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} | 2.4375 | stackv2 | import pandas as pd
import numpy as np
import os
import sys
os.chdir("../../../dynamic-reproducibility")
sys.path.append('utils')
from terminal_outputs import printProgressBar
from confs import Config
QUERIES = Config.chicago_queries
CLUSTERS = Config.chicago_clusters
if not os.path.exists('input/Crimes_Workload'):
print('creating directory' ,'Crimes_Workload')
os.makedirs('input/Crimes_Workload')
existing = set(os.listdir('input/Crimes_Workload'))
if 'train_workload_gauss-{}-users-{}.csv'.format(CLUSTERS,QUERIES) in existing or 'test_workload_gauss-{}-users-{}.csv'.format(CLUSTERS,QUERIES) in existing:
print("Files already exist; exiting ")
sys.exit(0)
def load_data():
print("Loading Data...")
global df
df = pd.read_csv('input/Crimes_-_2001_to_present.csv', header=0)
sample = df.sample(10000)
global x_mean
x_mean = float(sample[['X Coordinate']].mean())
global y_mean
y_mean = float(sample[['Y Coordinate']].mean())
global x_std
x_std = float(sample[['X Coordinate']].std())
global y_std
y_std = float(sample[['Y Coordinate']].std())
del sample
def set_number_and_locations_of_clusters():
print("Constructing central points for queries")
clusters = np.random.multivariate_normal([x_mean, y_mean], [[x_std**2, 0],[0, y_std**2]], CLUSTERS)
#Create queries around those central points
#Fraction of variance for how widespread the queries would be
queries = map(lambda x : np.random.multivariate_normal(x, [[0.01*(x_std),0],[0,0.01*(y_std)]], int(QUERIES/CLUSTERS)), clusters)
global col_queries
col_queries = np.array(list(queries)).reshape(-1,2)
def __get_query(q):
multiplier_x = np.random.rand()#varying range at queries
x_range = (x_std/2.0)*multiplier_x
predicate1_0 = df['X Coordinate']>=q[0]-x_range
predicate1_1 = df['X Coordinate']<=q[0]+x_range
multiplier_y = np.random.rand()
y_range = (y_std/2.0)*multiplier_y
predicate2_0 = df['Y Coordinate']>=q[1]-y_range
predicate2_1 = df['Y Coordinate']<=q[1]+y_range
return (x_range, y_range, ((predicate1_0) & (predicate1_1) & (predicate2_0) & (predicate2_1)))
def construct_queries():
complete_queries = []
i=0
for q in col_queries:
call = __get_query(q)
x_range = call[0]
y_range = call[1]
res = df[call[2]]
count = int(res.count()[0])
sum_ = float(res['Arrest'].sum())
avg = float(res['Beat'].mean())
complete_queries.append([q[0], q[1],x_range, y_range, count, sum_, avg])
i+=1
printProgressBar(i, QUERIES,prefix = 'Progress:', suffix = 'Complete', length = 50)
finished = pd.DataFrame(np.array(complete_queries), columns=['x','y','x_range', 'y_range', 'count', 'sum_','avg'])
test = finished.sample(frac=.2)
train = finished.drop(test.index)
print("Saving Output Files")
test['count'] = test['count'].replace([np.inf, -np.inf], np.nan).dropna()
test['sum_'] = test['sum_'].replace([np.inf, -np.inf], np.nan).dropna()
train['count'] = train['count'].replace([np.inf, -np.inf], np.nan).dropna()
train['sum_'] = train['sum_'].replace([np.inf, -np.inf], np.nan).dropna()
test.dropna().to_csv('input/Crimes_Workload/test_workload_gauss-{}-users-{}.csv'.format(CLUSTERS,QUERIES))
train.dropna().to_csv('input/Crimes_Workload/train_workload_gauss-{}-users-{}.csv'.format(CLUSTERS,QUERIES))
if __name__=='__main__':
np.random.seed(15)
load_data()
set_number_and_locations_of_clusters()
construct_queries()
| 85 | 40.88 | 157 | 15 | 1,025 | python | [] | 0 | true | |
2024-11-19T01:43:03.162685+00:00 | 1,568,097,772,000 | 18c2226f9be707528e39e21b920eaf22f08a20e9 | 2 | {
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"visit_date": "2022-12-10T02:49:27.979311"
} | 2.328125 | stackv2 | from . import main
from flask import render_template,redirect,request,url_for
from app.requests import getNews,getSearchItem,getArticles
# Insert home page view
@main.route('/')
def home():
"""
homepage route
"""
title = "Welcome to Newsrecapp"
news = getNews()
return render_template('index.html',sources = news, title = title)
@main.route('/source/<id>')
def source(id):
headlines = getArticles(id)
source = id
return render_template('articles.html',headlines = headlines , source = source)
@main.route('/news/highlight')
def highlight():
technology = getSearchItem('technology')
title = 'News Highlight'
search_article = request.args.get('searchword')
if search_article:
return redirect(url_for('.searchArticle',new_article = search_article))
else:
return render_template('newsHighlight.html',title = title,technology = technology)
@main.route('/search/<new_article>')
def searchArticle(new_article):
title = f'{new_article}'
search = getSearchItem(new_article)
return render_template('search.html',title = title,search = search) | 35 | 29.83 | 86 | 13 | 248 | python | [] | 0 | true | |
2024-11-19T01:43:03.275467+00:00 | 1,618,831,291,000 | 414ab79a4ade5f6580615cc63d0e6bfcd076b5b9 | 3 | {
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"path": "/csv_generator/models/schema.py",
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"repo_name": "Vitamal/schemas",
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"visit_date": "2023-05-07T15:38:15.865366"
} | 2.578125 | stackv2 | from django.db import models
from . import BaseModel
class Schema(BaseModel):
COMMA = ','
SEMICOLON = ';'
COLON = ':'
COLUMN_SEPARATOR = [
(COMMA, 'Comma (,)'),
(SEMICOLON, 'Semicolon(;)'),
(COLON, 'Colon(:)'),
]
QUOTE = "'"
DOUBLE_QUOTE = '"'
STRING_CHARACTER = [
(QUOTE, "Quote(')"),
(DOUBLE_QUOTE, 'Double quote(")'),
]
name = models.CharField(
max_length=100,
verbose_name='name'
)
column_separator = models.CharField(
max_length=1,
choices=COLUMN_SEPARATOR,
default=COMMA,
verbose_name='column separator'
)
string_character = models.CharField(
max_length=1,
choices=STRING_CHARACTER,
verbose_name='string character'
)
schema_column = models.JSONField(null=False, blank=True, default=dict)
def __str__(self):
return self.name
| 39 | 22.64 | 74 | 10 | 227 | python | [] | 0 | true | |
2024-11-19T01:43:05.796585+00:00 | 1,521,557,028,000 | fccab3cd4221e2a0a8d8bcf21150f419adc13bb6 | 3 | {
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"visit_date": "2021-04-09T13:40:17.114654"
} | 2.984375 | stackv2 | import discord
from discord.ext import commands
import aiohttp
import json
class Animals:
def __init__(self, client):
self.client = client
self.session = aiohttp.ClientSession()
@commands.command(name='rd', pass_context=True)
async def random_dog(self, ctx, *args):
"""
gets a random dog image from dog.ceo
:param args: breed of dog
"""
mention = ctx.message.author.mention
args = args or {}
if len(args) == 1:
url = "https://dog.ceo/api/breed/{0}/images/random".format(args[0])
elif len(args) == 2:
url = "https://dog.ceo/api/breed/{0}/{1}/images/random".format(
args[1], args[0])
else:
url = "https://dog.ceo/api/breeds/image/random"
r = await self.session.get(url)
resp = await r.text()
if resp == "Not found":
await self.client.say("{0} Error: No image found!".format(mention))
return
else:
resp = json.loads(resp)
if resp['status'] != "success":
await self.client.say("{0} Error: {1}"
.format(mention, resp['message']))
else:
try:
img = resp['message']
except Exception as e:
await self.client.say("{0} Error: Something went wrong!"
.format(mention))
print(e)
else:
embed = discord.Embed()
embed.set_image(url=img)
await self.client.say(embed=embed)
def setup(client):
client.add_cog(Animals(client))
| 59 | 27.39 | 79 | 20 | 368 | python | [] | 0 | true | |
2024-11-19T01:43:08.399074+00:00 | 1,630,733,693,000 | 685da1b346980640b79c50776c6e3acefc8741b1 | 2 | {
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"visit_date": "2023-07-17T03:37:41.910631"
} | 2.421875 | stackv2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from _lark.card.modules import Module
from typing import List, Optional
class CardConfig:
def __init__(self,
wide_screen_mode: bool = True,
enable_forward: bool = True):
self.wide_screen_mode = wide_screen_mode
self.enable_forward = enable_forward
class CardHeader:
def __init__(self, title: str, template: Optional[str] = None):
self.title = title
self.template = template
class CardMessage:
def __init__(self,
elements: List[Module],
config: Optional[CardConfig] = None,
header: Optional[CardHeader] = None):
self.elements = elements
self.config = config
self.header = header
| 29 | 26.03 | 67 | 11 | 170 | python | [] | 0 | true | |
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"license": "BSD-3-Clause",
"license_type": "permissive",
"path": "/foliage/utils.py",
"provenance": "stack-edu-0057.json.gz:284821",
"repo_name": "harrislapiroff/wagtail-foliage",
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"visit_date": "2022-06-08T11:28:01.664626"
} | 3.015625 | stackv2 | from typing import Iterable, Tuple, List
from wagtail import VERSION
if VERSION < (2, 0):
from wagtail.wagtailcore.models import Page, Site
else:
from wagtail.core.models import Page, Site
def build_page_tree(
# Would that mypy supported recursive types
tree: Iterable[Tuple[Page, Iterable]],
root: Page = None
) -> List[Page]:
"""
Construct a page tree in the database. Accepts a tree in the form:
[
(Page, [
(Page, [...]),
(Page, [...]),
Page,
]),
]
where ``[...]`` is a nested iterable of (Page, children) tuples or bare
page instances.
"""
created = []
for node in tree:
if isinstance(node, Page):
# If `node` is a bare page, it has no children
page = node
children = []
else:
# Otherwise assume it is a (Page, children) tuple
page, children = node
if root:
root.add_child(instance=page)
else:
type(page).add_root(instance=page)
created += [page]
created += build_page_tree(children, root=page)
return created
def get_site() -> Site:
try:
return Site.objects.get()
except Site.MultipleObjectsReturned:
# Reraise MultipleObjectsReturned, but with our own message
raise Site.MultipleObjectsReturned(
'Foliage can\'t auto-determine the Wagtail Site. '
'More than one Site exists in the database!'
)
def get_root_page() -> Page:
try:
return Page.objects.get(depth=1)
except Page.MultipleObjectsReturned:
# Reraise MultipleObjectsReturned, but with our own message
raise Site.MultipleObjectsReturned(
'Foliage can\'t auto-determine the root page. '
'More than one Page exists with depth 1 in the database!'
)
| 72 | 25.71 | 75 | 14 | 435 | python | [] | 0 | true | |
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"detected_licenses": [
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],
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"filename": "github.py",
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"license": "MIT",
"license_type": "permissive",
"path": "/github_art/github.py",
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"repo_name": "ufocoder/py-GithubArt",
"revision_date": 1431018320000,
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"visit_date": "2021-01-15T19:27:59.277561"
} | 2.484375 | stackv2 | import subprocess
import os
from datepoints import GithubDatePoints
from pixelsets import LettersPixelSet
class Github(object):
def __init__(self, string, cwd, dictionary):
self.__string = string
self.__dictionary = dictionary
self.__cwd = os.path.abspath(cwd)
self.commands = []
def __git_init(self):
self.commands.append(['git', 'init'])
self.commands.append(['git', 'show-ref'])
def __git_commit(self, datetime_point):
# Date formats, URL: http://git-scm.com/docs/git-commit
file = datetime_point.strftime("%Y-%m-%d_%H-%M-%S")
date = datetime_point.strftime("%Y-%m-%d %H:%M:%S %z")
self.commands.append(['touch', file])
self.commands.append(['git', 'add', file])
self.commands.append(['git', 'commit', '--message="' + file + '"', '--date="' + date + '"'])
def initialite(self):
pixel_set = LettersPixelSet(self.__string, self.__dictionary).get_pixel_set()
date_points = GithubDatePoints(pixel_set).get_date_points()
self.__git_init()
for date_point in date_points:
self.__git_commit(date_point)
def set_account(self, username, proejct):
github_url = 'https://' + username + '@github.com/' + username + '/' + proejct + '.git'
self.commands.append(['git', 'remote', 'add', 'origin', github_url])
self.commands.append(['git', 'push', '-u', 'origin', 'master'])
def run(self):
for command in self.commands:
subprocess.call(command, cwd=self.__cwd)
| 45 | 33.82 | 100 | 15 | 370 | python | [] | 0 | true | |
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"visit_date": "2020-05-18T15:57:09.566888"
} | 2.96875 | stackv2 | #!/usr/bin/python
# ---------------- READ ME ---------------------------------------------
# This Script is Created Only For Practise And Educational Purpose Only
# This Script Is Created For https://bitforestinfo.blogspot.in
# This Script is Written By
#
#
##################################################
######## Please Don't Remove Author Name #########
############### Thanks ###########################
##################################################
#
#
__author__='''
######################################################
By S.S.B Group
######################################################
Suraj Singh
Admin
S.S.B Group
surajsinghbisht054@gmail.com
https://bitforestinfo.blogspot.in/
Note: We Feel Proud To Be Indian
######################################################
'''
# Imprt Module
import bs4
import urllib2, sys
if len(sys.argv)==1:
print "[*] Please Provide Domain Name:\n Usages: python link_bs4.py www.examplesite.com\n"
sys.exit(0)
def parse_url(url):
try:
html=urllib2.urlopen(url).read() # Reading Html Codes
except Exception as e:
print "[Error] ",e
sys.exit(0)
parse=bs4.BeautifulSoup(html) # Feed Data To bs4
for i in parse.findAll('a'): # Searching For link Tag
if 'href' in i.attrs.keys(): # Searching For Href key
link=i.attrs['href']
print link
return
parse_url(sys.argv[1]) | 50 | 28.32 | 94 | 12 | 306 | python | [] | 0 | true | |
2024-11-19T01:43:18.153600+00:00 | 1,627,908,985,000 | d98fc8a37f9074c4325a295c475c6745b0b79e45 | 4 | {
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"visit_date": "2023-06-25T23:33:12.386799"
} | 3.703125 | stackv2 | import yfinance as yf
import streamlit as st
import pandas as pd
st.write("""
# Simple Stock Price App
Shown are the stock closing price and volume of Google!
