Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

71b22fc91381f282dc98817aa113784b5ba94563

714fadd433c05b126b7909e1de14ee00bd2b0096

/tests/test_http_client/test_http_service.py

1dcba7edf292aa326727edd97db710b4b948af70

[ "MIT" ]

permissive

Zheaoli/huskar-python

4b1a30fc6939d387e09aaa484e7afe3ca190d293

f62a2d3636b2804a552bf59f76903cf2841d75c9

refs/heads/master

2020-08-03T03:59:19.279390

2019-09-27T06:34:09

null

0

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UTF-8

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py

# -*- coding: utf-8 -*- from mock import Mock import pytest import gevent from huskar_sdk_v2.http.components.service import Service initial_service_data = {u'192.168.1.1_17400': { u'ip': u'192.168.1.1', u'meta': { u'control_daemon_port': 5544, u'protocol': u'thrift', u'pushSequence': 4974, u'soaVersion': u'0.14.5.3', u'weight': 1}, u'name': u'arch.test', u'port': {u'main': 17400}, u'state': u'up'}, } added_service_data = {"192.168.1.1_23471": { "ip": "192.168.1.1", "state": "up", "meta": { "control_daemon_port": 5544, "soaVersion": "0.14.5.3", "protocol": "thrift", "weight": 1, "pushSequence": 4975}, "name": "arch.test", "port": {"main": 23471}} } @pytest.fixture def service_component(request, requests_mock, started_client): assert started_client.connected.wait(1) return Service('arch.test', 'alpha-stable') @pytest.fixture def fake_service_component(started_file_cache_client, fake_service_with_file_cache_client): started_file_cache_client.watched_configs.add_watch( "arch.test", 'overall') started_file_cache_client.watched_switches.add_watch( "arch.test", 'another-cluster') started_file_cache_client.watched_services.add_watch( "arch.test", 'alpha-stable') return fake_service_with_file_cache_client('arch.test', 'alpha-stable') def test_service_should_yield_the_same_format_as_old_huskar( service_component, started_client, fake_service_component): assert started_client.connected.wait(1) assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data def test_service_changed_should_change_service_nodes( requests_mock, service_component, started_client, fake_service_component): assert started_client.connected.wait(1) requests_mock.set_result_file('test_data_changed.txt') assert requests_mock.wait_processed() new_service_data = dict(initial_service_data) new_service_data.update(added_service_data) assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == new_service_data gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == new_service_data def test_service_deleted_should_change_service_nodes( requests_mock, service_component, started_client, fake_service_component): listener = Mock() assert started_client.connected.wait(1) service_component.register_hook_function( 'arch.test', 'alpha-stable', listener) requests_mock.set_result_file('test_data_deleted.txt') assert requests_mock.wait_processed() assert listener.call_count == 2 listener.assert_any_call({}) assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == {} gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == {} def test_service_node_changed_should_notify_listeners( requests_mock, service_component, started_client, fake_service_component): assert started_client.connected.wait(1) listener = Mock() fake_listener = Mock() service_component.register_hook_function( 'arch.test', 'alpha-stable', listener) fake_service_component.register_hook_function( 'arch.test', 'alpha-stable', fake_listener) listener.assert_called_once_with(initial_service_data) gevent.sleep(0.5) fake_listener.assert_called_with(initial_service_data) requests_mock.set_result_file('test_data_changed.txt') assert requests_mock.wait_processed() new_service_data = dict(initial_service_data) new_service_data.update(added_service_data) listener.assert_any_call(new_service_data) gevent.sleep(0.5) fake_listener.assert_any_call(new_service_data) def test_file_client_add_watch_after_data_already_processed( requests_mock, service_component, started_client, fake_service_component): fake_service_component.client.app_id_cluster_map.pop('arch.test', None) assert started_client.connected.wait(1) listener = Mock() fake_listener = Mock() service_component.register_hook_function( 'arch.test', 'alpha-stable', listener) listener.assert_called_once_with(initial_service_data) gevent.sleep(0.5) assert ('alpha-stable' not in fake_service_component.client.app_id_cluster_map['arch.test']) fake_service_component.register_hook_function( 'arch.test', 'alpha-stable', fake_listener) fake_listener.assert_called_with(initial_service_data) assert ('alpha-stable' in fake_service_component.client.app_id_cluster_map['arch.test']) def test_service_batch_add_watch(requests_mock, service_component, started_client, started_file_cache_client, fake_service_component): service_component.preprocess_service_mappings({}) fake_service_component.preprocess_service_mappings({}) assert service_component.preprocess_service_mappings({ 'arch.test1': {'that-cluster'}, 'arch.test2': {'this-cluster'}, }) is True assert fake_service_component.preprocess_service_mappings({ 'arch.test1': {'that-cluster'}, 'arch.test2': {'this-cluster'}, }) is True assert dict(started_client.watched_services.app_id_cluster_map) == { 'arch.test': {'alpha-stable'}, 'arch.test1': {'that-cluster'}, 'arch.test2': {'this-cluster'}, } fake_services = started_file_cache_client.watched_services assert dict(fake_services.app_id_cluster_map) == { 'arch.test': {'alpha-stable'}, 'arch.test1': {'that-cluster'}, 'arch.test2': {'this-cluster'}, } def test_legacy_interface(requests_mock, service_component): service_component.set_min_server_num(1) def test_add_service_in_the_middle_of_runtime( requests_mock, service_component, started_client, fake_service_component): assert started_client.connected.wait(1) assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data requests_mock.add_response( r'{"body": {"service": {"arch.test": {"beta-stable": ' r'{"192.168.1.1_9999": {"value": "{\"ip\": \"192.168.1.1\"' r', \"state\": \"up\", \"meta\": {\"control_daemon_port\": 5544,' r' \"soaVersion\": \"0.14.5.3\", \"protocol\": \"thrift\",' r' \"weight\": 1, \"pushSequence\": 4975}, \"name\":' r' \"arch.test\", \"port\": {\"main\": 9999}}"}}}}},' r' "message": "update"}') assert requests_mock.wait_processed() assert service_component.get_service_node_list( 'arch.test', 'beta-stable') == {} gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'beta-stable') == {} assert service_component.add_service('arch.test', 'beta-stable', timeout=10) assert fake_service_component.add_service('arch.test', 'beta-stable', timeout=10) requests_mock.add_response( r'{"body": {"service": {"arch.test": {"beta-stable":' r' {"192.168.1.1_9999": {"value": "{\"ip\":' r' \"192.168.1.1\", \"state\": \"up\", \"meta\":' r' {\"control_daemon_port\": 5544, \"soaVersion\": \"0.14.5.3\",' r' \"protocol\": \"thrift\", \"weight\": 1, \"pushSequence\":' r' 4975}, \"name\": \"arch.test\", \"port\": {\"main\": 9999' r'}}"}}}}}, "message": "update"}') assert requests_mock.wait_processed() assert service_component.get_service_node_list( 'arch.test', 'beta-stable') gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'beta-stable') def test_service_should_not_update_if_watch_is_removed( requests_mock, service_component, started_client, fake_service_component): assert started_client.connected.wait(1) assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data gevent.sleep(0.5) assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data assert service_component.unwatch_service( 'arch.test', 'alpha-stable', timeout=2.0) assert fake_service_component.unwatch_service( 'arch.test', 'alpha-stable', timeout=2.0) requests_mock.add_response( r'{"body": {"service": {"arch.test": {"alpha-stable": ' r'{"192.168.1.1_9999": {"value": "{\"ip\": \"192.168.1.1\",' r' \"state\": \"up\", \"meta\": {\"control_daemon_port\": 5544,' r' \"soaVersion\": \"0.14.5.3\", \"protocol\": \"thrift\", \"weight\":' r' 1, \"pushSequence\": 4975}, \"name\": \"arch.test\", \"port\": ' r'{\"main\": 9999}}"}}}}}, "message": "update"}') assert requests_mock.wait_processed() assert service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data assert fake_service_component.get_service_node_list( 'arch.test', 'alpha-stable') == initial_service_data

