2420,
326,
318,
5292,
284,
2380,
198,
220,
220,
220,
262,
1438,
290,
3053,
2209,
286,
262,
1994,
15762,
13,
220,
2750,
9831,
11,
340,
198,
220,
220,
220,
3407,
281,
30978,
2579,
1828,
685,
41150,
2078,
1828,
60,
6920,
1438,
12,
29851,
11,
475,
612,
389,
645,
198,
220,
220,
220,
8733,
319,
663,
2695,
13,
220,
383,
19638,
4129,
287,
262,
13639,
198,
220,
220,
220,
26052,
262,
4129,
286,
262,
11787,
4522,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
15,
35,
628,
628,
198,
4871,
11787,
33682,
7,
47,
8317,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
1065,
13,
220,
11787,
3460,
4163,
6400,
316,
357,
24835,
1596,
8,
628,
220,
220,
220,
383,
11787,
3460,
4163,
19638,
318,
257,
12291,
286,
262,
11787,
4522,
19638,
13,
220,
632,
198,
220,
220,
220,
318,
6007,
286,
23069,
517,
3858,
286,
1366,
621,
262,
11787,
4522,
19638,
11,
198,
220,
220,
220,
543,
318,
3614,
284,
2420,
13,
220,
4525,
262,
11787,
4522,
19638,
11,
257,
11787,
3460,
4163,
198,
220,
220,
220,
19638,
743,
307,
15704,
416,
262,
1994,
4870,
5855,
944,
12,
32696,
4943,
393,
597,
584,
198,
220,
220,
220,
1994,
4870,
508,
16609,
284,
46125,
340,
13,
220,
18181,
355,
4367,
11,
257,
11787,
3460,
4163,
198,
220,
220,
220,
19638,
743,
307,
973,
6609,
326,
257,
11787,
4522,
19638,
743,
307,
973,
13,
628,
220,
220,
220,
2893,
11787,
3460,
4163,
24624,
389,
407,
257,
2672,
636,
286,
262,
4946,
6968,
47,
198,
220,
220,
220,
3210,
11,
25504,
40312,
2148,
379,
1551,
1576,
198,
220,
220,
220,
17764,
284,
6105,
5412,
257,
18094,
9877,
319,
262,
198,
220,
220,
220,
11787,
3460,
4163,
19638,
13,
220,
317,
2829,
835,
284,
466,
428,
318,
416,
13622,
262,
198,
220,
220,
220,
11787,
3460,
4163,
19638,
355,
257,
11787,
4522,
19638,
351,
32191,
10154,
11,
475,
198,
220,
220,
220,
281,
7822,
743,
779,
597,
2446,
10348,
13,
628,
220,
220,
220,
383,
11787,
3460,
4163,
19638,
318,
925,
510,
286,
530,
393,
517,
11688,
198,
220,
220,
220,
850,
8002,
1039,
13,
220,
5501,
850,
8002,
316,
10874,
286,
257,
850,
8002,
316,
13639,
290,
257,
198,
220,
220,
220,
1767,
13,
220,
383,
13639,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
262,
850,
8002,
316,
4129,
357,
16,
11,
362,
11,
393,
642,
19318,
1039,
8,
628,
220,
220,
220,
220,
532,
262,
850,
8002,
316,
2099,
357,
16,
19318,
316,
8,
628,
220,
220,
220,
290,
318,
3940,
416,
262,
850,
8002,
316,
2176,
1366,
13,
628,
220,
220,
220,
383,
691,
3058,
5447,
850,
8002,
316,
2099,
318,
352,
11,
1051,
4035,
281,
2939,
13,
198,
220,
220,
220,
1052,
7822,
40312,
8856,
597,
850,
8002,
316,
286,
257,
2099,
326,
340,
857,
198,
220,
220,
220,
407,
7564,
13,
220,
3834,
8002,
316,
3858,
1802,
832,
9796,
389,
10395,
329,
198,
220,
220,
220,
2839,
393,
11992,
779,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
11593,
4906,
312,
834,
796,
657,
87,
1157,
628,
220,
220,
220,
2488,
26745,
628,
198,
198,
4871,
39348,
19703,
11197,
18831,
1961,
1045,
53,
16,
7,
34500,
10138,
19703,
11197,
18831,
1961,
1045,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
642,
13,
1485,
13,
220,
15845,
13,
14711,
15109,
39348,
5038,
11197,
6060,
6400,
316,
357,
24835,
1248,
8,
628,
220,
220,
220,
383,
1632,
3020,
21879,
1146,
14711,
15109,
39348,
5038,
11197,
6060,
19638,
318,
257,
198,
220,
220,
220,
15304,
286,
262,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
13,
220,
632,
318,
257,
649,
198,
220,
220,
220,
3895,
2727,
329,
4946,
6968,
47,
326,
9405,
262,
1917,
286,
31521,
257,
198,
220,
220,
220,
17613,
284,
19365,
1366,
13,
220,
632,
318,
973,
287,
6087,
351,
257,
198,
220,
220,
220,
3401,
2649,
46254,
6127,
19638,
13,
628,
220,
220,
220,
1318,
318,
257,
11188,
3895,
287,
262,
3033,
34894,
850,
8002,
316,
198,
220,
220,
220,
326,
43397,
326,
281,
7822,
460,
6105,
779,
428,
19638,
198,
220,
220,
220,
2099,
13,
220,
1052,
7822,
17191,
1104,
42797,