""")
# https://towardsdatascience.com/how-to-get-stock-data-using-python-c0de1df17e75
#define the ticker symbol
tickerSymbol = 'GOOGL'
#get data on this ticker
tickerData = yf.Ticker(tickerSymbol)
#get the historical prices for this ticker
tickerDf = tickerData.history(period='1d', start='2010-5-31', end='2020-5-31')
# Open High Low Close Volume Dividends Stock Splits
st.line_chart(tickerDf.Close)
st.line_chart(tickerDf.Volume) | 22 | 26.09 | 80 | 8 | 176 | python | [] | 0 | true | |
2024-11-19T01:43:18.274032+00:00 | 1,621,602,436,000 | 9a9f3399d7f823e4f16abea7d511fa212e633efd | 3 | {
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"repo_name": "strpc/easy_notifyer",
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"visit_date": "2023-05-13T06:54:16.027671"
} | 2.921875 | stackv2 | import uuid
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from smtplib import SMTP, SMTP_SSL
from typing import BinaryIO, List, Optional, Union
class Mailer:
"""Object for send mail"""
def __init__(
self,
*,
host: str,
port: int,
login: str,
password: str,
ssl: bool = False,
):
"""
Args:
host (str, optional): = post of smtp server.
port (int, optional): = port of smtp server.
login (str, optional): = login for auth in smtp server.
password (str, optional): password for auth in smtp server.
ssl (bool, optional): use SSL connection for smtp.
"""
self._host = host
self._port = port
self._login = login
self._password = password
self._ssl = ssl
self._connection: Optional[SMTP_SSL, SMTP] = None
def __enter__(self):
self.connect()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.disconnect()
def connect(self):
"""Connect to smtp-server and create session"""
if self._connection is None:
type_conn = SMTP_SSL if self._ssl is True else SMTP
self._connection = type_conn(host=self._host, port=self._port)
self.login()
def login(self):
"""Create session with login/password"""
if self._connection is not None and self._login is not None and self._password is not None:
self._connection.login(user=self._login, password=self._password)
def disconnect(self):
"""Terminate session"""
if self._connection is not None:
self._connection.quit()
@staticmethod
def _format_message(
*,
from_addr: str,
to_addrs: List[str],
text: str,
subject: str,
attach: Union[bytes, str, BinaryIO],
filename: str,
) -> MIMEMultipart:
"""Formatting message for send.
Args:
from_addr (str): the address sending this mail.
to_addrs (list(str)): addresses to send this mail to.
subject (str, optional): subject of the mail.
attach (bytes, str, tuple, optional): file to send.
filename (str, optional): filename for attached file.
Returns:
MIMEMultipart: multipart of message with body, from, to, attach and subject.
"""
message = MIMEMultipart()
message["From"] = from_addr
message["To"] = ", ".join(to_addrs)
message["Subject"] = subject
message.attach(MIMEText(text))
if attach is not None:
filename = filename or uuid.uuid4().hex
if hasattr(attach, "read") and isinstance(attach.read(0), bytes):
attach = attach.read()
elif hasattr(attach, "encode"):
attach = attach.encode()
message.attach(MIMEApplication(attach, name=filename))
return message
def send_message(
self,
*,
message: Optional[str] = None,
from_addr: str,
to_addrs: Union[str, List[str]],
subject: Optional[str] = None,
attach: Optional[Union[bytes, str, BinaryIO]] = None,
filename: Optional[str] = None,
):
"""Send email.
Args:
message (str, optional): Text body of message.
from_addr (str, optional): the address sending this mail.
to_addrs (str, list(str), optional): addresses to send this mail to.
subject (str, optional): subject of the mail.
attach (bytes, str, tuple, optional): file to send.
filename (str, optional): filename for attached file.
"""
to_addrs = [mail.strip() for mail in to_addrs.split(",")]
msg = self._format_message(
from_addr=from_addr,
to_addrs=to_addrs,
text=message,
subject=subject,
attach=attach,
filename=filename,
)
self._connection.sendmail(from_addr=from_addr, to_addrs=to_addrs, msg=msg.as_string())
| 127 | 32.28 | 99 | 16 | 933 | python | [] | 0 | true | |
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} | 2.375 | stackv2 | #!/usr/bin/env python
##########################################################################
# frontends/swig_python/python_rpyc_server.py
#
# Part of Project Thrill - http://project-thrill.org
#
# Copyright (C) 2015 Timo Bingmann <tb@panthema.net>
#
# All rights reserved. Published under the BSD-2 license in the LICENSE file.
##########################################################################
import sys
import marshal
import types
import rpyc
import thrill
class RpcDIA():
def __init__(self, dia):
self._dia = dia
def AllGather(self):
return self._dia.AllGather()
def Size(self):
return self._dia.Size()
def Map(self, map_function):
code1 = marshal.loads(map_function)
func1 = types.FunctionType(code1, globals())
return RpcDIA(self._dia.Map(func1))
def ReduceBy(self, key_extractor, reduce_function):
code1 = marshal.loads(key_extractor)
func1 = types.FunctionType(code1, globals())
code2 = marshal.loads(reduce_function)
func2 = types.FunctionType(code2, globals())
return RpcDIA(self._dia.ReduceBy(func1, func2))
class RpcContext():
def __init__(self, host_ctx, my_host_rank):
self._ctx = thrill.PyContext(host_ctx, my_host_rank)
def Generate(self, generator_function, size):
code1 = marshal.loads(generator_function)
function1 = types.FunctionType(code1, globals())
return RpcDIA(self._ctx.Generate(function1, size))
def Distribute(self, array):
return RpcDIA(self._ctx.Distribute(array))
class MyService(rpyc.Service):
def on_connect(self):
# code that runs when a connection is created
# (to init the serivce, if needed)
print("hello client")
pass
def on_disconnect(self):
# code that runs when the connection has already closed
# (to finalize the service, if needed)
print("client disconnected")
pass
def exposed_Create(self, my_host_rank, endpoints):
print("Creating thrill context for rank",
my_host_rank, "endpoints", endpoints)
host_ctx = thrill.HostContext(my_host_rank, endpoints, 1)
return RpcContext(host_ctx, 0)
if __name__ == "__main__":
from rpyc.utils.server import ThreadedServer
t = ThreadedServer(MyService, port=int(sys.argv[1]),
protocol_config={"allow_public_attrs": True})
t.start()
##########################################################################
| 83 | 29.47 | 77 | 13 | 553 | python | [] | 0 | true | |
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} | 2.390625 | stackv2 | # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# api: paddle.fluid.framework.Program._prune_with_input()
# env: local
# device: cpu
# text：prune-with-input
import paddle.fluid as fluid
import paddle.fluid.optimizer as optimizer
import numpy as np
def sample_data():
res = []
for i in range(2):
data = np.random.normal(size=(2,))
label = np.random.randint(2, size=(1,))
res.append((data, label))
return res
x = fluid.layers.data(name='x', shape=[2], dtype='float32')
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
# define net here
y = fluid.layers.fc(input=[x], size=2, act="softmax")
loss = fluid.layers.cross_entropy(input=y, label=label)
loss = fluid.layers.mean(x=loss)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
with open("original_program", "w") as f:
f.write(str(fluid.default_main_program()))
pruned_program = fluid.default_main_program()._prune_with_input(
feeded_var_names=[y.name, label.name],
targets = [loss])
with open("pruned_program", "w") as f:
f.write(str(pruned_program))
| 51 | 32.12 | 74 | 12 | 421 | python | [] | 0 | true | |
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} | 3.75 | stackv2 | def flower_beds(beds: list):
right_bed = beds[0][1]
left_bed = beds[0][0]
for i in range(1, len(beds)):
if right_bed >= beds[i][0] and right_bed < beds[i][1]:
right_bed = beds[i][1]
elif beds[i][0] > right_bed:
print(f'{left_bed} {right_bed}')
left_bed = beds[i][0]
right_bed = beds[i][1]
print(f'{left_bed} {right_bed}')
if __name__ == '__main__':
gardeners = int(input())
array = [list(map(int, input().split())) for i in range(gardeners)]
sort = sorted(array, key=lambda x: [x[0], -x[1]])
flower_beds(sort)
| 18 | 32.78 | 71 | 15 | 182 | python | [] | 0 | true | |
2024-11-19T01:54:46.944693+00:00 | 1,691,682,979,000 | 3cf4d35d8fcd8899cdfcdee1d2eae8d9e0055ae6 | 3 | {
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} | 2.625 | stackv2 | ## NDA AWS Token Generator
## Author: NIMH Data Archives
## http://ndar.nih.gov
## License: MIT
## https://opensource.org/licenses/MIT
import binascii
import hashlib
import logging
import xml.etree.ElementTree as etree
import sys
if sys.version_info[0] == 2:
import urllib2 as urllib_request
else:
from urllib import request as urllib_request
class NDATokenGenerator(object):
__schemas = {
'soap': 'http://schemas.xmlsoap.org/soap/envelope/',
'data': 'http://gov/nih/ndar/ws/datamanager/server/bean/jaxb'
}
def __init__(self, url):
assert url is not None
self.url = url
logging.debug('constructed with url %s', url)
def generate_token(self, username, password):
logging.info('request to generate AWS token')
encoded_password = self.__encode_password(password)
request_xml = self.__construct_request_xml(username, encoded_password)
return self.__make_request(request_xml)
def __encode_password(self, password):
logging.debug('encoding password')
hasher = hashlib.sha1()
hasher.update(password.encode('utf-8'))
digest_bytes = hasher.digest()
byte_string = binascii.hexlify(digest_bytes)
output = byte_string.decode('utf-8')
logging.debug('encoded password hash: %s', output)
return output
def __construct_request_xml(self, username, encoded_password):
logging.debug('constructing request with %s - %s', username, encoded_password)
soap_schema = self.__schemas['soap']
datamanager_schema = self.__schemas['data']
element = etree.Element('{%s}Envelope' % soap_schema)
body = etree.SubElement(element, '{%s}Body' % soap_schema)
userelement = etree.SubElement(body, '{%s}UserElement' % datamanager_schema)
user = etree.SubElement(userelement, "user")
uid = etree.SubElement(user, "id")
uid.text = '0'
uid = etree.SubElement(user, "name")
uid.text = username
uid = etree.SubElement(user, "password")
uid.text = encoded_password
uid = etree.SubElement(user, "threshold")
uid.text = '0'
logging.debug(etree.tostring(element))
return etree.tostring(element)
def __make_request(self, request_message):
logging.debug('making post request to %s', self.url)
headers = {
'SOAPAction': '"generateToken"',
'Content-Type': 'text/xml; charset=utf-8'
}
request = urllib_request.Request(self.url, data=request_message, headers=headers)
logging.debug(request)
response = urllib_request.urlopen(request)
return self.__parse_response(response.read())
def __parse_response(self, response):
logging.debug('parsing response')
tree = etree.fromstring(response)
error = tree.find('.//errorMessage')
if error is not None:
error_msg = error.text
logging.error('response had error message: %s', error_msg)
raise Exception(error_msg)
generated_token = tree[0][0]
token_elements = [e.text for e in generated_token[0:4]]
token = Token(*token_elements)
return token
class Token:
def __init__(self, access_key, secret_key, session, expiration):
logging.debug('constructing token')
self._access_key = access_key
self._secret_key = secret_key
self._session = session
self._expiration = expiration
@property
def access_key(self):
return self._access_key
@property
def secret_key(self):
return self._secret_key
@property
def session(self):
return self._session
@property
def expiration(self):
return self._expiration
| 121 | 30.55 | 89 | 12 | 838 | python | [{"finding_id": "codeql_py/weak-sensitive-data-hashing_631a910934060c31_a55e6d18", "tool_name": "codeql", "rule_id": "py/weak-sensitive-data-hashing", "finding_type": "path-problem", "severity": "medium", "confidence": "high", "message": "[Sensitive data (password)](1) is used in a hashing algorithm (SHA1) that is insecure for password hashing, since it is not a computationally expensive hash function.\n[Sensitive data (password)](2) is used in a hashing algorithm (SHA1) that is insecure for password hashing, since it is not a computationally expensive hash function.", "remediation": "", "location": {"file_path": "unknown", "line_start": 39, "line_end": null, "column_start": 23, "column_end": 47, "code_snippet": ""}, "cwe_id": "CWE-327", "cwe_name": null, "cvss_score": null, "cvss_vector": null, "owasp_category": null, "references": [], "fingerprint": null, "tags": ["security", "external/cwe/cwe-327", "external/cwe/cwe-328", "external/cwe/cwe-916"], "raw_output": {"ruleId": "py/weak-sensitive-data-hashing", "ruleIndex": 29, "rule": {"id": "py/weak-sensitive-data-hashing", "index": 29}, "message": {"text": "[Sensitive data (password)](1) is used in a hashing algorithm (SHA1) that is insecure for password hashing, since it is not a computationally expensive hash function.\n[Sensitive data (password)](2) is used in a hashing algorithm (SHA1) that is insecure for password hashing, since it is not a computationally expensive hash function."}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 39, "startColumn": 23, "endColumn": 47}}}], "partialFingerprints": {"primaryLocationLineHash": "627b2ac495cdfac9:1", "primaryLocationStartColumnFingerprint": "14"}, "codeFlows": [{"threadFlows": [{"locations": [{"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 30, "startColumn": 40, "endColumn": 48}}, "message": {"text": "ControlFlowNode for password"}}}, {"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 32, "startColumn": 51, "endColumn": 59}}, "message": {"text": "ControlFlowNode for password"}}}, {"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 36, "startColumn": 33, "endColumn": 41}}, "message": {"text": "ControlFlowNode for password"}}}, {"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 39, "startColumn": 23, "endColumn": 47}}, "message": {"text": "ControlFlowNode for Attribute()"}}}]}]}, {"threadFlows": [{"locations": [{"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 36, "startColumn": 33, "endColumn": 41}}, "message": {"text": "ControlFlowNode for password"}}}, {"location": {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 39, "startColumn": 23, "endColumn": 47}}, "message": {"text": "ControlFlowNode for Attribute()"}}}]}]}], "relatedLocations": [{"id": 1, "physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 30, "startColumn": 40, "endColumn": 48}}, "message": {"text": "Sensitive data (password)"}}, {"id": 2, "physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 36, "startColumn": 33, "endColumn": 41}}, "message": {"text": "Sensitive data (password)"}}, {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 30, "startColumn": 40, "endColumn": 48}}}, {"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 36, "startColumn": 33, "endColumn": 41}}}]}}] | 1 | true | |
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} | 2.421875 | stackv2 | import ClientAPI
class PlayAnimation:
def __init__(self, oid):
self.OID = oid
def CancelEffect(self):
pass
def UpdateEffect(self):
pass
def ExecuteEffect(self, sourceOID, animName):
caster = ClientAPI.World.GetObjectByOID(sourceOID)
casterLoc = caster.Position
# attach and play the sound
if not animName in caster.Model.AnimationNames:
return
# play animation
caster.SetProperty('client.animationoverride', True)
caster.QueueAnimation(animName)
# wait for the duration of the animation to turn off the animation override
yield int(caster.Model.AnimationLength(animName) * 1000)
caster.SetProperty('client.animationoverride', False)
# clean up sounds
#target.ClearSounds()
# register the effect
ClientAPI.World.RegisterEffect("PlayAnimation", PlayAnimation)
| 38 | 24.5 | 91 | 14 | 197 | python | [] | 0 | true | |
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} | 2.796875 | stackv2 | from config import redis_conn
def has_fileid(file_id):
return redis_conn.sismember("fileids", file_id)
def add_fileid(file_id):
redis_conn.sadd("fileids", file_id)
def get_filename_by_id(file_id):
return redis_conn.get("{0}:filename".format(file_id))
def set_filename_with_id(file_id, filename):
return redis_conn.set("{0}:filename".format(file_id), filename)
def is_children(file_id, serial):
return redis_conn.sismember("{0}:children".format(file_id), serial)
def add_children(file_id, serial):
redis_conn.sadd("{0}:children".format(file_id), serial)
def get_fileids():
return redis_conn.smembers("fileids")
| 22 | 28.32 | 71 | 10 | 159 | python | [] | 0 | true | |
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} | 2.484375 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2019 Random.Zebra (https://github.com/random-zebra/)