[ "mozillazg101@gmail.com" ]

mozillazg101@gmail.com

6e69623a745e215d65a1524e8506cd9057e79e1a

ac1dc63c3316671b04f5826523b64b0e5f7a8154

/__init__.py

12d72caf8579d5620658b97bfdc50fab7e824117

[]

no_license

leonardocfor/multi-robot-vicsek

0f2b6188fd41415d8130aa0d69fec1f309f64da0

88d5c72f671dd4108bbf65d8bff54157371cf018

refs/heads/master

2021-07-06T22:55:58.287060

2020-10-03T00:30:27

192,839,463

4

0

null

UTF-8

Python

false

30

py

__all__ = ['bin','etc','lib']

[ "lecf.77@gmail.com" ]

lecf.77@gmail.com

d1f409cf102e8f3f8ab14c629e24c68701bf7f54

9620f0d4564be92deb2c09da6895cca920e51435

/app.py

65516d178d7bbb58e9def80f5954299db70f0f05

[]

no_license

talha-ghaffar/articler

14e4825099f55559b4882f852e30b6bf687dea8a

6037045775f779722077af809ae4a5474a74a28b

refs/heads/master

2020-04-04T11:04:17.887176

2018-11-02T14:33:27

155,878,038

1

0

null

UTF-8

Python

false

7,481

py

from flask import Flask, render_template, flash, redirect, url_for, session, request, logging from flask_mysqldb import MySQL from wtforms import Form, StringField, TextAreaField, PasswordField, validators from passlib.hash import sha256_crypt from functools import wraps app = Flask(__name__) # Config MySQL app.config['MYSQL_HOST'] = 'localhost' app.config['MYSQL_USER'] = 'root' app.config['MYSQL_PASSWORD'] = '12345' app.config['MYSQL_DB'] = 'myflaskapp' app.config['MYSQL_CURSORCLASS'] = 'DictCursor' # init MYSQL mysql = MySQL(app) # Index @app.route('/') def index(): return render_template('home.html') # About @app.route('/about') def about(): return render_template('about.html') # Articles @app.route('/articles') def articles(): # Create cursor cur = mysql.connection.cursor() # Get articles result = cur.execute("SELECT * FROM articles") articles = cur.fetchall() if result > 0: return render_template('articles.html', articles=articles) else: msg = 'No Articles Found' return render_template('articles.html', msg=msg) # Close connection cur.close() #Single Article @app.route('/article/<string:id>/') def article(id): # Create cursor cur = mysql.connection.cursor() # Get article result = cur.execute("SELECT * FROM articles WHERE id = %s", [id]) article = cur.fetchone() return render_template('article.html', article=article) # Register Form Class class RegisterForm(Form): name = StringField('Name', [validators.Length(min=1, max=50)]) username = StringField('Username', [validators.Length(min=4, max=25)]) email = StringField('Email', [validators.Length(min=6, max=50)]) password = PasswordField('Password', [ validators.DataRequired(), validators.EqualTo('confirm', message='Passwords do not match') ]) confirm = PasswordField('Confirm Password') # User Register @app.route('/register', methods=['GET', 'POST']) def register(): form = RegisterForm(request.form) if request.method == 'POST' and form.validate(): name = form.name.data email = form.email.data username = form.username.data password = sha256_crypt.encrypt(str(form.password.data)) # Create cursor cur = mysql.connection.cursor() # Execute query cur.execute("INSERT INTO users(name, email, username, password) VALUES(%s, %s, %s, %s)", (name, email, username, password)) # Commit to DB mysql.connection.commit() # Close connection cur.close() flash('You are now registered and can log in', 'success') return redirect(url_for('login')) return render_template('register.html', form=form) # User login @app.route('/login', methods=['GET', 'POST']) def login(): if request.method == 'POST': # Get Form Fields username = request.form['username'] password_candidate = request.form['password'] # Create cursor cur = mysql.connection.cursor() # Get user by username result = cur.execute("SELECT * FROM users WHERE username = %s", [username]) if result > 0: # Get stored hash data = cur.fetchone() password = data['password'] # Compare Passwords if sha256_crypt.verify(password_candidate, password): # Passed session['logged_in'] = True session['username'] = username flash('You are now logged in', 'success') return redirect(url_for('dashboard')) else: error = 'Invalid login' return render_template('login.html', error=error) # Close connection cur.close() else: error = 'Username not found' return render_template('login.html', error=error) return render_template('login.html') # Check if user logged in def is_logged_in(f): @wraps(f) def wrap(*args, **kwargs): if 'logged_in' in session: return f(*args, **kwargs) else: flash('Unauthorized, Please login', 'danger') return redirect(url_for('login')) return wrap # Logout @app.route('/logout') @is_logged_in def logout(): session.clear() flash('You are now logged out', 'success') return redirect(url_for('login')) # Dashboard @app.route('/dashboard') @is_logged_in def dashboard(): # Create cursor cur = mysql.connection.cursor() # Get articles #result = cur.execute("SELECT * FROM articles") # Show articles only from the user logged in result = cur.execute("SELECT * FROM articles WHERE author = %s", [session['username']]) articles = cur.fetchall() if result > 0: return render_template('dashboard.html', articles=articles) else: msg = 'No Articles Found' return render_template('dashboard.html', msg=msg) # Close connection cur.close() # Article Form Class class ArticleForm(Form): title = StringField('Title', [validators.Length(min=1, max=200)]) body = TextAreaField('Body', [validators.Length(min=30)]) # Add Article @app.route('/add_article', methods=['GET', 'POST']) @is_logged_in def add_article(): form = ArticleForm(request.form) if request.method == 'POST' and form.validate(): title = form.title.data body = form.body.data # Create Cursor cur = mysql.connection.cursor() # Execute cur.execute("INSERT INTO articles(title, body, author) VALUES(%s, %s, %s)",(title, body, session['username'])) # Commit to DB mysql.connection.commit() #Close connection cur.close() flash('Article Created', 'success') return redirect(url_for('dashboard')) return render_template('add_article.html', form=form) # Edit Article @app.route('/edit_article/<string:id>', methods=['GET', 'POST']) @is_logged_in def edit_article(id): # Create cursor cur = mysql.connection.cursor() # Get article by id result = cur.execute("SELECT * FROM articles WHERE id = %s", [id]) article = cur.fetchone() cur.close() # Get form form = ArticleForm(request.form) # Populate article form fields form.title.data = article['title'] form.body.data = article['body'] if request.method == 'POST' and form.validate(): title = request.form['title'] body = request.form['body'] # Create Cursor cur = mysql.connection.cursor() app.logger.info(title) # Execute cur.execute ("UPDATE articles SET title=%s, body=%s WHERE id=%s",(title, body, id)) # Commit to DB mysql.connection.commit() #Close connection cur.close() flash('Article Updated', 'success') return redirect(url_for('dashboard')) return render_template('edit_article.html', form=form) # Delete Article @app.route('/delete_article/<string:id>', methods=['POST']) @is_logged_in def delete_article(id): # Create cursor cur = mysql.connection.cursor() # Execute cur.execute("DELETE FROM articles WHERE id = %s", [id]) # Commit to DB mysql.connection.commit() #Close connection cur.close() flash('Article Deleted', 'success') return redirect(url_for('dashboard')) if __name__ == '__main__': app.secret_key='secret123' app.run(debug=True)

[ "talha.ghaffar@hotmail.com" ]

talha.ghaffar@hotmail.com

46d58ef295247a199fdfba6578d1c510ede1a49a

9db50ad2dcb936ff15711271a66a19711030efd2

/Delete_node_ linked.py

a88505b94b61f4707729ba7cfb89d91a9d10da8e

[]

no_license

sixbo/LeetCode

c3fa7bfb2942ecb23fa6e85cd8876780fc5061df

a6f7161735547b8d74ecdb8d22d9fe681b3b2294

refs/heads/master

2020-09-07T11:05:52.908256

2019-11-13T07:35:43

220,759,255

0

null

UTF-8

Python

false

493

py

def deleteNode(listnode, node): """ :type node: ListNode :rtype: void Do not return anything, modify node in-place instead. """ if len(listnode)<2: print("链列表太短了") elif node==len(listnode): print("你不能删除最后一个") else: for i in range(len(listnode)-1): if listnode[i]==node: del listnode[i] return listnode list=[1,2,3,2,4,5] node1=2 x=deleteNode(list,node1) print(x)

[ "liubo37@163.com" ]

liubo37@163.com

7d4d9e1c9a076511f2ab8479fb13a3113ee0c1bb

b2b4cd86da23cfcafa642b01ee86bd12284e6dfa

/ffWarAdminApi/apps.py

70a2b0313cd620646f566c3b41852550c662ea1a

[]

no_license

bikram-shaw/ffWarApi

d47e1116844654150dc278d0b723e67e7194075d

b16d482680c45423927a002a1ca2e35642beadca

refs/heads/main

2023-02-28T07:30:51.658906

2021-01-31T07:33:29

319,536,684

0

null

UTF-8

Python

false

101

py

from django.apps import AppConfig class FfwaradminapiConfig(AppConfig): name = 'ffWarAdminApi'