278,
777,
24624,
290,
198,
220,
220,
220,
40312,
4702,
15453,
606,
284,
262,
4697,
1632,
3020,
21879,
1146,
14711,
15109,
198,
220,
220,
220,
6060,
19638,
618,
1744,
13,
220,
4619,
428,
1366,
19638,
17289,
1028,
198,
220,
220,
220,
17613,
3434,
11,
428,
3210,
18656,
663,
25068,
13,
198,
220,
220,
220,
2893,
18447,
12695,
286,
428,
1366,
19638,
561,
2251,
198,
220,
220,
220,
48817,
1799,
2761,
11,
5801,
12695,
318,
19032,
1593,
13,
198,
220,
220,
220,
1052,
7822,
40312,
5734,
42685,
1104,
329,
428,
19638,
11,
198,
220,
220,
220,
475,
340,
26720,
13249,
340,
422,
584,
11701,
13,
628,
220,
220,
220,
1114,
1672,
11,
281,
7822,
1244,
13249,
422,
262,
779,
286,
257,
38012,
198,
220,
220,
220,
884,
355,
13435,
14711,
13168,
8997,
357,
32,
1546,
8,
393,
1815,
1659,
680,
326,
257,
2836,
198,
220,
220,
220,
6971,
428,
3895,
13,
220,
632,
1244,
1295,
287,
262,
14274,
5263,
6903,
286,
198,
220,
220,
220,
1194,
2836,
338,
1994,
9877,
257,
17571,
850,
8002,
316,
13,
220,
632,
1244,
635,
198,
220,
220,
220,
1944,
257,
2836,
351,
281,
3663,
284,
43519,
511,
898,
2116,
12,
198,
220,
220,
220,
9877,
351,
257,
17571,
850,
8002,
316,
13,
628,
220,
220,
220,
770,
19638,
4909,
1366,
19365,
351,
257,
23606,
19482,
12,
2539,
11862,
198,
220,
220,
220,
290,
6861,
1028,
17613,
416,
262,
25630,
12,
16,
12234,
11862,
13,
220,
1649,
198,
220,
220,
220,
340,
468,
587,
875,
15109,
11,
340,
481,
6032,
3994,
584,
24624,
357,
28950,
198,
220,
220,
220,
257,
25659,
1691,
6060,
19638,
393,
3082,
2790,
6060,
19638,
737,
220,
383,
938,
875,
15109,
198,
220,
220,
220,
19638,
287,
428,
19638,
338,
21437,
17191,
307,
257,
3401,
2649,
46254,
6127,
198,
220,
220,
220,
19638,
13,
628,
220,
220,
220,
383,
1767,
286,
428,
19638,
10874,
286,
25,
628,
220,
220,
220,
220,
532,
317,
530,
12,
38441,
316,
2196,
1271,
13,
220,
383,
691,
3058,
5447,
1988,
318,
198,
220,
220,
220,
220,
220,
220,
352,
13,
628,
220,
220,
220,
220,
532,
14711,
15109,
1366,
11,
262,
5072,
286,
262,
6163,
23606,
19482,
12,
2539,
38012,
198,
220,
220,
220,
220,
220,
220,
5361,
287,
44334,
37774,
4235,
351,
6482,
2033,
4961,
284,
262,
198,
220,
220,
220,
220,
220,
220,
2512,
2546,
286,
262,
38012,
357,
22495,
33,
12,
77,
810,
299,
318,
262,
2512,
2546,
737,
628,
220,
220,
220,
383,
23606,
19482,
38012,
973,
17191,
307,
7368,
287,
257,
5094,
12,
9218,
393,
198,
220,
220,
220,
1632,
3020,
19482,
12,
9218,
14711,
15109,
23575,
7383,
19638,
326,
8555,
274,
262,
198,
220,
220,
220,
1632,
3020,
21879,
1146,
14711,
15109,
6060,
19638,
13,
220,
554,
2035,
1339,
11,
262,
38012,
198,
220,
220,
220,
11862,
19318,
316,
318,
7694,
2966,
284,
262,
6246,
1994,
878,
340,
318,
198,
220,
220,
220,
19365,
13,
628,
220,
220,
220,
383,
1366,
318,
19365,
287,
18551,
33,
4235,
11,
351,
257,
18551,
33,
6482,
2546,
4961,
284,
262,
198,
220,
220,
220,
38012,
338,
2512,
2546,
13,
220,
383,
20768,
20650,
357,
3824,
8,
318,
7368,
355,
477,
198,
220,
220,
220,
1976,
27498,
13,
220,
5455,
286,
1262,
281,
8363,
11,
4946,
6968,
47,
21231,
274,
281,
19318,
316,
4731,
284,
198,
220,
220,
220,
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] | 3.291784 | 8,191 |
from datasets import ptb
from rnnlm_gen import RNNLMGen
corpus, word_to_id, id_to_word = ptb.load_data('train')
vocab_size = len(word_to_id)
corpus_size = len(corpus)
model = RNNLMGen()
# model.load_params('./chapter06/RNNLM.pkl')
# set start and skip word
start_word = 'you'
start_id = word_to_id[start_word]
skip_words = ['N', '<unk>', '$']
skip_ids = [word_to_id[w] for w in skip_words]
# generate sentence
word_ids = model.generate(start_id, skip_ids)
txt = ' '.join([id_to_word[i] for i in word_ids])
txt = txt.replace(' <eos>', '.\n')
print(txt)
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] | 2.345992 | 237 |
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