# Distributed under the MIT software license, see the accompanying
# file LICENSE.txt or http://www.opensource.org/licenses/mit-license.php.
from random import choice
import requests
from misc import getCallerName, getFunctionName, printException
api_keys = ["b62b40b5091e", "f1d66708a077", "ed85c85c0126", "ccc60d06f737"]
def process_cryptoID_exceptions(func):
def process_cryptoID_exceptions_int(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
message = "CryptoID Client exception"
printException(getCallerName(True), getFunctionName(True), message, str(e))
return None
return process_cryptoID_exceptions_int
def UTXOS_cryptoID_to_trezor(utxos):
# convert JSON labels
new_utxos = []
for u in utxos:
new_u = {}
new_u["txid"] = u["tx_hash"]
new_u["vout"] = u["tx_ouput_n"]
new_u["satoshis"] = u["value"]
new_u["confirmations"] = u["confirmations"]
new_u["script"] = u["script"]
new_utxos.append(new_u)
return new_utxos
class CryptoIDClient:
def __init__(self, isTestnet=False):
if isTestnet:
raise Exception("\nNo CryptoID Testnet server\n")
self.isTestnet = False
self.url = "http://chainz.cryptoid.info/pivx/api.dws"
self.parameters = {}
def checkResponse(self, parameters):
key = choice(api_keys)
parameters['key'] = key
resp = requests.get(self.url, params=parameters)
if resp.status_code == 200:
data = resp.json()
return data
return None
@process_cryptoID_exceptions
def getAddressUtxos(self, address):
self.parameters = {}
self.parameters['q'] = 'unspent'
self.parameters['active'] = address
res = self.checkResponse(self.parameters)
if res is None:
return None
else:
return UTXOS_cryptoID_to_trezor(res['unspent_outputs'])
@process_cryptoID_exceptions
def getBalance(self, address):
self.parameters = {}
self.parameters['q'] = 'getbalance'
self.parameters['a'] = address
return self.checkResponse(self.parameters)
| 76 | 30.12 | 87 | 15 | 607 | python | [] | 0 | true | |
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} | 2.65625 | stackv2 | import tensorflow as tf
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
IMAGE_SIZE = [100, 100]
epochs = 50
batch_size = 5
PATH = './data/'
# useful for getting number of files
image_files = glob(PATH + '/*/*.jp*g')
# useful for getting number of classes
folders = glob(PATH + '/*')
print("Number of images:", len(image_files))
print("Number of classes: ", len(folders))
resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
for layer in resnet.layers:
layer.trainable = False
x = Flatten()(resnet.output)
prediction = Dense(len(folders), activation='softmax')(x)
model = Model(inputs=resnet.input, outputs=prediction)
# model.summary()
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
gen = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
preprocessing_function=preprocess_input
)
train_generator = gen.flow_from_directory(
PATH,
target_size=IMAGE_SIZE,
shuffle=True,
batch_size=batch_size,
)
r = model.fit(
train_generator,
epochs=epochs,
steps_per_epoch=len(image_files) // batch_size,
)
model.save("models/saved_models_resnet/model100x100.hdf5")
print("Saved Resnet model") | 59 | 27.19 | 89 | 10 | 410 | python | [] | 0 | true | |
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} | 2.96875 | stackv2 | import functools
from enum import IntEnum
from typing import (
Callable,
Generator,
Iterable,
Iterator,
List,
Optional,
Tuple,
TypeVar,
Union,
)
from ..data_logging.data_recorder import DataRecorder
from .candidate_converter import CandidateConverter
class Type(IntEnum):
Value = 0, # None
Tuple = 1, # [(element_type, element_mapping)]
ValueTuple = 2, # length
List = 3, # [(element_type, element_mapping)]
ValueList = 4, # length
Dict = 5, # [(key, element_type, element_mapping)]
Object = 6, # (factory, [(key, element_type, element_mapping)])
From = TypeVar('From')
ValueMapping = None
IterableMapping = List[Tuple[Type, 'Mapping']]
ValueIterableMapping = int
DictMapping = List[Tuple[any, Type, any]]
ObjectMapping = Tuple[Callable[[], object], DictMapping]
Mapping = Union[ValueMapping, IterableMapping, ValueIterableMapping, DictMapping, ObjectMapping]
ValueGenerator = Generator[any, None, None]
class ObjectConverter(CandidateConverter[From, List]):
"""
Converts between POD objects and list of attribute's values.
"""
def __init__(self, prototype: Optional[object] = None):
self.element_type: Type = Type.Value
self.mapping: Mapping = []
self.length: int = 0
if prototype is not None:
self.setup(prototype)
def setup(self, prototype: object, recorder: DataRecorder) -> None:
"""
Creates a mapping of the prototype's attributes to their element types (list, tuple, dict, value)
:param prototype: the object whose attributes are to be mapped
:param recorder: a data recorder
"""
self.element_type, self.mapping = build_mapping(prototype, True)
# https://stackoverflow.com/questions/393053/length-of-generator-output
self.length = len(list(convert_from_element(self.mapping, prototype, self.element_type)))
def convert_from(self, candidate: From) -> List:
"""
:param candidate: instance of the prototype's element_type
:return: values converted from attributes of the candidate
"""
return [value for value in convert_from_element(self.mapping, candidate, self.element_type)]
def convert_to(self, candidate: Iterable) -> From:
"""
:param candidate: list, tuple or ndarray of values of the candidate
:return: instance of element_type with provided values
"""
return convert_to_element(self.mapping, iter(candidate), self.element_type)
def build_iterable_map(prototype: Iterable) -> IterableMapping:
return [build_mapping(element) for element in prototype]
def build_dict_map(prototype: dict) -> DictMapping:
# print(sorted(prototype.keys()))
return build_kvp_map(sorted(prototype.items()))
def build_object_map(prototype: object) -> ObjectMapping:
"""
Entry point for building a map of the prototype object's attributes to their types
:param prototype:
:return:
"""
sorted_attributes = sorted([(attr, getattr(prototype, attr))
for attr in dir(prototype)
if not callable(getattr(prototype, attr))
and not attr.startswith("__")])
print([e[0] for e in sorted_attributes])
return (lambda: type(prototype)(), build_kvp_map(sorted_attributes))
def build_kvp_map(items: Iterable[Tuple[any, any]]) -> DictMapping:
return [(key,) + build_mapping(value) for key, value in items]
def is_mapping_all_values(mapping) -> bool:
return functools.reduce(lambda acc, e: acc and e[0] == Type.Value, mapping, True)
def build_mapping(prototype: any, on_root=False) -> ('Type', Mapping):
if isinstance(prototype, tuple):
mapping = build_iterable_map(prototype)
if is_mapping_all_values(mapping):
return Type.ValueTuple, len(mapping)
return Type.Tuple, mapping
if isinstance(prototype, list):
mapping = build_iterable_map(prototype)
if is_mapping_all_values(mapping):
return Type.ValueList, len(mapping)
return Type.List, mapping
if isinstance(prototype, dict):
return Type.Dict, build_dict_map(prototype)
# if this is the root, then use an object mapping
if on_root:
return Type.Object, build_object_map(prototype)
return Type.Value, None
def convert_from_value(_: None, source: any) -> ValueGenerator:
yield source
def convert_from_iterable(mapping: IterableMapping, source: Iterable) -> ValueGenerator:
for i, value in enumerate(source):
element_type, element_mapping = mapping[i]
yield from convert_from_element(element_mapping, value, element_type)
def convert_from_value_iterable(_: ValueIterableMapping, source: Iterable) -> ValueGenerator:
yield from source
def convert_from_dict(mapping: DictMapping, source: {}) -> ValueGenerator:
for key, element_type, element_mapping in mapping:
yield from convert_from_element(element_mapping, source[key], element_type)
def convert_from_object(mapping: ObjectMapping, source: object) -> ValueGenerator:
for key, element_type, element_mapping in mapping[1]:
yield from convert_from_element(element_mapping, getattr(source, key), element_type)
convert_from_jump_table = {
Type.Value: convert_from_value,
Type.Tuple: convert_from_iterable,
Type.ValueTuple: convert_from_value_iterable,
Type.List: convert_from_iterable,
Type.ValueList: convert_from_value_iterable,
Type.Dict: convert_from_dict,
Type.Object: convert_from_object,
}
def convert_from_element(mapping: Mapping, source, element_type: Type) -> ValueGenerator:
"""
Entry point for converting a source object's attributes to their values using the given mapping
:param mapping: map of the source's attributes to their types
:param source: object from which to convert
:param element_type: type of source
:return: a generator of the object's values
"""
yield from convert_from_jump_table[element_type](mapping, source)
def convert_to_value(_: None, source: Iterator) -> any:
return next(source)
def convert_to_list(mapping: IterableMapping, source: Iterator) -> []:
return list(convert_to_generator(mapping, source))
def convert_to_value_list(mapping: ValueIterableMapping, source: Iterator) -> []:
return list(convert_to_value_generator(mapping, source))
def convert_to_tuple(mapping: IterableMapping, source: Iterator) -> ():
return tuple(convert_to_generator(mapping, source))
def convert_to_value_tuple(mapping: ValueIterableMapping, source: Iterator) -> []:
return tuple(convert_to_value_generator(mapping, source))
def convert_to_generator(mapping: IterableMapping, source: Iterator) -> ValueGenerator:
return (convert_to_element(element_mapping, source, element_type)
for element_type, element_mapping in mapping)
def convert_to_value_generator(mapping: ValueIterableMapping, source: Iterator) -> ValueGenerator:
return (next(source) for i in range(mapping))
def convert_to_dict(mapping: DictMapping, source: Iterator) -> {}:
return {key: convert_to_element(element_mapping, source, element_type)
for key, element_type, element_mapping in mapping}
def convert_to_object(mapping: ObjectMapping, source: Iterator) -> object:
target = mapping[0]()
for key, element_type, element_mapping in mapping[1]:
setattr(target, key, convert_to_element(element_mapping, source, element_type))
return target
convert_to_jump_table = {
Type.Value: convert_to_value,
Type.Tuple: convert_to_tuple,
Type.ValueTuple: convert_to_value_tuple,
Type.List: convert_to_list,
Type.ValueList: convert_to_value_list,
Type.Dict: convert_to_dict,
Type.Object: convert_to_object,
}
def convert_to_element(mapping, source, element_type) -> any:
"""
Entry point for converting values into the source object's attributes using the given mapping
:param mapping: map of the source's attributes to their types
:param source: object from which to convert
:param element_type: type of source
:return: object of type element_type
"""
return convert_to_jump_table[element_type](mapping, source)
| 238 | 34.39 | 105 | 16 | 1,820 | python | [] | 0 | true | |
2024-11-19T01:54:48.208282+00:00 | 1,608,588,137,000 | 233663103a8c8491a9e4a2ad7cc962dc963bf48f | 3 | {
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} | 2.71875 | stackv2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Randall Balestriero"
import symjax.tensor as T
import matplotlib.pyplot as plt
from symjax.viz import compute_graph
x = T.random.randn((10,), name="x")
y = T.random.randn((10,), name="y")
z = T.random.randn((10,), name="z")
w = T.Variable(T.ones(1), name="w")
out = (x + y).sum() * w + z.sum()
graph = compute_graph(out)
graph.draw("file.png", prog="dot")
import matplotlib.image as mpimg
img = mpimg.imread("file.png")
plt.figure(figsize=(15, 5))
imgplot = plt.imshow(img)
plt.xticks()
plt.yticks()
plt.tight_layout()
| 28 | 19.96 | 36 | 10 | 174 | python | [] | 0 | true | |
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} | 2.625 | stackv2 | from enum import Enum
from labelbox.orm.db_object import DbObject
from labelbox.orm.model import Field
class AssetAttachment(DbObject):
""" Asset attachment provides extra context about an asset while labeling.