[ "bikramshawbnk@gmail.com" ]

bikramshawbnk@gmail.com

658ac4aa4accaa322e09456a967e92697c29df16

1cee01e4e31672df57d4e7841c6d0f0efda6153b

/RP_impl/hello_world.py

2801e3a09022ae697763bd254746ebbbbe80364e

[]

no_license

andrealoevemaerke/thesis

9c3f5ab9416b6da979fcaf16637bd35e28bead2a

701c0ed2de3c2bae4430238b37739659b883b6d8

refs/heads/master

2020-05-19T17:58:36.356030

2019-06-12T13:19:10

185,146,376

0

null

UTF-8

Python

false

5,835

py

import socket import numpy as np from threading import Thread import FFArithmetic as field import shamir_scheme as ss import proc import TcpSocket5 as sock import time import queue as que from participantCodePLOT import party import matplotlib.pyplot as plt import os port = 65 party_addr = [['192.168.100.1', 65], #P0 ['192.168.100.2', 65], #P1 ['192.168.100.3', 65], #P2 ['192.168.100.4', 65], #P3 ['192.168.100.5', 65], #P3 ['192.168.100.6', 65] #P3 ] ccu_adr = '192.168.100.246' server_addr = [[ccu_adr, 4031], #P0 [ccu_adr, 4040], #P1 [ccu_adr, 4041], #P2 [ccu_adr, 4050], #P3 [ccu_adr, 4060], #Reciever 4 [ccu_adr, 4061] #Reciever 5 ] class commsThread (Thread): stop = False def __init__(self, threadID, name, server_info,q): Thread.__init__(self) self.q = q self.threadID = threadID self.name = name self.server_info = server_info # (Tcp_ip, Tcp_port) self.Rx_packet = [] # tuple [[client_ip, client_port], [Rx_data[n]]] def run(self): # print("Starting " + self.name) #Create TCP socket tcpsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) tcpsock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) tcpsock.bind(tuple(self.server_info)) #Communication loop - Wait->Receive->Put to queue while not self.stop: Rx_packet = sock.TCPserver(tcpsock) # print("Client info:",Rx_packet[0]) # print("Data recv:",Rx_packet[1]) if not self.q.full(): self.q.put(Rx_packet) print("Exiting " + self.name) m = 7979490791 F = field.GF(m) n = 2 t = 1 x = 7 ipv4 = os.popen('ip addr show eth0').read().split("inet ")[1].split("/")[0] pnr = party_addr.index([ipv4, port]) q = que.Queue() q2 = que.Queue() q3 = que.Queue() #Initialization.. #TCP_IP = '192.168.100.246' #TCP_PORT = 62 #UDP_PORT2 = 3000 server_info = party_addr[pnr]#(TCP_IP, TCP_PORT) #server2_info = (ipv4, UDP_PORT2) # Create new threads.. t1_comms = commsThread(1, "Communication Thread", server_info,q) #2_commsSimulink = UDPcommsThread(2, "t2_commsSimulink", server2_info) #ploting = plotter(q3) #ploting.start() class party(Thread): def __init__(self, F, x, n, t, i, q, q2,q3, paddr, saddr): Thread.__init__(self) self.c = 0 self.comr = 0 self.recv = {} self.F = F self.x = x self.n = n self.t = t self.i = i self.q = q self.q2 = q2 self.q3 = q3 self.party_addr = paddr self.server_addr = saddr def distribute_shares(self, sec): shares = ss.share(self.F, sec, self.t, self.n) for i in range(self.n): sock.TCPclient(self.party_addr[i][0], self.party_addr[i][1], ['input' + str(self.i) , int(str(shares[i]))]) def broadcast(self, name, s): for i in range(self.n): sock.TCPclient(self.party_addr[i][0], self.party_addr[i][1], [name + str(self.i) , int(str(s))]) def readQueue(self): while not self.q.empty(): b = self.q.get()[1] self.recv[b[0]] = b[1] self.q3.put([b[0][-1], b[1]]) def get_shares(self, name): res = [] for i in range(self.n): while name + str(i) not in self.recv: self.readQueue() res.append(self.F(self.recv[name+str(i)])) del self.recv[name + str(i)] return res def reconstruct_secret(self, name): return ss.rec(self.F, self.get_shares(name)) def get_share(self, name): while name not in self.recv: self.readQueue() a = self.F(self.recv[name]) del self.recv[name] return a def get_triplets(self): while 'triplets' not in self.recv: self.readQueue() b = self.recv['triplets'] res = [] for i in b: res.append([self.F(j) for j in i]) self.triplets = res def mult_shares(self, a, b): r = self.triplets[self.c] self.c += 1 d_local = a - r[0] self.broadcast('d' + str(self.comr), d_local) d_pub = self.reconstruct_secret('d' + str(self.comr)) self.comr +=1 e_local = b - r[1] self.broadcast('e' + str(self.comr), e_local) e_pub = self.reconstruct_secret('e' + str(self.comr)) self.comr+=1 return d_pub * e_pub + d_pub*r[1] + e_pub*r[0] + r[2] def legendreComp(self,a,b): r = self.triplets[self.c] self.c+=1 t = self.tt g = a - b k = self.mult_shares(t, self.mult_shares(r[0], r[0])) j_loc = self.mult_shares(g, k) self.broadcast('j'+ str(self.comr), j_loc) j_pub = self.reconstruct_secret('j'+str(self.comr)) self.comr+=1 ex = (self.F.p-1)/2 sym = pow(int(str(j_pub)),int(ex), self.F.p) f = sym * t c = self.mult_shares((f+1), self.F(2).inverse()) return c #def run(self): # self.distribute_shares(x,'x_shares') p = party(F,int(x),n,t,pnr, q, q2, q3, party_addr, server_addr) # Start new Threads #t2_commsSimulink.start() t1_comms.start() while True: try: sock.TCPclient(party_addr[5][0], party_addr[5][1], ['flag', 1]) break except: time.sleep(1) continue print('Connected to 5!') p.start() sock.TCPclient(party_addr[5][0], party_addr[5][1], ['output', int(p.x)]) while True: if not q.empty(): print(q.get())

[ "andreatram@gmail.com" ]

andreatram@gmail.com

93a85d1ecf6825449a3f7c64ee9f281b94e8bdb4

be043a809f54e592038e44db56627d5895ea5582

/torobot/fetcher.py

e08d7a44ceff39072d0ef327fa59e012f42f2461

[]

no_license

Yeolar/tornado-robot

4767b862bfec3e21c270813db49305c4571a42d5

9f04556da1ff3f4b2614bc605711e6ebbb5a0c1a

refs/heads/master

2021-01-18T14:11:11.439181

2013-08-20T04:53:02

null

0

null

UTF-8

Python

false

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#!/usr/bin/env python # -*- coding: utf-8 -*- # # Created on 2013-04-12. Yeolar <yeolar@gmail.com> # import logging from tornado.httpclient import AsyncHTTPClient, HTTPRequest from zmq.eventloop.ioloop import IOLoop class Fetcher(object): def __init__(self, *args, **kwargs): self.identity = '' self.initialize(*args, **kwargs) def initialize(self, *args, **kwargs): pass def build_request(self, request): raise NotImplementedError() def prepare(self): pass def on_finish(self): pass def fetch(self, request, callback): raise NotImplementedError() def __call__(self, request, callback): self.prepare() self.fetch(request, callback) self._log(request) self.on_finish() def _log(self, request): logging.info('[%s] %s %s', self.identity, request.method, request.url) class HTTPFetcher(Fetcher): def build_request(self, request): return HTTPRequest( url=request.url, method=request.method, body=request.body or None, connect_timeout=request.connect_timeout, request_timeout=request.request_timeout) def fetch(self, request, callback): client = AsyncHTTPClient(IOLoop.instance()) client.fetch(self.build_request(request), callback)

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def factorial(n): Sum=1 for i in range(1,n+1): Sum=Sum*i return Sum num=int(input("Enter a no. ")) Sum=0 n=num while(num>0): rem=num%10 Sum=Sum+factorial(rem) num=num//10 if n==Sum: print("Enter number",n,"Strong no.") else: print("Enter number is",n,"not Strong no.")