Attributes:
attachment_type (str): IMAGE, VIDEO, TEXT, or IMAGE_OVERLAY
attachment_value (str): URL to an external file or a string of text
"""
class AttachmentType(Enum):
VIDEO = "VIDEO"
IMAGE = "IMAGE"
TEXT = "TEXT"
IMAGE_OVERLAY = "IMAGE_OVERLAY"
for topic in AttachmentType:
vars()[topic.name] = topic.value
attachment_type = Field.String("attachment_type", "type")
attachment_value = Field.String("attachment_value", "value")
| 25 | 28.32 | 78 | 11 | 158 | python | [] | 0 | true | |
2024-11-19T01:54:49.425883+00:00 | 1,573,896,666,000 | cecca567dc686ee3f24f95fab22d9d379bbaea55 | 3 | {
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} | 3.1875 | stackv2 | import pickle
import pandas as pd
import numpy as np
from scipy.stats import poisson
class FootballModel:
"""
Model that predict a game result between
2 internationals football teams
"""
def __init__(self, path: str):
self.model = self.load_model(path)
@staticmethod
def load_model(path: str):
"""
"""
with open(path, 'rb') as file:
model = pickle.load(file)
return model
def predict_avg_score(self, game):
"""
"""
return self.model.predict(game).values[0] | 27 | 20.11 | 49 | 14 | 128 | python | [] | 0 | true | |
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} | 4.09375 | stackv2 | # create blank class
print "creating empty class object"
print
class A(object):
pass
# create hypothetical constructor
print "defining class constructor"
print "will set instance attribute \"a\""
print
def init(cls, a):
print "initializing the instance"
print
cls.__setattr__("a",a)
# create new method
print """\
defining method \"show\" that takes one argument, \
a class. It then prints class variable \"a\" and \
instance variable \"b\" \
"""
print
def show(cls):
print "received class", cls, "as first argument"
print "class attribute \"b\"", cls.b
print "instnace attribute \"a\":", cls.a
print
# add attribute to class
print "adding class attribute b=10"
print
A.b = 10
# add constructor to class
print "adding class constructor"
print "will set instance attribute \"a\""
print
A.__init__ = init
# add method to class
print "adding \"show\" method to class"
print
A.show = show
# create instance
print "creating instance \"q\" and passing \"5\" to constructor"
print
q = A(5)
# check class attribute
print "checking class attribute \"b\""
print "should equal 10"
print q.b
print
# check instance attribute
print "checking instance attribute \"a\""
print "should equal 5"
print q.a
print
# test instance method
print "testing method \"show\""
print
q.show()
# change class attribute
print "now chaning class attribute \"b\" to 30"
print
A.b = 30
# check reference to class attribute
print "checking that the class attribute changed"
print
q.show()
| 87 | 16.1 | 64 | 8 | 382 | python | [] | 0 | true | |
2024-11-19T01:54:49.910657+00:00 | 1,583,978,741,000 | 02262ebb47b577f6de1c9845338653e100136fb9 | 2 | {
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} | 2.46875 | stackv2 | from .check import RemoteCheck
from .exceptions import MissingExpectedValueError
REPR = 'CheckFile'
class CheckFile(RemoteCheck):
MANDATORY = ['filename']
STAT_FLAGS = {'access_rights': r'%a',
'blocks_num': r'%b',
'byte_per_block': r'%B',
'dev_num': r'%d',
'type': r'%F',
'gid': r'%g',
'group': r'%G',
'link_refcount': r'%h',
'inode': r'%i',
'mount': r'%m',
'name': r'%n',
'size': r'%s',
'uid': r'%u',
'owner': r'%U',
'create_time': r'%W',
'access_time': r'%X',
'mod_time': r'%Y',
}
def __init__(self, check):
self.filename = None
self.seperator = ','
super().__init__(check, self.MANDATORY)
def __repr__(self):
return f"Check File filename: {self.filename}"
def __parse_stat_output(self, output):
return dict(item.split("=") for item in output.split(self.seperator)) if output is not None else None
def execute(self, remote):
check_result = super().execute(remote)
if check_result.return_code == 0:
if check_result.stdout is not None:
try:
check_result.add_result(self.__parse_stat_output(check_result.stdout))
check_result.set(0,check_result.result)
if self.expect is not None:
for k in self.expect:
if k not in check_result.result:
check_result.set(-1,f"Missing expected value in result: {MissingExpectedValueError(k)}")
if check_result.result[k] != self.expect[k]:
check_result.set(1, f'{k}: {check_result.result[k]} != {self.expect[k]}')
except Exception as e:
check_result.set(-1,f'stat command returned 0 but no output available: {e}')
else:
check_result.set(2,f'Error stat command returned {check_result.stderr}')
return check_result
def get_command(self):
format_string = '-c'
for k in CheckFile.STAT_FLAGS:
format_string += f'{k}={CheckFile.STAT_FLAGS[k]}{self.seperator}'
return f'stat {self.filename} {format_string[:-1]}'
| 64 | 39.34 | 120 | 25 | 551 | python | [] | 0 | true | |
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} | 2.4375 | stackv2 | import logging
import discord
from discorrecd.events import EventManager
log = logging.getLogger(__name__)
_EVENT_METHODS = [
('on_ready', 'ready'),
('on_resumed', 'resumed'),
('on_message', 'message'),
('on_message_delete', 'message_delete'),
('on_message_edit', 'message_edit'),
('on_typing', 'typing'),
('on_channel_create', 'channel_create'),
('on_channel_delete', 'channel_delete'),
('on_channel_update', 'channel_update'),
('on_member_create', 'member_create'),
('on_member_delete', 'member_delete'),
('on_member_update', 'member_update'),
('on_member_ban', 'member_ban'),
('on_member_unban', 'member_unban'),
('on_group_join', 'group_join'),
('on_group_remove', 'group_remove'),
('on_server_join', 'server_join'),
('on_server_remove', 'server_remove'),
('on_server_update', 'server_update'),
('on_server_available', 'server_available'),
('on_server_unavailable', 'server_unavailable'),
('on_server_role_create', 'server_role_create'),
('on_server_role_delete', 'server_role_delete'),
('on_server_role_update', 'server_role_update'),
('on_server_emojis_update', 'server_emojis_update'),
('on_voice_state_update', 'voice_state_update'),
('on_socket_raw_receive', 'socket_raw_receive'),
('on_socket_raw_send', 'socket_raw_send')
]
class CoreClient(discord.Client):
"""A wrapper client for the Discorrecd Core"""
def __init__(self, event_manager: EventManager, **options):
"""
:param event_manager: The event manager to events should be registered
:param options: The options which will passed onto the parent (:class:`discord.Client`) constructor
"""
super().__init__(**options)
self._event_manager = event_manager # type: EventManager
self._hook_events()
def _hook_events(self):
"""Populate the event manager by hooking the :class:`discord.Client` event methods."""
self._hook_error_method()
for method_name, event in _EVENT_METHODS:
self._hook_method(method_name, event)
def _hook_method(self, method_name: str, event: str):
"""Hook a :class:`discord.Client` event method.
:param method_name: The name of the method to be hooked
:param event: The name of the event
"""
async def method(*args, **kwargs):
await self._event_manager.emit(event, *args, **kwargs)
method.__name__ = method_name
if event not in self._event_manager:
self._event_manager.add(event)
self.event(method)
def _hook_error_method(self):
"""Hook the `on_error` method"""
async def on_error(*args, **kwargs):
log.error(*args, **kwargs)
await self._event_manager.emit('client_error', *args, **kwargs)
await super().on_error(*args, **kwargs)
if 'client_error' not in self._event_manager:
self._event_manager.add('client_error')
self.event(on_error)
| 85 | 34.64 | 107 | 14 | 700 | python | [] | 0 | true | |
2024-11-19T01:54:50.102401+00:00 | 1,563,498,010,000 | 3721d7aab5e1c4609f4a59c0bdb3e2dc09b6ce85 | 3 | {
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} | 2.78125 | stackv2 | # -*- coding: UTF-8 -*-
import re
import os
import hashlib
class Storage():
def __init__(self, video_url: str) -> None:
self.video_url = video_url
def get_file_path(self) -> str:
return 'subtitle_{0}.en.vtt'.format(re.sub(
r'[^\w-]', '', hashlib.md5(str(self.video_url).encode('utf-8')).hexdigest()))
def remove_file(self) -> None:
os.remove(self.get_file_path())
| 18 | 22.22 | 89 | 20 | 109 | python | [] | 0 | true | |
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"visit_date": "2023-07-01T16:16:01.802364"
} | 2.328125 | stackv2 | from discord_slash.utils.manage_commands import create_option, create_choice
import discord
from discord.ext.commands import Bot
from discord.ext import commands
from discord_slash import cog_ext, SlashContext, SlashCommand
from discord import DMChannel
import re
import asyncio
import os
guild_ids = [int(os.environ['guild_id'])]
embed_color = 0x4ac8c7
class 제리(commands.Cog):
def __init__(self, client):
self.client = client
@cog_ext.cog_slash(name="제리",
description="📁 제리 디시콘",
guild_ids=guild_ids,
options=[
create_option(
name="내용",
description="무슨 내용인가요?",
option_type=3,
required=True,
choices=[
create_choice(
name="메롱",
value="제리메롱"
),
create_choice(
name="처먹",
value="제리처먹"
),
create_choice(
name="띠용",
value="제리띠용"
),
create_choice(
name="화들짝",
value="제리화들짝"
),
create_choice(
name="경악",
value="제리경악"
),
create_choice(
name="쯧쯧",
value="제리쯧쯧"
),
create_choice(
name="끄덕",
value="제리끄덕"
),
create_choice(
name="인사",
value="제리인사"
),
create_choice(
name="폭소",
value="제리폭소"
)
]
)
])
async def _제리(self, ctx, 내용: str):
if 내용 == "제리메롱":
embed=discord.Embed(description="제리메롱", color=embed_color)
embed.set_image(url="https://i.ibb.co/2ymkqL0/wpflapfhd.gif")
await ctx.send(embed=embed)
elif 내용 == "제리처먹":
embed=discord.Embed(description="제리처먹", color=embed_color)
embed.set_image(url="https://i.ibb.co/wsD8mfj/wpflcjajr.gif")
await ctx.send(embed=embed)
elif 내용 == "제리띠용":
embed=discord.Embed(description="제리띠용", color=embed_color)
embed.set_image(url="https://i.ibb.co/whdv6J4/wpflEldyd.gif")
await ctx.send(embed=embed)
elif 내용 == "제리화들짝":
embed=discord.Embed(description="제리화들짝", color=embed_color)
embed.set_image(url="https://i.ibb.co/qDn4516/wpflghkemfWkr.gif")
await ctx.send(embed=embed)
elif 내용 == "제리경악":
embed=discord.Embed(description="제리경악", color=embed_color)
embed.set_image(url="https://i.ibb.co/ngM8DtF/wpflruddkr.gif")
await ctx.send(embed=embed)
elif 내용 == "제리쯧쯧":
embed=discord.Embed(description="제리쯧쯧", color=embed_color)
embed.set_image(url="https://i.ibb.co/1rNnyPY/wpflWmtWmt.gif")
await ctx.send(embed=embed)
elif 내용 == "제리끄덕":
embed=discord.Embed(description="제리끄덕", color=embed_color)
embed.set_image(url="https://i.ibb.co/dL00Xk6/wpfl-Rmejr.gif")
await ctx.send(embed=embed)
elif 내용 == "제리인사":
embed=discord.Embed(description="제리인사", color=embed_color)
embed.set_image(url="https://i.ibb.co/Gs7sNxZ/wpfldlstk2.gif")
await ctx.send(embed=embed)
elif 내용 == "제리폭소":
embed=discord.Embed(description="제리폭소", color=embed_color)
embed.set_image(url="https://i.ibb.co/JKxvDfN/wpflvhrth.gif")
await ctx.send(embed=embed)
def setup(client):
client.add_cog(제리(client)) | 106 | 39.97 | 77 | 18 | 939 | python | [] | 0 | true | |
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"visit_date": "2023-08-20T03:42:48.606441"
} | 2.75 | stackv2 | """
-*- coding: utf-8 -*-
========================
AWS Lambda
========================
Contributor: Chirag Rathod (Srce Cde)
========================
"""
import os
import json
import boto3
import pandas as pd
def lambda_handler(event, context):
BUCKET_NAME = os.environ["BUCKET_NAME"]
PREFIX = os.environ["PREFIX"]
job_id = json.loads(event["Records"][0]["Sns"]["Message"])["JobId"]
page_lines = process_response(job_id)
csv_key_name = f"{job_id}.csv"
df = pd.DataFrame(page_lines.items())
df.columns = ["PageNo", "Text"]
df.to_csv(f"/tmp/{csv_key_name}", index=False)
upload_to_s3(f"/tmp/{csv_key_name}", BUCKET_NAME, f"{PREFIX}/{csv_key_name}")
print(df)
return {"statusCode": 200, "body": json.dumps("File uploaded successfully!")}
def upload_to_s3(filename, bucket, key):
s3 = boto3.client("s3")
s3.upload_file(Filename=filename, Bucket=bucket, Key=key)
def process_response(job_id):
textract = boto3.client("textract")
response = {}
pages = []
response = textract.get_document_text_detection(JobId=job_id)
pages.append(response)
nextToken = None
if "NextToken" in response:
nextToken = response["NextToken"]
while nextToken:
response = textract.get_document_text_detection(
JobId=job_id, NextToken=nextToken
)
pages.append(response)
nextToken = None
if "NextToken" in response:
nextToken = response["NextToken"]
page_lines = {}
for page in pages:
for item in page["Blocks"]:
if item["BlockType"] == "LINE":
if item["Page"] in page_lines.keys():
page_lines[item["Page"]].append(item["Text"])
else:
page_lines[item["Page"]] = []
return page_lines
| 72 | 24.4 | 81 | 18 | 439 | python | [] | 0 | true | |
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} | 2.53125 | stackv2 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals # unicode by default
from datetime import datetime
from sqlalchemy import Column, UnicodeText, Boolean, Integer, ForeignKey, \
DateTime, Unicode
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.hybrid import hybrid_property
from mootiro_form import _
from mootiro_form.models import Base, id_column, sas
from mootiro_form.models.form import Form
from mootiro_form.utils.text import random_word
class Collector(Base):
'''Represents a collector to collect form entries.'''
__tablename__ = "collector"
id = id_column(__tablename__)
# Inheritance configuration
typ = Column('type', UnicodeText(50))
__mapper_args__ = {'polymorphic_on': typ}
name = Column(UnicodeText(255), nullable=False)
# When an entry is received, we can either display a thanks message,
# or redirect to some URL. 3 columns are needed for this:
thanks_message = Column(UnicodeText)
thanks_url = Column(UnicodeText(2000))
# We define on_completion as a property to validate its possible values:
ON_COMPLETION_VALUES = ('msg', 'url')
_on_completion = Column('on_completion', Unicode(3))
@hybrid_property
def on_completion(self):
return self._on_completion
@on_completion.setter
def on_completion(self, val):
if val not in self.ON_COMPLETION_VALUES:
raise ValueError \
('Invalid value for on_completion: "{0}"'.format(val))
self._on_completion = val
email_each_entry = Column(Boolean, default=False)
limit_by_date = Column(Boolean, default=False)
start_date = Column(DateTime)
end_date = Column(DateTime)
message_after_end = Column(UnicodeText)
message_before_start = Column(UnicodeText)
# When an instance is persisted, it automatically gets a slug,
slug = Column(UnicodeText(10), nullable=False, # a part of the URL.
index=True, default=lambda: random_word(10))
form_id = Column(Integer, ForeignKey('form.id'), index=True)
form = relationship(Form, backref=backref('collectors', order_by=id,
cascade='all'))
def __unicode__(self):
return self.name
def __repr__(self):
return 'Collector(id={0}, name="{1}")'.format(self.id, self.name)
def to_dict(self, translator=None):
d = {k: getattr(self, k) for k in ('id', 'name', 'thanks_message',
'thanks_url', 'on_completion', 'message_before_start',
'message_after_end', 'email_each_entry', 'limit_by_date', 'slug',
'status')}
d['start_date'] = unicode(self.start_date)[:16] \
if self.start_date else ''
d['end_date'] = unicode(self.end_date)[:16] if self.end_date else ''
d['type'] = self.typ
d['display_type'] = self.typ.replace("_", " ").capitalize()
d['translated_status'] = \
translator(d['status']) if translator else d['status']
return d
STATUS_BEFORE = _('pending') # before start date
STATUS_DURING = _('published') # entries may be created
STATUS_AFTER = _('closed') # after end date
@property
def status(self):
'''Returns a status code.'''
if (self.start_date and datetime.utcnow() < self.start_date
and self.limit_by_date):
return self.STATUS_BEFORE
if (self.end_date and datetime.utcnow() > self.end_date
and self.limit_by_date):
return self.STATUS_AFTER
return self.STATUS_DURING
class PublicLinkCollector(Collector):
'''A collector that provides a slug based public link for collecting
entries.
We expect to add columns here in the future.
'''
__tablename__ = 'public_link_collector'
__mapper_args__ = {'polymorphic_identity': 'public_link'}
id = Column(Integer, ForeignKey('collector.id'), primary_key=True)
class WebsiteCodeCollector(Collector):
'''A collector that provides slug based html codes for collecting entries
inside external websites.
We expect to add columns here in the future, also.
'''
__tablename__ = 'website_code_collector'
__mapper_args__ = {'polymorphic_identity': 'website_code'}
id = Column(Integer, ForeignKey('collector.id'), primary_key=True)
invitation_message = Column(UnicodeText)
invitation_popup_width = Column(Integer)
invitation_popup_height = Column(Integer)
embed_frame_height = Column(Integer)
def to_dict(self, translator=None):
d = super(WebsiteCodeCollector, self).to_dict(translator=translator)
d['invitation_message'] = self.invitation_message
d['invitation_popup_width'] = self.invitation_popup_width
d['invitation_popup_height'] = self.invitation_popup_height
d['embed_frame_height'] = self.embed_frame_height
return d
| 127 | 37.43 | 77 | 15 | 1,100 | python | [] | 0 | true | |
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} | 2.671875 | stackv2 | import codecs
import json
import numpy as np
from gensim.models import Word2Vec
from keras.layers import Embedding
# tokenizing function
def tokenize(sentence):
result = sentence.replace('\n', '').split(' ')
return(result)
# create embeddings with gensim
def create_embeddings(file_name,
embeddings_path='temp_embeddings/embeddings.gensimmodel',
vocab_path='temp_embeddings/mapping.json',
**params):
class SentenceGenerator(object):
def __init__(self, filename):
self.filename = filename
def __iter__(self):
for line in codecs.open(self.filename, 'rU', encoding='utf-8'):
yield tokenize(line)
sentences = SentenceGenerator(file_name)
model = Word2Vec(sentences, **params)
model.save(embeddings_path)
# weights = model.syn0
# np.save(open(embeddings_path, 'wb'), weights)
# http://stackoverflow.com/questions/35596031/gensim-word2vec-find-number-of-words-in-vocabulary
vocab = dict([(k, v.index) for k, v in model.wv.vocab.items()])
with open(vocab_path, 'w') as f:
f.write(json.dumps(vocab))
return vocab, model
# load vocabulary index from json file
def load_vocab(vocab_path='temp_embeddings/mapping.json'):
with open(vocab_path, 'r') as f:
data = json.loads(f.read())
word2idx = data
idx2word = dict([(v, k) for k, v in data.items()])
return word2idx, idx2word
# embedding layer function
def word2vec_embedding_layer(embeddings_path='temp_embeddings/embeddings.npz'):
weights = np.load(open(embeddings_path, 'rb'))
layer = Embedding(input_dim=weights.shape[0], output_dim=weights.shape[1], weights=[weights])
return layer | 54 | 31.43 | 100 | 14 | 413 | python | [] | 0 | true | |
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} | 4 | stackv2 | """
# PROBLEM 9
A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
a^2 + b^2 = c^2
For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
There exists exactly one Pythagorean triplet for which a + b + c = 1000.
Find the product abc.
"""
from math import ceil
# Okay, so obviously a, b, and c must be constrained to be below 997.
# And a and b both need to be below 332 and 499 respectively. Which implies the lowest
# possible value for c is 334. But there has to be an upper bound to c as well that is actually
# lower than 997. Because 1 ^ 2 + 2 ^ 2 != 997 ^ 2.
# There's the ~ n ^ 2 complexity solution, which requires us to iterate a and b between
# The bounds we've established. Is there are smart way to work from both ends and iteratively
# Squeeze the values towards a solution, maybe in linear time?
# I can't think of what this might be, so I may as well just settle for a mildly smart
# brute-ish force solution
for a in range(1, 333):
for b in range(a + 1, int(500 - ceil(a/2) + 1)):
c = 1000 - b - a
# Note that when a is even, we need to remove the case where b == c
if c > b and (a ** 2 + b ** 2) == c ** 2:
print(a, b, c, a * b * c)
exit(0)
# we've been assured there's only one possible number, so exit with no error here
exit(1) # something obviously has gone wrong.
| 37 | 36.22 | 95 | 14 | 437 | python | [] | 0 | true | |
2024-11-19T01:54:50.967513+00:00 | 1,430,214,365,000 | a95617a9165a5ac94f00b63b5dfc1a30b3eb0a70 | 3 | {
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} | 2.59375 | stackv2 | # -*- coding: utf-8 -*-
import nltk
from ... import config
if "simple" in config.engines:
labels = config.engines["simple"]["labels"]
def convert_label(label):
if label in labels:
return labels[label]
else:
print "simple:",label
return label
# see http://timmcnamara.co.nz/post/2650550090/extracting-names-with-6-lines-of-python-code
def extract_entities(text, lang):
entities={}
for sent in nltk.sent_tokenize(text):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
# see http://stackoverflow.com/questions/26352041/nltk-entity-extraction-difference-from-nltk-2-0-4-to-nltk-3-0
if hasattr(chunk, "label"):
key=' '.join(c[0] for c in chunk.leaves())
value=convert_label(chunk.label())
entities[key]=value
return entities | 28 | 26.93 | 114 | 17 | 218 | python | [] | 0 | true | |
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################################################################################
#This software was developed by the University of Tennessee as part of the
#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
#project funded by the US National Science Foundation.
#
#See the license text in license.txt
#
#copyright 2009, University of Tennessee
################################################################################
import wx
from wx.lib.scrolledpanel import ScrolledPanel
MAX_NBR_DATA = 4
WIDTH = 430
HEIGHT = 350
class DialogPanel(ScrolledPanel):
def __init__(self, *args, **kwds):
ScrolledPanel.__init__(self, *args, **kwds)
self.SetupScrolling()
class BatchDataDialog(wx.Dialog):
"""
The current design of Batch fit allows only of type of data in the data
set. This allows the user to make a quick selection of the type of data
to use in fit tab.
"""
def __init__(self, parent=None, *args, **kwds):
wx.Dialog.__init__(self, parent, *args, **kwds)
self.SetSize((WIDTH, HEIGHT))
self.data_1d_selected = None
self.data_2d_selected = None
self._do_layout()
def _do_layout(self):
"""
Draw the content of the current dialog window
"""
vbox = wx.BoxSizer(wx.VERTICAL)
box_description = wx.StaticBox(self, wx.ID_ANY, str("Hint"))
hint_sizer = wx.StaticBoxSizer(box_description, wx.VERTICAL)
selection_sizer = wx.GridBagSizer(5, 5)
button_sizer = wx.BoxSizer(wx.HORIZONTAL)
self.data_1d_selected = wx.RadioButton(self, wx.ID_ANY, 'Data1D',
style=wx.RB_GROUP)
self.data_2d_selected = wx.RadioButton(self, wx.ID_ANY, 'Data2D')
self.data_1d_selected.SetValue(True)
self.data_2d_selected.SetValue(False)
button_cancel = wx.Button(self, wx.ID_CANCEL, "Cancel")
button_OK = wx.Button(self, wx.ID_OK, "Ok")
button_OK.SetFocus()
hint = "Selected Data set contains both 1D and 2D Data.\n"
hint += "Please select on type of analysis before proceeding.\n"
hint_sizer.Add(wx.StaticText(self, wx.ID_ANY, hint))
#draw area containing radio buttons
ix = 0
iy = 0
selection_sizer.Add(self.data_1d_selected, (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
iy += 1
selection_sizer.Add(self.data_2d_selected, (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
#contruction the sizer contaning button
button_sizer.Add((20, 20), 1, wx.EXPAND | wx.ADJUST_MINSIZE, 0)
button_sizer.Add(button_cancel, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
button_sizer.Add(button_OK, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
vbox.Add(hint_sizer, 0, wx.EXPAND | wx.ALL, 10)
vbox.Add(selection_sizer, 0, wx.TOP | wx.BOTTOM, 10)
vbox.Add(wx.StaticLine(self, wx.ID_ANY), 0, wx.EXPAND, 0)
vbox.Add(button_sizer, 0, wx.TOP | wx.BOTTOM, 10)
self.SetSizer(vbox)
self.Layout()
def get_data(self):
"""
return 1 if user requested Data1D , 2 if user requested Data2D
"""
if self.data_1d_selected.GetValue():
return 1
else:
return 2
class DataDialog(wx.Dialog):
"""
Allow file selection at loading time
"""
def __init__(self, data_list, parent=None, text='',
nb_data=MAX_NBR_DATA, *args, **kwds):
wx.Dialog.__init__(self, parent, *args, **kwds)
self.SetTitle("Data Selection")
self._max_data = nb_data
self._nb_selected_data = nb_data
self.SetSize((WIDTH, HEIGHT))
self.list_of_ctrl = []
if not data_list:
return
select_data_text = " %s Data selected.\n" % str(self._nb_selected_data)
self._data_text_ctrl = wx.StaticText(self, wx.ID_ANY, str(select_data_text))
self._data_text_ctrl.SetForegroundColour('blue')
self._sizer_main = wx.BoxSizer(wx.VERTICAL)
self._sizer_txt = wx.BoxSizer(wx.VERTICAL)
self._sizer_button = wx.BoxSizer(wx.HORIZONTAL)
self._choice_sizer = wx.GridBagSizer(5, 5)
self._panel = DialogPanel(self, style=wx.RAISED_BORDER,
size=(WIDTH - 20, HEIGHT / 3))
self.__do_layout(data_list, text=text)
def __do_layout(self, data_list, text=''):
"""
layout the dialog
"""
if not data_list or len(data_list) <= 1:
return
#add text
if text.strip() == "":
text = "Fitting: We recommend that you selected"
text += " no more than '%s' data\n" % str(self._max_data)
text += "for adequate plot display size. \n"
text += "unchecked data won't be send to fitting . \n"