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gmiraval/stats

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#!/usr/bin/env python # -*- coding: utf-8 -*- import requests import json import sys import urllib3 import logging import boto3 #acceso a aws-poner credenciales en .aws from decimal import Decimal from datetime import * import redis #seteo nivel de log #logging.basicConfig(level=logging.DEBUG) ##logging.basicConfig(level=logging.INFO) ## doc api bcra: ## https://estadisticasbcra.com/api/documentation url = "http://api.estadisticasbcra.com/usd_of" ##en dias poner desde cuantos dias atras arranco a importar dias = 30 startdate = int((datetime.now()-timedelta(dias)).strftime("%Y%m%d")) logging.info(startdate) headers = { 'Authorization': "Bearer eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.eyJleHAiOjE1NzM3MzQxNDUsInR5cGUiOiJleHRlcm5hbCIsInVzZXIiOiJtaXJhdmFsbGVzZ0BnbWFpbC5jb20ifQ.4Kb5JA1RiwN9DSxqH319B1aT-DWiYflz6odQbB3cAAL3hCJMOpe8rXOBHvcQyWruoVVME2uUTx4F35ZNQa8dVg", 'Accept': "*/*", 'Accept-Encoding': "gzip, deflate", 'Connection': "keep-alive", 'cache-control': "no-cache" } response = requests.request("POST", url, headers=headers).json() #.json() pasa la respuesta a un objeto json ##respuesta ejemplo para debug sin pegarle a la api ##response = json.loads('[{"d": "2003-08-20","v": 2.91},{"d": "2019-07-01","v": 14.05},{"d": "2019-07-02","v": 14.06}]') ##defino conexion a tabla ##dynamodb = boto3.resource('dynamodb') ##table = dynamodb.Table('usd') # create a connection to the localhost Redis server instance, by # default it runs on port 6379 redis_db = redis.StrictRedis(host="localhost", port=6379, db=0) #voy insertando por cada item en tabla for x in response: logging.info(x['d'][0:4]+x['d'][5:7]+x['d'][8:10]) date = int(x['d'][0:4]+x['d'][5:7]+x['d'][8:10]) if date > startdate: ## table.put_item( ## Item={ ## 'd': x['d'], ## 'v': Decimal(str(x['v'])) ## } ##) redis_db.zadd('usd',date,x['v'])

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merissab44/Events_site

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#!/Users/merissabridgeman/dev/courses/BEW1.2/BEW-1.2-Events-Homework/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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omarbakeer/PY4E

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2021-03-19T11:09:42.316161

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name = input("Enter file:") if len(name) < 1 : name = "mbox-short.txt" handle = open(name) lst = list() dic = dict() for line in handle : if not line.startswith("From "): continue line = line.split() time = line[5] time = time.split(":") hour = time[0] lst.append(hour) for hours in lst: dic[hours] = dic.get(hours,0)+1 for k,v in sorted(dic.items()): print(k,v)

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/tests/test_parsers.py

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pketh/investigation-google-search-audit

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2022-11-19T07:18:26.304659

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""" Test Parsers ============ This script is an abstract test for HTML parsers. Use it for two reasons: 1. Checking if Google has made changes to search results. 2. Assuring parsers are still mostly working. It is OK if some tests fail, it just means that the specific parser's target was not in the input examples. This code ships with a default dataset. You can include an extra parameter of a directory with new HTML files to test. Author: Leon Yin """ import os import sys import glob import unittest import inspect import pandas as pd from bs4 import BeautifulSoup from parameterized import parameterized # the software we're testing is in this directory as `utils` sys.path.append('..') import utils.parsers as P functions_list = [o for o in inspect.getmembers(P) if inspect.isfunction(o[1]) and '_parser' in o[0]] class TestParsers(unittest.TestCase): ''' This class contains three functions that are run in the order (the name of the first and last matter!). `test_abstract` is to be used by all parsers in `functions_list`. It iterates through the files read from `data_dir`, and sends each HTML file through the parsers. If the parser targets exist, the parser will return a list of dictionaries. ''' data_dir = '../data/test/input_local_searches' metadata_dir = '../data/test/parser_output' for d in [data_dir, metadata_dir]: os.makedirs(d, exist_ok=True) @classmethod def setUpClass(cls): ''' Initializes parameters for HTML parsers. Note that every file is read into memory and placed within a BeautifulSoup object. ''' # create an empty dictionary ro record metadata on tests cls.report = dict() # select the local directory with HTML files to test. if not os.path.isdir(cls.data_dir): raise Exception('The input directory does not exist.') cls.input_filenames = glob.glob( os.path.join(cls.data_dir, '*.html') ) cls.n_inputs = len(cls.input_filenames) # read each HTML file into Beautiful soup and store them as a list in `parse_trees` soups = [] for fn in cls.input_filenames: with open(fn) as f: filestream = f.read() soup = BeautifulSoup(filestream, 'lxml') soups.append(soup) cls.parse_trees = soups @parameterized.expand(functions_list) def test_abstract(self, func_name, parser_func): ''' This is the abstract of a test, thanks for the decorator, each test will be parameterized by each tuple in `parser_params`. The tuple contains a function name and the parser function itself. The test sends all the input files in `soups` To make sure these tests are accurate, make sure the at least one inputs contain elements you're looking for... The results of the parsers are saved as a key-value pair in the `report` property ''' results = [] hits = 0 for i, soup in enumerate(self.parse_trees): elements = parser_func(soup) if len(elements) != 0: hits += 1 for item in elements: item.update({'filename' : os.path.abspath(self.input_filenames[i])}) results.extend(elements) self.assertTrue(hits != 0) self.report[func_name] = results @classmethod def tearDownClass(cls): ''' This will provide some sort of aggregate statistic... We'll figure out what to do with this later. ''' for test, data in cls.report.items(): df = pd.DataFrame(data) fn_out = os.path.join(cls.metadata_dir, f"{test}_results.csv") df.to_csv(fn_out, index=False) if __name__ == '__main__': if len(sys.argv) > 1: TestParsers.data_dir = sys.argv.pop() unittest.main()

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leon.yin@themarkup.org

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/code/docker/python/flask_2/app.py

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afcarl/pythoh_machine_learning_excerise

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2020-03-21T06:00:08.623962

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# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import unicode_literals from flask import Flask, render_template, request from wtforms import Form, TextAreaField, validators import pickle import sqlite3 import os import numpy as np from vectorizer import vect app = Flask(__name__) cur_dir = os.path.dirname(__file__) clf = pickle.load(open(os.path.join(cur_dir, 'pickle_objects', 'classifier.pkl'), 'rb')) db = os.path.join(cur_dir, 'reviews.sqlite') def classify(document): label = {0: 'negative', 1: 'positive'} X =vect.transform([document]) y = clf.predict(X)[0] proba = np.max(clf.predict_proba(X)) return label[y], proba def train(document, y): X = vect.transform([document]) clf.partial_fit(X, [y]) def sqlite_entry(path, document, y): conn = sqlite3.connect(path) c = conn.cursor() c.execute("INSERT INTO review_db (review, sentiment, date)"\ " VALUES (?, ?, DATETIME('now'))", (document, y)) conn.commit() conn.close() class ReviewForm(Form): moviereview = TextAreaField('', [validators.DataRequired(), validators.length(min=15)]) @app.route('/') def index(): form = ReviewForm(request.form) return render_template('reviewform.html', form=form) @app.route('/requests', methods=['POST']) def results(): form = ReviewForm(request.form) if request.method == 'POST' and form.validate(): review = request.form['movierview'] y, proba = classify(review) return render_template('results.html', content=review, prediction=y, probability=round(proba * 100, 2)) return render_template('reviewform.html', form=form) @app.route('/thanks', methods=['POST']) def feedback(): feedback = request.form['feedback_button'] review = request.form['review'] prediction = request.form['prediction'] inv_label = {'negative': 0, 'positive': 1} y = inv_label[prediction] if feedback == 'Incorrect': y = int(not(y)) train(review, y) sqlite_entry(db, review, y) return render_template('thanks.html') if __name__ == '__main__': app.run(debug=True)

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snow.akogi.pgel@gmail.com

62f3762c1f4cc277c8f0b20c4777ee5c189eb345

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/tests/plugins/test_speedrunslive.py

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fuglede/streamlink

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import unittest from streamlink.plugins.speedrunslive import SpeedRunsLive class TestPluginSpeedRunsLive(unittest.TestCase): def test_can_handle_url(self): should_match = [ 'http://www.speedrunslive.com/#!/twitch', ] for url in should_match: self.assertTrue(SpeedRunsLive.can_handle_url(url)) def test_can_handle_url_negative(self): should_not_match = [ 'https://www.twitch.tv', ] for url in should_not_match: self.assertFalse(SpeedRunsLive.can_handle_url(url))

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gravyboat@users.noreply.github.com

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gabriellaec/desoft-analise-exercicios

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2023-01-31T17:19:42.050628

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def lista_primos(n): a=2 lista=[] contador = 3 while len(lista)!=n: if a == 2: lista.append(a) a+=1 elif a%2 == 0: a+=1 elif contador < a : contador = 3 while contador < a: if a%contador == 0: contador+=2 else: lista.append(a) contador=a+2 a+=1 else: a+=1 return lista

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you@example.com

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python def solution(n): for k in range(n): y=len([k for k in range(1,n+1) if not n %k]) if y == 3: return True else: return False