text_ctrl = wx.StaticText(self, wx.ID_ANY, str(text))
self._sizer_txt.Add(text_ctrl)
iy = 0
ix = 0
data_count = 0
for i in range(len(data_list)):
data_count += 1
cb = wx.CheckBox(self._panel, wx.ID_ANY, str(data_list[i].name), (10, 10))
wx.EVT_CHECKBOX(self, cb.GetId(), self._count_selected_data)
if data_count <= MAX_NBR_DATA:
cb.SetValue(True)
else:
cb.SetValue(False)
self.list_of_ctrl.append((cb, data_list[i]))
self._choice_sizer.Add(cb, (iy, ix),
(1, 1), wx.LEFT | wx.EXPAND | wx.ADJUST_MINSIZE, 15)
iy += 1
self._panel.SetSizer(self._choice_sizer)
#add sizer
self._sizer_button.Add((20, 20), 1, wx.EXPAND | wx.ADJUST_MINSIZE, 0)
button_cancel = wx.Button(self, wx.ID_CANCEL, "Cancel")
self._sizer_button.Add(button_cancel, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
button_OK = wx.Button(self, wx.ID_OK, "Ok")
button_OK.SetFocus()
self._sizer_button.Add(button_OK, 0,
wx.LEFT | wx.RIGHT | wx.ADJUST_MINSIZE, 10)
static_line = wx.StaticLine(self, wx.ID_ANY)
self._sizer_txt.Add(self._panel, 0, wx.EXPAND | wx.ALL, 10)
self._sizer_main.Add(self._sizer_txt, 0, wx.EXPAND | wx.ALL, 10)
self._sizer_main.Add(self._data_text_ctrl, 0, wx.EXPAND | wx.ALL, 10)
self._sizer_main.Add(static_line, 0, wx.EXPAND, 0)
self._sizer_main.Add(self._sizer_button, 0, wx.EXPAND | wx.ALL, 10)
self.SetSizer(self._sizer_main)
self.Layout()
def get_data(self):
"""
return the selected data
"""
temp = []
for item in self.list_of_ctrl:
cb, data = item
if cb.GetValue():
temp.append(data)
return temp
def _count_selected_data(self, event):
"""
count selected data
"""
if event.GetEventObject().GetValue():
self._nb_selected_data += 1
else:
self._nb_selected_data -= 1
select_data_text = " %s Data selected.\n" % str(self._nb_selected_data)
self._data_text_ctrl.SetLabel(select_data_text)
if self._nb_selected_data <= self._max_data:
self._data_text_ctrl.SetForegroundColour('blue')
else:
self._data_text_ctrl.SetForegroundColour('red')
| 188 | 39.05 | 86 | 16 | 1,916 | python | [] | 0 | true | |
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} | 2.390625 | stackv2 | # -*- coding: utf-8 -*-
import inspect
from functools import partial
import boto3
from django.conf import settings
__author__ = 'dreipol GmbH'
class Decorator(object):
def __init__(self, function, *args, **kwargs):
self.function = function
@property
def original_function(self):
function = self.function
while isinstance(function, Decorator):
function = function.function
return function
class DefaultConnection(Decorator):
def __get__(self, obj, objtype=None):
if obj is None:
return self.function
return partial(self, obj)
def __call__(self, *args, **kwargs):
connection_keyword = 'connection'
if len(args) + len(kwargs) == 0:
call_kwargs = dict()
else:
call_kwargs = inspect.getcallargs(
self.original_function,
*args,
**kwargs
)
if not call_kwargs.get(connection_keyword, None):
call_kwargs[connection_keyword] = get_sns_connection()
return self.function(**call_kwargs)
class PushLogger(Decorator):
def __get__(self, obj, objtype=None):
if obj is None:
return self.function
self.obj = obj
return partial(self, obj)
def __call__(self, *args, **kwargs):
call_kwargs = inspect.getcallargs(self.original_function, *args,
**kwargs)
push_message = call_kwargs.get('push_message')
self.obj.sign(push_message)
if logging_enabled():
push_message.save()
return self.function(*args, **kwargs)
def get_sns_connection():
"""
Creates a new AWS connection based upon the credentials defined in the django configuration
:param region: the region of the DynamoDB, defaults to Ireland
:return: a new dynamodb2 connection
"""
region = settings.SCARFACE_REGION_NAME if hasattr(settings, "SCARFACE_REGION_NAME") else 'eu-west-1'
return boto3.client('sns',
region,
aws_access_key_id=settings.AWS_ACCESS_KEY,
aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY
)
def logging_enabled():
return settings.SCARFACE_LOGGING_ENABLED if hasattr(
settings,
'SCARFACE_LOGGING_ENABLED'
) else True
| 81 | 28.26 | 104 | 14 | 491 | python | [] | 0 | true | |
2024-11-19T01:54:52.540790+00:00 | 1,620,772,291,000 | 8d6fd962bec5fe715518ec83e07c0ac80c4f890f | 3 | {
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} | 2.78125 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 28 15:13:31 2019
@author: peter
***
This program requires ffmpeg be installed in anaconda
To install, run the following command in your anaconda terminal
conda install -c conda-forge ffmpeg
***
"""
import h5py
import numpy as np
import matplotlib
#matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.animation import FFMpegWriter
def pickTimeInds(pick_time, timearr):
output = np.zeros(pick_time.shape, dtype=np.int32)
for i, t in enumerate(pick_time):
ti = np.argmin(np.abs(timearr - t))
output[i] = ti
return output
def simpleMovie(inds, x, y, arr, savefile, contour=False, cmap=None):
dpi =600
if cmap is None:
cmap = 'jet'
nframes = len(inds)
extent = [x[0], x[-1], y[0], y[-1]]
data = arr[inds, :, :]
vmin = np.min(data)
vmax = np.max(data)
metadata = dict(title='Movie test', artist="uclahedp", comment='comment')
writer = FFMpegWriter(fps=15, metadata=metadata)
fig = plt.figure(figsize=(6,6))
x = np.squeeze(arr[inds[0],:,:])
cplot = plt.imshow(x, cmap=cmap, aspect='auto',
vmin=vmin, vmax=vmax,
origin='lower', extent=extent)
with writer.saving(fig, savefile, dpi):
for i in range(nframes):
print("Frame: " + str(i))
x = np.squeeze(arr[inds[i],:,:])
cplot.set_data(x)
writer.grab_frame()
if __name__ == '__main__':
f = '/Volumes/PVH_DATA/LAPD_Jan2019/FULL/run18_LAPD_C6_full.hdf5'
savefile = '/Volumes/PVH_DATA/LAPD_Jan2019/FULL/simpleMovie.mp4'
with h5py.File(f, 'r') as f:
x = f['xaxis'][:]
y = f['yaxis']
timearr = f['time'][:]
arr = f['data'][:,:,:,0,0,2]
times = np.linspace(0, 2, num=60)
tinds = pickTimeInds(times, timearr*1e6)
simpleMovie(tinds, x, y, arr, savefile)
| 85 | 23.24 | 77 | 14 | 603 | python | [] | 0 | true | |
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} | 3.109375 | stackv2 | #!/usr/bin/python
# hat.py
#
# Script to grab one set of serial data and log it to a sql database
# Requires a database and table to be set up already for the data
#
# Sources:
# pyserial.readthedoc.io/en/latest/pyserial_api.html
# pyserial.readthedoc.io/en/latest/shortintro.html
# https://docs.python.org/3/library/sqlite3.html
#
# Written by Josh Andrews 4/27/18
import serial
import time
import datetime
import sqlite3 as sq
# list to hold all data going to sql database
sqldat = []
# Get date and time in YYYY-MM-DD HH:MM:SS
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sqldat.append(timestamp)
# connect to the expansion hat and write to it
# (any write returns all sensor values).
ser = serial.Serial('/dev/ttyAMA0', 9600, timeout=0.5)
ser.write(b'1')
# while we have data to read, read it and store in list as a float.
# currently uses the timeout to breakout of loop if no errors.
# need to look more at documentation on EOL with readline()
while True:
try:
ser_bytes = ser.readline()
info = float(ser_bytes[0:len(ser_bytes)-2].decode("utf-8"))
sqldat.append(info)
except:
break
# use tuple form to insert data into sql
sqldat = tuple(sqldat,)
#open/create sql database, use full path to make crontab happy.
conn = sq.connect('/var/www/html/sensor_data.db')
c = conn.cursor()
# insert our sensor information to database
c.execute('''INSERT INTO sens_data VALUES (?,(?*1.8)+32,?,?,?,?,((?/100)*1.8)+32,?/10000,?/1000)''',sqldat)
# delete data over 24 hours old. Wouldn't want to actually do this in a
# real project, just have the php file serve up whats needed so we keep a
# record of historical sensor information.
c.execute('''DELETE FROM sens_data WHERE timestamp <= datetime('now','-24 hours','localtime')''')
# commit the changes and close the database
conn.commit()
conn.close()
| 61 | 29.82 | 107 | 17 | 519 | python | [] | 0 | true | |
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"visit_date": "2021-02-12T14:24:28.344682"
} | 2.859375 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Vinícius Madureira"
__copyright__ = "Copyright 2020, Vinícius Madureira"
__license__ = "Creative Commons Zero 1.0 Universal"
__version__ = "0.01a"
__maintainer__ = "Vinícius Madureira"
__email__ = "viniciusmadureira@outlook.com"
__status__ = "Testing"
from collections import OrderedDict
from model.category import Category
from controller.sqlite import SQLite
from sqlite3 import Error
"""
CategoryData class: Controller to add, delete and update Product type objects in static product's dictionary (OrderedDict).
"""
class CategoryData:
categories = OrderedDict()
@staticmethod
def add(category: Category):
if not CategoryData.exists(category.name) and category.isValid():
CategoryData.categories[category.name] = category
return True
return False
@staticmethod
def delete(categoryName: str):
if CategoryData.exists(categoryName):
CategoryData.categories.pop(categoryName)
return True
return False
@staticmethod
def update(category: Category, category_name: str):
if CategoryData.exists(categoryName):
CategoryData.products[category.name] = CategoryData.categories.pop(categoryName)
return True
return False
@staticmethod
def exists(categoryName: str):
return categoryName in CategoryData.categories.keys()
@staticmethod
def loadCategories():
sqlite = SQLite(SQLite.PATH)
CategoryData.categories = OrderedDict()
try:
result = sqlite.select("SELECT categories.id, categories.name, categories.description FROM categories ORDER BY categories.name;")
if result:
CategoryData.categories = result
except Error as e:
print("SQLite Error. Message: {}.".format(str(e)))
return CategoryData.categories
| 60 | 31.28 | 141 | 17 | 403 | python | [] | 0 | true | |
2024-11-19T01:54:54.125749+00:00 | 1,552,719,922,000 | 1fe4a523e8be6fd6b34630afb3237a9889bd6654 | 3 | {
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"visit_date": "2020-04-05T04:59:02.426159"
} | 3 | stackv2 | '''
K-means Clustering (from scratch) with elbow method selection
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import euclidean
import common.utils as ut
# get data from:
# https://www.kaggle.com/c/otto-group-product-classification-challenge
TRN_DATA_PATH = 'datasets/otto-group-product-classification/train.csv'
NUM_SAMPLES = 2000
COLOR_POOL = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet',
'brown', 'gold', 'black', 'pink', 'tan', 'lime', 'magenta',
'teal', 'lavender', 'khaki', 'aqua', 'fuchsia', 'ivory']
def visualize_data(feats, labels, ca):
'''Display labeled data and clustered data
'''
print("Visualizing data...")
red_feats = ut.reduce_to_2D_by_tsne(feats)
label2col_map = ['red', 'orange', 'yellow', 'green', 'blue',
'violet', 'brown', 'gray', 'pink']
label_list = np.unique(labels)
cluster_list = np.unique(ca)
fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
for label in label_list:
# get samples with label == label
idxs = np.where(labels == label)
# get components
pc1, pc2 = red_feats['pc1'].values[idxs], red_feats['pc2'].values[idxs]
# scatter plot w/ color based on labels
ax[0].scatter(x=pc1, y=pc2, color=label2col_map[label-1],
alpha=0.5, label=label)
ax[0].set_xlabel('PC1')
ax[0].set_ylabel('PC2')
ax[0].set_title('Labeled Products')
for i, cluster in enumerate(cluster_list):
# get samples assigned to cluster
idxs = np.where(ca == cluster)
# get components
pc1, pc2 = red_feats['pc1'].values[idxs], red_feats['pc2'].values[idxs]
# scatter plot w/ color based on cluster
ax[1].scatter(x=pc1, y=pc2, color=COLOR_POOL[i],
alpha=0.5, label=cluster)
ax[1].set_xlabel('PC1')
ax[1].set_ylabel('PC2')
ax[1].set_title('Grouped Products')
for graph in ax:
graph.legend() # show legend
graph.grid(True) # show gridlines
plt.show()
def calculate_cost(X, u):
'''
Calculate k-means distortion/cost
X- samples assigned to cluster
u - cluster mean
'''
cost = 0
cost += np.sum([(euclidean(x, u)**2) for x in X])
return cost
def k_means(k, X, max_iter=100, print_every_iter=5):
'''
K-means from scratch
k - no. of clusters
X - samples
'''
if k > len(X):
raise Exception("No. of clusters > no. of samples!")