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import sys sys.path.append('..') import argparse from configparser import ConfigParser import ast import random from copy import deepcopy import numpy as np import torch from torch import optim from torch.utils.data import DataLoader, SubsetRandomSampler import torchvision.transforms as T import torchvision.transforms.functional as TF from dataset import DomainNet from models import SimpleCNN, MDANet, MODANet from routines import (fs_train_routine, fm_train_routine, dann_train_routine, mdan_train_routine, mdan_train_routine, moda_train_routine, moda_fm_train_routine) from utils import MSDA_Loader, Logger def main(): parser = argparse.ArgumentParser(description='Domain adaptation experiments with the DomainNet dataset.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('-m', '--model', default='MODAFM', type=str, metavar='', help='model type (\'FS\' / \'DANNS\' / \'DANNM\' / \'MDAN\' / \'MODA\' / \'FM\' / \'MODAFM\'') parser.add_argument('-d', '--data_path', default='/ctm-hdd-pool01/DB/DomainNet192', type=str, metavar='', help='data directory path') parser.add_argument('-t', '--target', default='clipart', type=str, metavar='', help='target domain (\'clipart\' / \'infograph\' / \'painting\' / \'quickdraw\' / \'real\' / \'sketch\')') parser.add_argument('-o', '--output', default='msda.pth', type=str, metavar='', help='model file (output of train)') parser.add_argument('--icfg', default=None, type=str, metavar='', help='config file (overrides args)') parser.add_argument('--arch', default='resnet152', type=str, metavar='', help='network architecture (\'resnet101\' / \'resnet152\'') parser.add_argument('--mu_d', type=float, default=1e-2, help="hyperparameter of the coefficient for the domain discriminator loss") parser.add_argument('--mu_s', type=float, default=0.2, help="hyperparameter of the non-sparsity regularization") parser.add_argument('--mu_c', type=float, default=1e-1, help="hyperparameter of the FixMatch loss") parser.add_argument('--n_rand_aug', type=int, default=2, help="N parameter of RandAugment") parser.add_argument('--m_min_rand_aug', type=int, default=3, help="minimum M parameter of RandAugment") parser.add_argument('--m_max_rand_aug', type=int, default=10, help="maximum M parameter of RandAugment") parser.add_argument('--weight_decay', default=0., type=float, metavar='', help='hyperparameter of weight decay regularization') parser.add_argument('--lr', default=1e-3, type=float, metavar='', help='learning rate') parser.add_argument('--epochs', default=50, type=int, metavar='', help='number of training epochs') parser.add_argument('--batch_size', default=8, type=int, metavar='', help='batch size (per domain)') parser.add_argument('--checkpoint', default=0, type=int, metavar='', help='number of epochs between saving checkpoints (0 disables checkpoints)') parser.add_argument('--eval_target', default=False, type=int, metavar='', help='evaluate target during training') parser.add_argument('--use_cuda', default=True, type=int, metavar='', help='use CUDA capable GPU') parser.add_argument('--use_visdom', default=False, type=int, metavar='', help='use Visdom to visualize plots') parser.add_argument('--visdom_env', default='domainnet_train', type=str, metavar='', help='Visdom environment name') parser.add_argument('--visdom_port', default=8888, type=int, metavar='', help='Visdom port') parser.add_argument('--verbosity', default=2, type=int, metavar='', help='log verbosity level (0, 1, 2)') parser.add_argument('--seed', default=42, type=int, metavar='', help='random seed') args = vars(parser.parse_args()) # override args with icfg (if provided) cfg = args.copy() if cfg['icfg'] is not None: cv_parser = ConfigParser() cv_parser.read(cfg['icfg']) cv_param_names = [] for key, val in cv_parser.items('main'): cfg[key] = ast.literal_eval(val) cv_param_names.append(key) # dump args to a txt file for your records with open(cfg['output'] + '.txt', 'w') as f: f.write(str(cfg)+'\n') # use a fixed random seed for reproducibility purposes if cfg['seed'] > 0: random.seed(cfg['seed']) np.random.seed(seed=cfg['seed']) torch.manual_seed(cfg['seed']) torch.cuda.manual_seed(cfg['seed']) device = 'cuda' if (cfg['use_cuda'] and torch.cuda.is_available()) else 'cpu' log = Logger(cfg['verbosity']) log.print('device:', device, level=0) # normalization transformation (required for pretrained networks) normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) if 'FM' in cfg['model']: # weak data augmentation (small rotation + small translation) data_aug = T.Compose([ # T.RandomCrop(224), # T.Resize(128), T.RandomHorizontalFlip(), T.RandomAffine(5, translate=(0.125, 0.125)), T.ToTensor(), # normalize, # normalization disrupts FixMatch ]) eval_transf = T.Compose([ # T.RandomCrop(224), # T.Resize(128), T.ToTensor(), ]) else: data_aug = T.Compose([ # T.RandomCrop(224), # T.Resize(128), T.RandomHorizontalFlip(), T.ToTensor(), normalize, ]) eval_transf = T.Compose([ # T.RandomCrop(224), # T.Resize(128), T.ToTensor(), normalize, ]) domains = ['clipart', 'infograph', 'painting', 'quickdraw', 'real', 'sketch'] datasets = {domain: DomainNet(cfg['data_path'], domain=domain, train=True, transform=data_aug) for domain in domains} n_classes = len(datasets[cfg['target']].class_names) test_set = DomainNet(cfg['data_path'], domain=cfg['target'], train=False, transform=eval_transf) if 'FM' in cfg['model']: target_pub = deepcopy(datasets[cfg['target']]) target_pub.transform = eval_transf # no data augmentation in test else: target_pub = datasets[cfg['target']] if cfg['model'] != 'FS': train_loader = MSDA_Loader(datasets, cfg['target'], batch_size=cfg['batch_size'], shuffle=True, num_workers=0, device=device) if cfg['eval_target']: valid_loaders = {'target pub': DataLoader(target_pub, batch_size=6*cfg['batch_size']), 'target priv': DataLoader(test_set, batch_size=6*cfg['batch_size'])} else: valid_loaders = None log.print('target domain:', cfg['target'], '| source domains:', train_loader.sources, level=1) else: train_loader = DataLoader( datasets[cfg['target']], batch_size=cfg['batch_size'], shuffle=True) test_loader = DataLoader( test_set, batch_size=cfg['batch_size']) log.print('target domain:', cfg['target'], level=1) if cfg['model'] == 'FS': model = SimpleCNN(n_classes=n_classes, arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) valid_loaders = {'target pub': test_loader} if cfg['eval_target'] else None fs_train_routine(model, optimizer, train_loader, valid_loaders, cfg) elif cfg['model'] == 'FM': model = SimpleCNN(n_classes=n_classes, arch=cfg['arch']).to(device) for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) cfg['excl_transf'] = None fm_train_routine(model, optimizer, train_loader, valid_loaders, cfg) elif cfg['model'] == 'DANNS': for src in train_loader.sources: model = MODANet(n_classes=n_classes, arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) dataset_ss = {src: datasets[src], cfg['target']: datasets[cfg['target']]} train_loader = MSDA_Loader(dataset_ss, cfg['target'], batch_size=cfg['batch_size'], shuffle=True, device=device) dann_train_routine(model, optimizer, train_loader, valid_loaders, cfg) torch.save(model.state_dict(), cfg['output']+'_'+src) elif cfg['model'] == 'DANNM': model = MODANet(n_classes=n_classes, arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) dann_train_routine(model, optimizer, train_loader, valid_loaders, cfg) elif args['model'] == 'MDAN': model = MDANet(n_classes=n_classes, n_domains=len(train_loader.sources), arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) mdan_train_routine(model, optimizer, train_loader, valid_loaders, cfg) elif cfg['model'] == 'MODA': model = MODANet(n_classes=n_classes, arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) moda_train_routine(model, optimizer, train_loader, valid_loaders, cfg) elif cfg['model'] == 'MODAFM': model = MODANet(n_classes=n_classes, arch=cfg['arch']).to(device) conv_params, fc_params = [], [] for name, param in model.named_parameters(): if 'fc' in name.lower(): fc_params.append(param) else: conv_params.append(param) optimizer = optim.Adadelta([ {'params':conv_params, 'lr':0.1*cfg['lr'], 'weight_decay':cfg['weight_decay']}, {'params':fc_params, 'lr':cfg['lr'], 'weight_decay':cfg['weight_decay']} ]) cfg['excl_transf'] = None moda_fm_train_routine(model, optimizer, train_loader, valid_loaders, cfg) else: raise ValueError('Unknown model {}'.format(cfg['model'])) torch.save(model.state_dict(), cfg['output']) if __name__ == '__main__': main()

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jayrambhia/Extract-News-Summary

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import urllib2 from xml.dom.minidom import parseString #Standard Python parser for XML files (Non standard HTML) import sys def search( term, count ): #Term = query string, count = number of links desired results = [] #List for storing the URLs obj = parseString( urllib2.urlopen('http://news.google.com/news?q=%s&output=rss&num=%s' % (term, str(count))).read() ) #Open Google news for the desired query and number of links, get RSS output and parse it as a string with XML DOM links = obj.getElementsByTagName('link')[2: count+2] #From the parsed string get the Elements with the tag name <link>, skip the first two tags for link in links: results.append( link.childNodes[0].data.split('=')[-1] ) #From the data inside the <link> tag, seperate them at "=" and append the URL in the result list return results

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Rsheikh-shab/lauretta.io.test

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def value_predictor(value): # check length n = len(value) # check at least 6 elements and sort the list if n >= 6 and sorted(value): # check even number of value if n % 2 != 0: x = float(value[n / 2]) return x return float(value[int(n / 2)] + value[int((n - 1) / 2)]) / 2.0 else: return "List must have at least 6 elements" print(value_predictor([1, 2, 3, 4, 5, 6])) print(value_predictor([1, 1, 1, 6, 6, 6]))

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rajuiium121@gmail.com

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2022-11-23T19:35:25.270466

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""" Python For Quantitative Finance """ """ Support_Vector_Machine """ """ Shaofei Li """ from __future__ import print_function import pprint import re try: from html.parser import HTMLParser except ImportError: from HTMLParser import HTMLParser from sklearn.cross_validation import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import confusion_matrix from sklearn.svm import SVC class ReutersParser(HTMLParser): """ ReutersParser subclasses HTMLParser and is used to open the SGML files associated with the Reuters-21578 categorised test collection. The parser is a generator and will yield a single document at a time. Since the data will be chunked on parsing, it is necessary to keep some internal state of when tags have been "entered" and "exited". Hence the in_body, in_topics and in_topic_d boolean members. """ def __init__(self, encoding=’latin-1’): """ Initialise the superclass (HTMLParser) and reset the parser. Sets the encoding of the SGML files by default to latin-1. """ HTMLParser.__init__(self) self._reset() self.encoding = encoding def _reset(self): """ This is called only on initialisation of the parser class and when a new topic-body tuple has been generated. It resets all off the state so that a new tuple can be subsequently generated. """ self.in_body = False self.in_topics = False self.in_topic_d = False self.body = "" self.topics = [] self.topic_d = "" def parse(self, fd): """ parse accepts a file descriptor and loads the data in chunks in order to minimise memory usage. It then yields new documents as they are parsed. """ self.docs = [] for chunk in fd: self.feed(chunk.decode(self.encoding)) for doc in self.docs: yield doc self.docs = [] self.close() def handle_starttag(self, tag, attrs): """ This method is used to determine what to do when the parser comes across a particular tag of type "tag". In this instance we simply set the internal state booleans to True if that particular tag has been found. """ if tag == "reuters": pass elif tag == "body": self.in_body = True elif tag == "topics": self.in_topics = True elif tag == "d": self.in_topic_d = True def handle_endtag(self, tag): """ This method is used to determine what to do when the parser finishes with a particular tag of type "tag". If the tag is a <REUTERS> tag, then we remove all white-space with a regular expression and then append the topic-body tuple. If the tag is a <BODY> or <TOPICS> tag then we simply set the internal state to False for these booleans, respectively. If the tag is a <D> tag (found within a <TOPICS> tag), then we append the particular topic to the "topics" list and finally reset it. """ if tag == "reuters": self.body = re.sub(r’\s+’, r’ ’, self.body) self.docs.append( (self.topics, self.body) ) self._reset() elif tag == "body": self.in_body = False elif tag == "topics": self.in_topics = False elif tag == "d": self.in_topic_d = False self.topics.append(self.topic_d) self.topic_d = "" def handle_data(self, data): """ The data is simply appended to the appropriate member state for that particular tag, up until the end closing tag appears. """ if self.in_body: self.body += data elif self.in_topic_d: self.topic_d += data def obtain_topic_tags(): """ Open the topic list file and import all of the topic names taking care to strip the trailing "\n" from each word. """ topics = open( "data/all-topics-strings.lc.txt", "r" ).readlines() topics = [t.strip() for t in topics] return topics def filter_doc_list_through_topics(topics, docs): """ Reads all of the documents and creates a new list of two-tuples that contain a single feature entry and the body text, instead of a list of topics. It removes all geographic features and only retains those documents which have at least one non-geographic topic. """ ref_docs = [] for d in docs: if d[0] == [] or d[0] == "": continue for t in d[0]: if t in topics: d_tup = (t, d[1]) ref_docs.append(d_tup) break return ref_docs def create_tfidf_training_data(docs): """ Creates a document corpus list (by stripping out the class labels), then applies the TF-IDF transform to this list. The function returns both the class label vector (y) and the corpus token/feature matrix (X). """ # Create the training data class labels y = [d[0] for d in docs] # Create the document corpus list corpus = [d[1] for d in docs] # Create the TF-IDF vectoriser and transform the corpus vectorizer = TfidfVectorizer(min_df=1) X = vectorizer.fit_transform(corpus) return X, y def train_svm(X, y): """ Create and train the Support Vector Machine. """ svm = SVC(C=1000000.0, gamma="auto", kernel=’rbf’) svm.fit(X, y) return svm if __name__ == "__main__": # Create the list of Reuters data and create the parser files = ["data/reut2-%03d.sgm" % r for r in range(0, 22)] parser = ReutersParser() # Parse the document and force all generated docs into # a list so that it can be printed out to the console docs = [] for fn in files: for d in parser.parse(open(fn, ’rb’)): docs.append(d) # Obtain the topic tags and filter docs through it topics = obtain_topic_tags() ref_docs = filter_doc_list_through_topics(topics, docs) # Vectorise and TF-IDF transform the corpus X, y = create_tfidf_training_data(ref_docs) # Create the training-test split of the data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # Create and train the Support Vector Machine svm = train_svm(X_train, y_train) # Make an array of predictions on the test set pred = svm.predict(X_test) # Output the hit-rate and the confusion matrix for each model print(svm.score(X_test, y_test)) print(confusion_matrix(pred, y_test))

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noreply@github.com

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Shrekinator19/Unit3_Lesson5

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2020-04-09T13:33:06.903528

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py

name = 'Skye' print ('name')

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noreply@github.com

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faraazn/music-transcription

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2021-07-01T01:58:26.728091

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import tensorflow as tf import numpy as np from amt.protobuf import music_pb2 from amt.music import audio_io import wave import six def wav_to_num_frames(wav_audio, frames_per_second): # TODO: make a version using samples, sample rate, hop length """Transforms a wav-encoded audio string into number of frames.""" w = wave.open(six.BytesIO(wav_audio)) return np.int32(w.getnframes() / w.getframerate() * frames_per_second) path = "/home/faraaz/workspace/music-transcription/amt/models/onsets_frames/tfrecord/clean_midi_test.tfrecord" example = tf.train.Example() sample_rate = 16000 spec_hop_length = 512 frames_per_sec = sample_rate / spec_hop_length #print(len([record for record in tf.python_io.tf_record_iterator(path)])) for record in tf.python_io.tf_record_iterator(path): example.ParseFromString(record) #print(example) f = example.features.feature song_name = f['id'].bytes_list.value[0].decode('utf-8') wav_bytes = f['wav'].bytes_list.value[0] samples_bytes = f['audio'].float_list.value samples_array = np.asarray(samples_bytes, dtype="float32") firstsamples = audio_io.load_audio(song_name, 16000) wav_data = audio_io.samples_to_wav_data(firstsamples, 16000) og_samples_array = audio_io.wav_data_to_samples(wav_data, 16000) og_bytes = og_samples_array.tobytes() x = np.frombuffer(og_bytes, dtype=np.float32) assert wav_data == wav_bytes print(wav_to_num_frames(wav_data, frames_per_sec)) print(len(firstsamples)/512) break

[ "faraaz.nadeem@gmail.com" ]

faraaz.nadeem@gmail.com

c84c53395f7b921b51abae53e9b8bec22605a294

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[]