# randomly initialize means
U = X[np.random.choice(range(len(X)), size=k, replace=True)]
# initialize cluster array
c = np.array([-1] * len(X))
for j in range(max_iter):
U_prev = np.array(U)
# cluster assignment
for i, x in enumerate(X):
u_dists = [euclidean(x, u)
for u in U]
c[i] = np.argmin(u_dists)
# update means
cost = 0
for k_idx in range(k):
c_xs = X[np.where(c == k_idx)]
if len(c_xs) == 0:
# re-initialize mean/cluster
U[k_idx] = X[np.random.choice(range(len(X)))]
else:
U[k_idx] = np.mean(c_xs)
# get cost
cost += calculate_cost(c_xs, U[k_idx])
cost /= len(X)
if (j+1) % print_every_iter == 0:
print("k = %d. Iter %d. Cost: %.6f" % (k, j+1, cost))
# if no update, then minima is already found
if np.array_equal(U, U_prev):
break
return c, cost
def show_elbow(K, costs):
'''Graph "elbow" for selection of k
'''
plt.subplots(figsize=(8, 5))
plt.plot(K, costs, 'o-')
plt.xticks(K)
plt.grid(True)
plt.xlabel('K', fontsize=15)
plt.ylabel('Cost', fontsize=15)
plt.title('Elbow method')
plt.show()
def main():
'''Main
'''
feats, labels = ut.get_data_from_csv(TRN_DATA_PATH)
if NUM_SAMPLES < len(feats):
feats, labels = ut.sample(feats, labels, NUM_SAMPLES)
tries = 5
min_k = 3
max_k = 12
K = list(range(min_k, max_k + 1))
K_ca = []
K_costs = []
for k in K:
best_cost = 0.0
best_ca = None
for t in range(tries):
print("-- Trial %d --" % (t+1))
# get cluster assignment and cost
ca, cost = k_means(k=k, X=feats.values)
if t == 0:
best_cost = cost
best_ca = ca
else:
if cost < best_cost:
best_cost = cost
best_ca = ca
K_costs.append(best_cost)
K_ca.append(best_ca)
# elbow method to select k
show_elbow(K, K_costs)
# get input
try:
selected_k = int(input('Choose k (range: %d - %d): ' % (min_k, max_k)))
if k < min_k or k > max_k:
raise ValueError
except ValueError:
print('Invalid k given!')
return 1
idx = K.index(selected_k)
ca = K_ca[idx]
print("Selected k: %d. Final Cost: %.6f" % (selected_k, K_costs[idx]))
# visualize results
visualize_data(feats, labels, ca)
return 0
if __name__ == "__main__":
main()
| 186 | 27.04 | 79 | 19 | 1,446 | python | [] | 0 | true | |
2024-11-19T01:54:54.232492+00:00 | 1,485,783,244,000 | 5adbe92b88cfa34c3d5d5bef206e82f6500e50b2 | 3 | {
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} | 2.9375 | stackv2 | import re
import datetime
import dateparser
from .exceptions import SearchSyntaxError
units = {
'd': datetime.timedelta(days=1),
'h': datetime.timedelta(hours=1),
}
def parse_date(value):
match = re.match(r"^([\-\+])(\d+)([{}])".format(''.join(units)), value)
if not match:
returned = dateparser.parse(value)
if returned is None:
raise SearchSyntaxError('Invalid date provided: {!r}'.format(value))
return returned.timestamp()
returned = datetime.datetime.now()
sign, num, unit = match.groups()
num = int(num)
diff = num * units[unit]
if sign == '-':
returned -= diff
else:
returned += diff
return returned.timestamp()
| 32 | 21.59 | 80 | 14 | 169 | python | [] | 0 | true | |
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} | 2.8125 | stackv2 | import matplotlib.pyplot as plt
import numpy as np
import csv
import scipy.special as sc
def binary_entropy(x):
return -(sc.xlogy(x, x) + sc.xlog1py(1 - x, -x))/np.log(2)
plt.rcParams.update({'font.size': 20})
with open('dcache', newline='\n') as csvfile:
ic = list(csv.reader(csvfile))
icx = [float(x[0]) for x in ic]
icy = [float(x[1]) for x in ic]
icap = []
for i in range(len(icx)):
cap = icx[i] * (1 - binary_entropy(icy[i]))
icap.append(cap)
#fig, ax2 = plt.subplots(1,1,figsize=(15,5))
file = 'bitrate.pdf'
#fig.suptitle('Error rate - Throughput')
#fig.text(0.99, 0.5, 'Capacity (bps)', va='center', rotation='vertical', fontsize=30)
plt.plot(icx, icy, 'o', color='black')
plt.plot(np.unique(icx), np.poly1d(np.polyfit(icx, icy, 6))(np.unique(icx)), color='black')
plt.title('(b) D-Cache', fontsize=30)
plt.xlabel('bit rate (bps)')
plt.ylabel('Bit Error Probability')
#plt.ylim(0,0.4)
#plt.yaxis.grid()
#plt.yaxis.set_major_locator(plt.MaxNLocator(2))
plt.tight_layout()
plt.show()
#plt.savefig(file, bbox_inches='tight')
| 34 | 30.09 | 91 | 12 | 340 | python | [] | 0 | true | |
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"visit_date": "2020-03-22T12:15:36.471557"
} | 2.8125 | stackv2 | """Script that checks for changes uploaded to the database by Tom through
FileZilla and updates the master sheet to be the new sheet. Then, it alters the
changelog.html file to reflect the changes. Uses pandas for the table changes
and Beautiful Soup 4 for the html changes. Returns False if no changes are made
and True if changes are made.
"""
import pandas as pd
import os
import bs4
import sys
import xlrd
import tableManager
__author__ = "Robbie Freeman"
__credits__ = ["Thomas Duffy"]
__maintainer__ = "Robbie Freeman"
__email__ = "robbie.a.freeman@gmail.com"
__status__ = "Development"
def getSheetNames():
xl = pd.ExcelFile("static/downloads/single-crystal_db_complete.xlsx")
xlNames = []
for s in xl.sheet_names:
if not "Refs" in s and not "Key" in s:
xlNames.append(s)
return xlNames
def main():
''' Commented out for bug testing, and this is an unimplemented feature
# Take in a new excel sheet. Read in the data using Pandas. Make it the new
# master sheet
if not os.path.isfile("../static/downloads/newSheet.xlsx") :
return False
newData = tableManager.getInitialTables("static/downloads/newSheet.xlsx")
currentData = tableManager.getInitialTables()
# Check for validity. Return the error if invalid. TODO expand checks
assert(len(newData) == len(currentData))
# Compare the new data with the data already in the master sheet to determine
# the nature of the changes TODO implement
changes = []
if len(newData.index) == len(currentData.index):
changes.append("Updated current minerals")
elif len(newData.index) > len(currentData.index):
changes.append("Added minerals")
elif len(newData.index) < len(currentData.index):
changes.append("Removed minerals")
'''
tables = tableManager.getInitialTables(asOne=False)
# Create the CSV file from the original, master excel sheet
'''if os.path.isfile('static/downloads/single-crystal_db_complete.csv') :
os.remove("static/downloads/single-crystal_db_complete.csv")
tables.to_csv('static/downloads/single-crystal_db_complete.csv', index=False) #TODO fix '''
# Find the mineral group and structure categories and log them in mineralCats.txt
# for generating search.html
# Scan through each row looking for unique mineral classes and unique structures
# within each
lastLabel = ''
categories = [] # list of lists of tuples of the form (class, structure)
classdf = pd.DataFrame(columns=tables[0].columns)
sheetNames = getSheetNames()
for i in range(len(tables)):
print(sheetNames[i])
category = []
for index, row in tables[i].iterrows(): # for each of the rows
rowdf = row.to_frame() # Dataframe of row, which is normally a Series
lastLabel = lastLabel.replace(" "," ") # account for spaces in classes (NOT structures)
if isinstance(row['Structure/SG'], str):
structure = row['Structure/SG'].split(',')[0]
# if it's a row with a mineral class label
if not (pd.isnull(row['Name'])) and pd.isnull(row['Composition']) and lastLabel != row['Name']:
lastLabel = row['Name']
# if it's a row following a label but not a label itself
elif lastLabel != '' and (lastLabel, structure) not in category:
print((lastLabel, structure))
category.append((lastLabel, structure))
categories.append(category)
if os.path.isfile("static/text/categories.txt") :
os.remove("static/text/categories.txt")
orig_stdout = sys.stdout
file = open('static/text/categories.txt', 'w')
sys.stdout = file
for i in range(len(tables)):
print(sheetNames[i], end='')
print(categories[i], end='')
sys.stdout = orig_stdout
file.close()
'''# Delete the master and use the new sheet as the current master
os.remove("static/downloads/single-crystal_db.xlsx")
os.remove("static/downloads/single-crystal_db.csv")
os.rename("static/downloads/newSheet.xlsx", "static/downloads/single-crystal_db.xlsx")
newData.to_csv("static/downloads/single-crystal_db.csv")
os.remove("static/downloads/newSheet.xlsx")
# Log the changes
changelog(changes) '''
# Update the entries.html page (or at least the string that holds its data)
sys.path.insert(0, 'static/py')
import allEntries
allEntries.main()
# return True, as changes were successfully made
return True
| 111 | 39.91 | 107 | 17 | 1,024 | python | [] | 0 | true | |
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} | 2.4375 | stackv2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from config import Config
from data_loader import BehavioralDataset
from models import BehavioralModel
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
def load_data(dataset):
triple_x_train, y_train = dataset.load_data()
triple_x_train, triple_x_val, y_train, y_val = train_test_split(
triple_x_train, y_train, test_size=0.2, random_state=42
)
return triple_x_train, triple_x_val, y_train, y_val
def plot_history(history):
train_loss = history.history["loss"]
val_loss = history.history["val_loss"]
if "acc" in history.history:
train_acc = history.history["acc"]
val_acc = history.history["val_acc"]
epoch_nums = len(train_loss)
if "acc" in history.history:
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(10, 10))
loss_ax = ax[0]
else:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
loss_ax = ax
loss_ax.set_xlabel("epochs")
loss_ax.set_ylabel("loss")
loss_ax.set_title("loss")
loss_ax.plot(range(0, epoch_nums), train_loss, label="train loss")
loss_ax.plot(range(0, epoch_nums), val_loss, label="val loss")
loss_ax.legend()
if "acc" in history.history:
ax[1].set_xlabel("epochs")
ax[1].set_ylabel("val")
ax[1].set_title("accuracy")
ax[1].plot(range(0, epoch_nums), train_acc, label="train acc")
ax[1].plot(range(0, epoch_nums), val_acc, label="val acc")
ax[1].legend()
plt.suptitle("train-val logs")
# plt.show()
plt.savefig("train_val_logs.png")
def main():
dt_config = Config()
dataset = BehavioralDataset(path_to_data=dt_config.DATA_PATH)
triple_x_train, triple_x_val, y_train, y_val = load_data(dataset)
model = BehavioralModel(input_shape=(160, 320, 3))
callbacks = [
ModelCheckpoint(
os.path.join(dt_config.SAVED_MODELS_PATH, "model-{epoch:03d}.h5"),
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto",
period=1,
),
ReduceLROnPlateau(monitor="val_loss", factor=0.1, patience=7, verbose=0, mode="auto", min_lr=0.00001),
EarlyStopping(monitor="val_loss", min_delta=0, patience=15, verbose=0, mode="auto"),
]
history = model.fit_generator(
batch_generator(triple_x_train, y_train, dt_config.BATCH_SIZE),
epochs=dt_config.EPOCHS,
steps_per_epoch=len(triple_x_train) * 6 // dt_config.BATCH_SIZE + 1,
validation_data=batch_generator(triple_x_val, y_val, dt_config.BATCH_SIZE, False),
validation_steps=(len(triple_x_val) - 1) // dt_config.BATCH_SIZE + 1,
callbacks=callbacks,
)
if __name__ == "__main__":
main()
| 85 | 33.55 | 110 | 15 | 775 | python | [] | 0 | true | |
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} | 2.53125 | stackv2 | from ..core._geopandas import from_geopandas
from ._ressources import read_remote_shapefile
REPR = """
AR6 reference regions - Iturbide et al., 2020
Attributes
----------
all : Regions
All regions (land + ocean).
land : Regions
Land regions only
ocean : Regions
Ocean regions only
"""
class ar6_cls:
"""docstring for ar6"""
def __init__(self):
self._df = None
self._all = None
self._land = None
self._ocean = None
self._name = "AR6 reference regions"
self._source = "Iturbide et al., 2020 (Earth Syst. Sci. Data)"
@property
def df(self):
if self._df is None:
self._df = read_remote_shapefile("IPCC-WGI-reference-regions-v4.zip")
return self._df
@property
def all(self):
if self._all is None:
self._all = from_geopandas(
self.df,
names="Name",
abbrevs="Acronym",
name=self._name,
source=self._source,
overlap=False,
)
return self._all
@property
def land(self):
if self._land is None:
land = self.df.Type.str.contains("Land")
self._land = from_geopandas(
self.df.loc[land],
names="Name",
abbrevs="Acronym",
name=self._name + " (land only)",
source=self._source,
overlap=False,
)
return self._land
@property
def ocean(self):
if self._ocean is None:
ocean = self.df.Type.str.contains("Ocean")
self._ocean = from_geopandas(
self.df.loc[ocean],
names="Name",
abbrevs="Acronym",
name=self._name + " (ocean only)",
source=self._source,
overlap=False,
)
return self._ocean
def __repr__(self): # pragma: no cover
return REPR
ar6 = ar6_cls()
| 93 | 20.82 | 81 | 16 | 478 | python | [] | 0 | true | |
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"visit_date": "2022-10-16T23:33:18.045620"
} | 2.71875 | stackv2 | # Copyright 2020 The Keras CV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://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 tensorflow as tf
from .anchor_generator import AnchorGenerator
class MultiScaleAnchorGenerator(tf.keras.layers.Layer):
"""Defines a MultiScaleAnchorGenerator that generates anchor boxes for multiple feature maps.
# Attributes:
image_size: A list/tuple of 2 ints, the 1st represents the image height, the 2nd image width.
scales: A list/tuple of list/tuple of positive floats (usually less than 1.) as a fraction to shorter
side of `image_size`. It represents the base anchor size (when aspect ratio is 1.).
For example, if `image_size=(300, 200)`, and `scales=[[.1]]`, then the base anchor size is 20.
If `image_size=(300, 200)` and `scales=[[.1], [.2]]`, then the base anchor sizes are 20 and 40.
aspect_ratios: a list/tuple of list/tuple of positive floats representing the ratio of anchor width
to anchor height. **Must** have the same length as `scales`.
For example, if `image_size=(300, 200)`, `scales=[[.1]]`, and `aspect_ratios=[[.64]]`, the base anchor
size is 20, then anchor height is 25 and anchor width is 16. If `image_size=(300, 200)`,
`scales=[[.1], [.2]]`, and `aspect_ratios=[[.64], [.1]]`, the base anchor size is 20 and 40, then
the anchor heights are 25 and 40, the anchor widths are 16 and 40.