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kiyeonj21/nlp-tutorial

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2023-03-13T06:42:40.145721

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# code by Tae Hwan Jung(Jeff Jung) @graykode # Reference : https://github.com/hunkim/PyTorchZeroToAll/blob/master/14_2_seq2seq_att.py import numpy as np import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F import matplotlib.pyplot as plt dtype = torch.FloatTensor # S: Symbol that shows starting of decoding input # E: Symbol that shows starting of decoding output # P: Symbol that will fill in blank sequence if current batch data size is short than time steps sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E'] word_list = " ".join(sentences).split() word_list = list(set(word_list)) word_dict = {w: i for i, w in enumerate(word_list)} number_dict = {i: w for i, w in enumerate(word_list)} n_class = len(word_dict) # vocab list # Parameter n_hidden = 128 def make_batch(sentences): input_batch = [np.eye(n_class)[[word_dict[n] for n in sentences[0].split()]]] output_batch = [np.eye(n_class)[[word_dict[n] for n in sentences[1].split()]]] target_batch = [[word_dict[n] for n in sentences[2].split()]] # make tensor return Variable(torch.Tensor(input_batch)), Variable(torch.Tensor(output_batch)), Variable(torch.LongTensor(target_batch)) class Attention(nn.Module): def __init__(self): super(Attention, self).__init__() self.enc_cell = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) self.dec_cell = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # Linear for attention self.attn = nn.Linear(n_hidden, n_hidden) self.out = nn.Linear(n_hidden * 2, n_class) def forward(self, enc_inputs, hidden, dec_inputs): enc_inputs = enc_inputs.transpose(0, 1) # enc_inputs: [n_step(=n_step, time step), batch_size, n_class] dec_inputs = dec_inputs.transpose(0, 1) # dec_inputs: [n_step(=n_step, time step), batch_size, n_class] # enc_outputs : [n_step, batch_size, num_directions(=1) * n_hidden], matrix F # enc_hidden : [num_layers(=1) * num_directions(=1), batch_size, n_hidden] enc_outputs, enc_hidden = self.enc_cell(enc_inputs, hidden) trained_attn = [] hidden = enc_hidden n_step = len(dec_inputs) model = Variable(torch.empty([n_step, 1, n_class])) for i in range(n_step): # each time step # dec_output : [n_step(=1), batch_size(=1), num_directions(=1) * n_hidden] # hidden : [num_layers(=1) * num_directions(=1), batch_size(=1), n_hidden] dec_output, hidden = self.dec_cell(dec_inputs[i].unsqueeze(0), hidden) attn_weights = self.get_att_weight(dec_output, enc_outputs) # attn_weights : [1, 1, n_step] trained_attn.append(attn_weights.squeeze().data.numpy()) # matrix-matrix product of matrices [1,1,n_step] x [1,n_step,n_hidden] = [1,1,n_hidden] context = attn_weights.bmm(enc_outputs.transpose(0, 1)) dec_output = dec_output.squeeze(0) # dec_output : [batch_size(=1), num_directions(=1) * n_hidden] context = context.squeeze(1) # [1, num_directions(=1) * n_hidden] model[i] = self.out(torch.cat((dec_output, context), 1)) # make wn shape [n_step, n_class] return model.transpose(0, 1).squeeze(0), trained_attn def get_att_weight(self, dec_output, enc_outputs): # get attention weight one 'dec_output' with 'enc_outputs' n_step = len(enc_outputs) attn_scores = Variable(torch.zeros(n_step)) # attn_scores : [n_step] for i in range(n_step): attn_scores[i] = self.get_att_score(dec_output, enc_outputs[i]) # Normalize scores to weights in range 0 to 1 return F.softmax(attn_scores).view(1, 1, -1) def get_att_score(self, dec_output, enc_output): # enc_outputs [batch_size, num_directions(=1) * n_hidden] score = self.attn(enc_output) # score : [batch_size, n_hidden] return torch.dot(dec_output.view(-1), score.view(-1)) # inner product make scalar value input_batch, output_batch, target_batch = make_batch(sentences) # hidden : [num_layers(=1) * num_directions(=1), batch_size, n_hidden] hidden = Variable(torch.zeros(1, 1, n_hidden)) model = Attention() criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # Train for epoch in range(2000): optimizer.zero_grad() output, _ = model(input_batch, hidden, output_batch) loss = criterion(output, target_batch.squeeze(0)) if (epoch + 1) % 400 == 0: print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss)) loss.backward() optimizer.step() # Test test_batch = [np.eye(n_class)[[word_dict[n] for n in 'SPPPP']]] test_batch = Variable(torch.Tensor(test_batch)) predict, trained_attn = model(input_batch, hidden, test_batch) predict = predict.data.max(1, keepdim=True)[1] print(sentences[0], '->', [number_dict[n.item()] for n in predict.squeeze()]) # Show Attention fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(1, 1, 1) ax.matshow(trained_attn, cmap='viridis') ax.set_xticklabels([''] + sentences[0].split(), fontdict={'fontsize': 14}) ax.set_yticklabels([''] + sentences[2].split(), fontdict={'fontsize': 14}) plt.show()

[ "kiyeonj21@gmail.com" ]

kiyeonj21@gmail.com

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rashidalabri/forsa-web

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refs/heads/master

2022-12-13T00:29:11.112140

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#!/usr/bin/env python import os import sys if __name__ == '__main__': configuration = os.getenv('ENVIRONMENT', 'development').title() os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'forsa.settings') os.environ.setdefault('DJANGO_CONFIGURATION', configuration) try: from configurations.management import execute_from_command_line except ImportError as exc: raise ImportError( 'Couldn\'t import Django. Are you sure it\'s installed and ' 'available on your PYTHONPATH environment variable? Did you ' 'forget to activate a virtual environment?') from exc execute_from_command_line(sys.argv)

[ "itsrashidalabri@gmail.com" ]

itsrashidalabri@gmail.com

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krkettle57/moo

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from typing import List, get_args from moo.models.moo import MOO, CallResultSet, Target, TargetLengthOption class MOOCLIViewer: def start_verify(self, yes: str = "y", no: str = "n") -> str: return f"進行中のMOOが存在します。\n新しくMOOを開始しますか？[{yes}/{no}]: " def start_done(self) -> str: return "MOOを開始しました" def start_cancel(self) -> str: return "新しいMOOの開始を中止しました。" def giveup(self, target: Target) -> str: return f"MOOを終了します。ターゲットは{target.target}でした。" def clear(self, moo: MOO) -> str: return f"クリア！ターゲット: {moo.target.target}, コール数: {len(moo.called_results)}" def called_result(self, result: CallResultSet) -> str: return f"Call: {result.called.called}, {result.num_eat}-EAT, {result.num_bite}-BITE" def history(self, called_results: List[CallResultSet]) -> str: hist = [self.called_result(result) for result in called_results] return "\n".join(["[Called History]"] + hist) def no_moo_on_play(self) -> str: return "進行中のMOOが存在しません。 startコマンドで開始して下さい。" def no_moo_started(self) -> str: return "MOOのプレイ記録が存在しません。startコマンドで開始して下さい。" def invalid_target_length(self) -> str: options = ", ".join([str(i) for i in get_args(TargetLengthOption)]) return f"ターゲットの桁数は{options}のいずれかで入力して下さい。" def invalid_call_length(self, target_length: int) -> str: return f"コールする値は{target_length}桁で入力して下さい。" def invalid_call_value(self) -> str: return "コールする値の各桁は0~9のいずれかで入力して下さい。"

[ "krkettle57@gmail.com" ]

krkettle57@gmail.com

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/Assignment 3 - Recursion/Question1_power.py

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[]

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ginajoerger/Data-Structures

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refs/heads/main

2023-02-02T22:58:54.891519

2020-12-21T00:50:33

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def power(x,n): ''' @x: the base, integer @n: the exponent, integer x, n can be negative integer. @return: x^n ''' if n == 0: return 1 if n >= 1: return x * power(x, n-1) if n <= -1: return (1/power(x, -n)) def main(): print(power(-2, 4)) # 16 print(power(4, 3)) # 64 print(power(-2, -3)) # -0.125 #main()

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noreply@github.com

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/tensorforce/core/policies/state_value.py

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[ "Apache-2.0" ]

permissive

tensorforce/tensorforce

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refs/heads/master

2023-08-17T17:35:34.578444

2023-08-14T20:14:08

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# Copyright 2020 Tensorforce Team. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from tensorforce.core import SignatureDict, TensorSpec, tf_function from tensorforce.core.policies import BasePolicy class StateValue(BasePolicy): """ Base class for state-value functions, here categorized as "degenerate" policy. Args: device (string): Device name (default: inherit value of parent module). l2_regularization (float >= 0.0): Scalar controlling L2 regularization (default: inherit value of parent module). name (string): internal use. states_spec (specification): internal use. auxiliaries_spec (specification): internal use. actions_spec (specification): internal use. """ def __init__( self, *, device=None, l2_regularization=None, name=None, states_spec=None, auxiliaries_spec=None, actions_spec=None ): BasePolicy.__init__( self=self, device=device, l2_regularization=l2_regularization, name=name, states_spec=states_spec, auxiliaries_spec=auxiliaries_spec, actions_spec=actions_spec ) def input_signature(self, *, function): if function == 'state_value': return SignatureDict( states=self.states_spec.signature(batched=True), horizons=TensorSpec(type='int', shape=(2,)).signature(batched=True), internals=self.internals_spec.signature(batched=True), auxiliaries=self.auxiliaries_spec.signature(batched=True) ) else: return super().input_signature(function=function) def output_signature(self, *, function): if function == 'state_value': return SignatureDict( singleton=TensorSpec(type='float', shape=()).signature(batched=True) ) else: return super().output_signature(function=function) @tf_function(num_args=4) def state_value(self, *, states, horizons, internals, auxiliaries): raise NotImplementedError