The anchor aspect ratio is independent to the original aspect ratio of image size.
strides: A list/tuple of list/tuple of 2 ints or floats representing the distance between anchor
points. For example, `stride=[(30, 40)]` means each anchor is separated by 30 pixels in height,
and 40 pixels in width. Defaults to `None`, where anchor stride would be calculated as
`min(image_height, image_width) / feature_map_height` and
`min(image_height, image_width) / feature_map_width` for each feature map.
offsets: A list/tuple of list/tuple of 2 floats between [0., 1.] representing the center of anchor
points relative to the upper-left border of each feature map cell. Defaults to `None`, which is the
center of each feature map cell when `strides=None`, or center of each anchor stride otherwise.
clip_boxes: Boolean to represents whether the anchor coordinates should be clipped to the image size.
Defaults to `True`.
normalize_coordinates: Boolean to represents whether the anchor coordinates should be normalized to [0., 1.]
with respect to the image size. Defaults to `True`.
"""
def __init__(
self,
image_size,
scales,
aspect_ratios,
strides=None,
offsets=None,
clip_boxes=True,
normalize_coordinates=True,
name=None,
**kwargs
):
self.image_size = image_size
self.image_height = image_size[0]
self.image_width = image_size[1]
self.scales = scales
self.aspect_ratios = aspect_ratios
if strides is None:
strides = [None] * len(scales)
if offsets is None:
offsets = [None] * len(scales)
self.strides = strides
self.offsets = offsets
self.clip_boxes = clip_boxes
self.normalize_coordinates = normalize_coordinates
self.anchor_generators = []
for (i, (scale_list, aspect_ratio_list, stride, offset)) in enumerate(
zip(scales, aspect_ratios, strides, offsets)
):
self.anchor_generators.append(
AnchorGenerator(
image_size,
scales=scale_list,
aspect_ratios=aspect_ratio_list,
stride=stride,
offset=offset,
clip_boxes=clip_boxes,
normalize_coordinates=normalize_coordinates,
name="anchor_generator_" + str(i),
)
)
super(MultiScaleAnchorGenerator, self).__init__(name=name, **kwargs)
def call(self, feature_map_sizes):
result = []
for feature_map_size, anchor_generator in zip(
feature_map_sizes, self.anchor_generators
):
anchors = anchor_generator(feature_map_size)
anchors = tf.reshape(anchors, (-1, 4))
result.append(anchors)
return tf.concat(result, axis=0)
def get_config(self):
config = {
"image_size": self.image_size,
"scales": self.scales,
"aspect_ratios": self.aspect_ratios,
"strides": self.strides,
"offsets": self.offsets,
"clip_boxes": self.clip_boxes,
"normalize_coordinates": self.normalize_coordinates,
}
base_config = super(MultiScaleAnchorGenerator, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
| 114 | 48.06 | 120 | 17 | 1,246 | python | [] | 0 | true | |
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} | 2.609375 | stackv2 | import sqlite3
def connect(func):
def func_wrapper(*args, **kwargs):
conn = sqlite3.connect("myDB.db")
return func(*args, **kwargs)
conn.close()
return func_wrapper
#TODO check and edit function
@connect
def createUsers(conn):
try:
conn.execute('''CREATE TABLE USERS
(username text, hashresult text, salt text, role text)''')
conn.commit()
except(sqlite3.OperationalError):
print('')
@connect
def createSites(conn):
try:
conn.execute('''CREATE TABLE SITES
(siteid text, siteurl text)''')
conn.commit()
except(sqlite3.OperationalError):
print('')
@connect
def createVoteReport(conn):
try:
conn.execute('''CREATE TABLE VOTEREPORT
(username text, first text, second text, third text)''')
conn.commit()
except(sqlite3.OperationalError):
print('')
#TODO check and edit function
@connect
def addUser(conn, username, hashresult, salt, role):
c = conn.cursor()
t = (username,)
c.execute('SELECT * FROM USERS WHERE username=?', t)
data = c.fetchone()
if (data is None):
params = (username, hashresult, salt, role)
c.execute("INSERT INTO USERS VALUES (?, ?, ?, ?)", params)
conn.commit()
return True
else:
return False
@connect
def addSite(conn, siteid, siteurl):
c = conn.cursor()
t = (siteid,)
c.execute('SELECT * FROM SITES WHERE siteid=?', t)
data = c.fetchone()
if (data is None):
params = (siteid, siteurl)
c.execute("INSERT INTO SITES VALUES (?, ?)", params)
conn.commit()
return True
else:
return False
@connect
def addVote(conn, username, first, second, third):
c = conn.cursor()
t = (username,)
c.execute('SELECT * FROM VOTEREPORT WHERE username=?', t)
data = c.fetchone()
if (data is None):
params = (username, first, second, third)
c.execute("INSERT INTO VOTEREPORT VALUES (?, ?, ?, ?)", params)
conn.commit()
return True
else:
return False
| 79 | 25.62 | 71 | 12 | 518 | python | [] | 0 | true | |
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} | 2.75 | stackv2 | # -*- coding: utf-8 -*-
"""
定义一些任务的执行操作，将具体的操作从tasks.py里面抽离出来
每个任务需要饮用的模块放到函数里面引用，方便单独调试函数
"""
import requests
def get_link_status(url):
"""
请求地址，返回请求状态和内容
@param url:
@return:
"""
try:
resp = requests.get(url, timeout=5, verify=False)
except Exception:
return 500, '请求超时'
return resp.status_code, resp.text
def white_list_check(lis, string):
"""
校验一个字符串是否包含一个列表中任意一个元素
@param lis:
@param string:
@return: bool
"""
for each in lis:
if each in string:
return True
return False
def action_update_article_cache():
"""
更新所有文章的缓存，缓存格式跟文章视图保持一致
@return:
"""
from markdown import Markdown
from markdown.extensions.toc import TocExtension # 锚点的拓展
from markdown.extensions.codehilite import CodeHiliteExtension
from django.core.cache import cache
from django.utils.text import slugify
from blog.utils import CustomHtmlFormatter
from blog.models import Article
total_num, done_num = 0, 0
# 查询到所有缓存的key
keys = cache.keys('article:markdown:*')
for obj in Article.objects.all():
total_num += 1
ud = obj.update_date.strftime("%Y%m%d%H%M%S")
md_key = f'article:markdown:{obj.id}:{ud}'
# 设置不存在的缓存
if md_key not in keys:
md = Markdown(extensions=[
'markdown.extensions.extra',
'markdown_checklist.extension',
CodeHiliteExtension(pygments_formatter=CustomHtmlFormatter),
TocExtension(slugify=slugify),
])
# 设置过期时间的时候分散时间，不要设置成同一时间
cache.set(md_key, (md.convert(obj.body), md.toc), 3600 * 24 + 10 * done_num)
done_num += 1
data = {'total': total_num, 'done': done_num}
return data
def action_check_friend_links(site_link=None, white_list=None):
"""
检查友链:
1、检查当前显示的友链，请求友链，将非200的友链标记为不显示，并记录禁用原因
2、检查当前不显示的友链，请求友链，将200返回的标记为显示，并删除禁用原因
3、新增补充校验：可以添加参数site_link，则不仅仅校验网页是否打开200，还会校验网站中是否有site_link外链
@return:
"""
import re
from blog.models import FriendLink
white_list = white_list or [] # 设置白名单，不校验
active_num = 0
to_not_show = 0
to_show = 0
active_friend_list = FriendLink.objects.filter(is_active=True)
for active_friend in active_friend_list:
active_num += 1
if active_friend.name in white_list:
continue
if active_friend.is_show is True:
code, text = get_link_status(active_friend.link)
if code != 200:
active_friend.is_show = False
active_friend.not_show_reason = f'网页请求返回{code}'
active_friend.save(update_fields=['is_show', 'not_show_reason'])
to_not_show += 1
else:
# 设置了网站参数则校验友链中是否包含本站外链
if site_link:
site_check_result = re.findall(site_link, text)
if not site_check_result:
active_friend.is_show = False
active_friend.not_show_reason = f'网站未设置本站外链'
active_friend.save(update_fields=['is_show', 'not_show_reason'])
to_not_show += 1
else:
code, text = get_link_status(active_friend.link)
if code == 200:
if not site_link:
active_friend.is_show = True
active_friend.not_show_reason = ''
active_friend.save(update_fields=['is_show', 'not_show_reason'])
to_show += 1
else:
site_check_result = re.findall(site_link, text)
if site_check_result:
active_friend.is_show = True
active_friend.not_show_reason = ''
active_friend.save(update_fields=['is_show', 'not_show_reason'])
to_show += 1
data = {'active_num': active_num, 'to_not_show': to_not_show, 'to_show': to_show}
return data
def action_clear_notification(day=200, is_read=True):
"""
清理消息推送
@param is_read: False表示清理所有，True表示只清理已读，默认清理已读
@param day: 清理day天前的信息
@return:
"""
from datetime import datetime, timedelta
from django.db.models import Q
from comment.models import Notification, SystemNotification
current_date = datetime.now()
delta = timedelta(days=day)
past_date = current_date - delta
if is_read is True:
query = Q(create_date__lte=past_date, is_read=True)
else:
query = Q(create_date__lte=past_date)
comment_notification_objects = Notification.objects.filter(query)
system_notification_objects = SystemNotification.objects.filter(query)
comment_num = comment_notification_objects.count()
system_num = system_notification_objects.count()
comment_notification_objects.delete()
system_notification_objects.delete()
return {'comment_num': comment_num, 'system_num': system_num}
def action_cleanup_task_result(day=3):
"""
清理任务结果
清理day天前成功或结束的，其他状态的一概不清理
@return:
"""
from datetime import datetime, timedelta
from django.db.models import Q
from django_celery_results.models import TaskResult
current_date = datetime.now()
delta = timedelta(days=day)
past_date = current_date - delta
query = Q(date_done__lte=past_date)
task_result_objects = TaskResult.objects.filter(query)
task_result_count = task_result_objects.count()
task_result_objects.delete()
return {'task_result_count': task_result_count}
def action_baidu_push(baidu_url, months):
"""
主动推送文章地址到百度，指定推送最近months月的文章链接
@param baidu_url: 百度接口调用地址，包含token
@param months: 几个月内的文章
@return:
"""
import requests
from datetime import datetime
from dateutil.relativedelta import relativedelta
from blog.models import Article
from blog.utils import site_full_url
def baidu_push(urls):
headers = {
'User-Agent': 'curl/7.12.1',
'Host': 'data.zz.baidu.com',
'Content-Type': 'text/plain',
'Content-Length': '83'
}
try:
response = requests.post(baidu_url, headers=headers, data=urls, timeout=5)
return True, response.json()
except Exception as e:
return False, e
current_date = datetime.now()
previous_date = current_date - relativedelta(months=months)
article_list = Article.objects.filter(create_date__gte=previous_date, is_publish=True)
article_count = article_list.count()
if not article_count:
return {'article_count': article_count, 'status': True, 'result': 'ignore'}
url_list = [f'{site_full_url()}{each.get_absolute_url()}' for each in article_list]
status, result = baidu_push('\n'.join(url_list))
return {'article_count': article_count, 'status': status, 'result': result}
def action_check_site_links(white_domain_list=None):
"""
校验导航网站有效性，只校验状态为True或者False的，为空的不校验，所以特殊地址可以设置成空跳过校验
@param white_domain_list: 域名白名单
@return:
"""
from webstack.models import NavigationSite
white_domain_list = white_domain_list or []
active_num = 0
to_not_show = 0
to_show = 0
active_site_list = NavigationSite.objects.filter(is_show__isnull=False)
for site in active_site_list:
active_num += 1
# 当站点包含白名单域名则直接跳过校验
if white_list_check(white_domain_list, site.link):
continue
if site.is_show is True:
code, text = get_link_status(site.link)
if code < 200 or code >= 400:
site.is_show = False
site.not_show_reason = f'网页请求返回{code}'
site.save(update_fields=['is_show', 'not_show_reason'])
to_not_show += 1
else:
code, text = get_link_status(site.link)
if 200 <= code < 400:
site.is_show = True
site.not_show_reason = ''
site.save(update_fields=['is_show', 'not_show_reason'])
to_show += 1
data = {'active_num': active_num, 'to_not_show': to_not_show, 'to_show': to_show}
return data
if __name__ == '__main__':
import os
import django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'izone.settings')
django.setup()
# print(action_clear_notification(100))
# print(action_cleanup_task_result(7))
print(action_check_site_links())
| 255 | 32.4 | 90 | 22 | 2,159 | python | [{"finding_id": "codeql_py/request-without-cert-validation_1fcc7e933dc682b3_f0267f36", "tool_name": "codeql", "rule_id": "py/request-without-cert-validation", "finding_type": "problem", "severity": "medium", "confidence": "medium", "message": "This request may run without certificate validation because [it is disabled](1).", "remediation": "", "location": {"file_path": "unknown", "line_start": 16, "line_end": null, "column_start": 16, "column_end": 58, "code_snippet": ""}, "cwe_id": "CWE-295", "cwe_name": null, "cvss_score": null, "cvss_vector": null, "owasp_category": null, "references": [], "fingerprint": null, "tags": ["security", "external/cwe/cwe-295"], "raw_output": {"ruleId": "py/request-without-cert-validation", "ruleIndex": 42, "rule": {"id": "py/request-without-cert-validation", "index": 42}, "message": {"text": "This request may run without certificate validation because [it is disabled](1)."}, "locations": [{"physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 16, "startColumn": 16, "endColumn": 58}}}], "partialFingerprints": {"primaryLocationLineHash": "5be342ad35708513:1", "primaryLocationStartColumnFingerprint": "7"}, "relatedLocations": [{"id": 1, "physicalLocation": {"artifactLocation": {"uri": "code.py", "uriBaseId": "%SRCROOT%", "index": 0}, "region": {"startLine": 16, "startColumn": 52, "endColumn": 57}}, "message": {"text": "it is disabled"}}]}}] | 1 | true |
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