[ "alexkuhnle@t-online.de" ]

alexkuhnle@t-online.de

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/adaboost_ex.py

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[]

no_license

timellemit/scikit_learn_practice

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2021-01-22T05:53:56.345852

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import pylab as pl import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import make_gaussian_quantiles # Construct dataset X1, y1 = make_gaussian_quantiles(cov=2., n_samples=200, n_features=2, n_classes=2, random_state=1) X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5, n_samples=300, n_features=2, n_classes=2, random_state=1) X = np.concatenate((X1, X2)) y = np.concatenate((y1, - y2 + 1)) # Create and fit an AdaBoosted decision tree bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), algorithm="SAMME", n_estimators=200) bdt.fit(X, y) plot_colors = "br" plot_step = 0.02 class_names = "AB" pl.figure(figsize=(10, 5)) # Plot the decision boundaries pl.subplot(121) x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)) Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) cs = pl.contourf(xx, yy, Z, cmap=pl.cm.Paired) pl.axis("tight") # Plot the training points for i, n, c in zip(range(2), class_names, plot_colors): idx = np.where(y == i) pl.scatter(X[idx, 0], X[idx, 1], c=c, cmap=pl.cm.Paired, label="Class %s" % n) pl.xlim(x_min, x_max) pl.ylim(y_min, y_max) pl.legend(loc='upper right') pl.xlabel("Decision Boundary") # Plot the two-class decision scores twoclass_output = bdt.decision_function(X) plot_range = (twoclass_output.min(), twoclass_output.max()) pl.subplot(122) for i, n, c in zip(range(2), class_names, plot_colors): pl.hist(twoclass_output[y == i], bins=10, range=plot_range, facecolor=c, label='Class %s' % n, alpha=.5) x1, x2, y1, y2 = pl.axis() pl.axis((x1, x2, y1, y2 * 1.2)) pl.legend(loc='upper right') pl.ylabel('Samples') pl.xlabel('Decision Scores') pl.subplots_adjust(wspace=0.25) pl.show()

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yury.kashnitsky@gmail.com

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/company/migrations/0003_alter_contactusmodel_created.py

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alienone305/jewlery

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2023-08-26T12:33:03.974500

2021-11-04T16:48:35

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# Generated by Django 3.2 on 2021-08-07 15:05 import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('company', '0002_alter_contactusmodel_created'), ] operations = [ migrations.AlterField( model_name='contactusmodel', name='created', field=models.DateTimeField(default=datetime.datetime(2021, 8, 7, 15, 5, 29, 722621, tzinfo=utc)), ), ]

[ "alienone305@gmail.com" ]

alienone305@gmail.com

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/client.py

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[]

no_license

rajkumar368/socket

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refs/heads/master

2023-08-14T23:50:12.103411

2021-10-17T17:45:18

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UTF-8

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py

import socket conn = socket.socket() conn.connect(("localhost", 9998)) name = input("name please") conn.send((name).encode()) print(conn.recv(1024).decode()) conn.close()

[ "rathorerajkumar368@gmail.com" ]

rathorerajkumar368@gmail.com

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/tuples.py

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no_license

randomguy069/PythonPractice

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2021-08-31T14:34:14.060996

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py

t = ("Hello","World","Ibrahimovic") print(dir(t))

[ "noreply@github.com" ]

noreply@github.com

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/test_proj/IPsamp/modules/StaticFullGrowthModel/HuangFullFunc.py

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csivilo/IPMP

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2021-01-09T20:21:05.661063

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# -*- coding: utf-8 -*- """ Created on Fri Mar 08 22:30:34 2013 @author: Lihan_Huang """ import numpy as np class HuangFullModelFunc(): def __init__(self, Ymax, Y0, mumax, Lag, x): b = x + 0.25*np.log(1 + np.exp(-4.0*(x-Lag))) - 0.25*np.log(1+np.exp(4.0*Lag)) self.HuangFull = Y0 + Ymax -np.log(np.exp(Y0) + (np.exp(Ymax)-np.exp(Y0))*np.exp(-mumax*b)) print x, self.HuangFull def main(): Y0 = 2.0*2.303 Ymax = 8.5*2.303 mumax = 2.0 Lag = 5.0 # generate an x array or list x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) HuangFullModel = HuangFullModelFunc(Ymax, Y0, mumax, Lag, x) if __name__ == '__main__': main()

[ "carlo@sivilotti.com" ]

carlo@sivilotti.com

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/125_algorithms/_examples/_algorithms_challenges/pybites/intermediate/intermediate-bite-14-generate-a-table-of-n-sequences.py

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[]

no_license

syurskyi/Python_Topics

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refs/heads/master

2023-06-08T19:29:16.214395

2023-05-29T17:09:11

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""" DATE: 05 Nov 2020 TASK DESCRIPTION: Write a function that receives one or more sequences. The sequences are already defined for you. The function should return a table (list of strings) where the columns are the sequences (example below). To keep it simple we work with equally sized sequences so you don't have to worry about handling a missing value (you should end up with a grid of 6 rows x n columns). There are some Pythonic idioms you can use here, hint: think of pants ;) Example call (look at the tests for more detail): >>> generate_table(names, aliases) ['Julian | Pythonista', 'Bob | Nerd', 'PyBites | Coder', 'Dante | Pythonista', 'Martin | Nerd', 'Rodolfo | Coder'] Bonus: use a generator to build up the table rows. """ import random names = 'Julian Bob PyBites Dante Martin Rodolfo'.split() aliases = 'Pythonista Nerd Coder'.split() * 2 points = random.sample(range(81, 101), 6) awake = [True, False] * 3 SEPARATOR = ' | ' ### ----------- My solution --------------------------- def my_generate_table(*args): l = [] result = zip(*args) for i in result: s = "" for t in i: if s == "": s = str(t) else: s = s + " | " + str(t) l.append(s) return l ### ---------- PyBites original solution --------------- def pyb_generate_table(*sequences): for seq in zip(*sequences): seq = [str(val) for val in seq] yield SEPARATOR.join(seq)

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

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/django_signup/cminji_signup/cminji_signup/settings.py

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c-min-ji/LikeLion_8th

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""" Django settings for cminji_signup project. Generated by 'django-admin startproject' using Django 3.1. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve(strict=True).parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '#1dn^xmakc4ehngc!lme-dh@1$4qld6)zh&a+ar0whgn8au3kg' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'accounts', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'cminji_signup.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'cminji_signup.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' #Email을 발송하는 유저 EMAIL_HOST = 'smtp.gmail.com' #Gmail 사용위한 Port number - 권장사항 EMAIL_PORT = '587' EMAIL_HOST_USER = 'mjeewh@gmail.com' #실제 비번 쓰기 EMAIL_HOST_PASSWORD = '*******' #TLS EMAIL_USE_TLS = True #회신에 대한 기본 설정 DEFAULT_FROM_EMAIL = EMAIL_HOST_USER

[ "mjeewh@gmail.com" ]

mjeewh@gmail.com

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/musicbot/exceptions.py

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Dodek69/ChudyBot

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2020-04-20T17:28:18.159390

2019-02-14T19:44:55

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import shutil import textwrap class MusicbotException(Exception): def __init__(self, message, *, expire_in=0): super().__init__(message) self._message = message self.expire_in = expire_in @property def message(self): return self._message @property def message_no_format(self): return self._message class CommandError(MusicbotException): pass class ExtractionError(MusicbotException): pass class WrongEntryTypeError(ExtractionError): def __init__(self, message, is_playlist, use_url): super().__init__(message) self.is_playlist = is_playlist self.use_url = use_url class FFmpegError(MusicbotException): pass class FFmpegWarning(MusicbotException): pass class SpotifyError(MusicbotException): pass class PermissionsError(CommandError): @property def message(self): return "You don't have permission to use that command.\nReason: " + self._message class HelpfulError(MusicbotException): def __init__(self, issue, solution, *, preface="An error has occured:", footnote='', expire_in=0): self.issue = issue self.solution = solution self.preface = preface self.footnote = footnote self.expire_in = expire_in self._message_fmt = "\n{preface}\n{problem}\n\n{solution}\n\n{footnote}" @property def message(self): return self._message_fmt.format( preface = self.preface, problem = self._pretty_wrap(self.issue, " Problem:"), solution = self._pretty_wrap(self.solution, " Solution:"), footnote = self.footnote ) @property def message_no_format(self): return self._message_fmt.format( preface = self.preface, problem = self._pretty_wrap(self.issue, " Problem:", width=None), solution = self._pretty_wrap(self.solution, " Solution:", width=None), footnote = self.footnote ) @staticmethod def _pretty_wrap(text, pretext, *, width=-1): if width is None: return '\n'.join((pretext.strip(), text)) elif width == -1: pretext = pretext.rstrip() + '\n' width = shutil.get_terminal_size().columns lines = textwrap.wrap(text, width=width - 5) lines = ((' ' + line).rstrip().ljust(width-1).rstrip() + '\n' for line in lines) return pretext + ''.join(lines).rstrip() class HelpfulWarning(HelpfulError): pass class Signal(Exception): pass class RestartSignal(Signal): pass class TerminateSignal(Signal): pass

[ "dodek@vip.interia.pl" ]

dodek@vip.interia.pl

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/bambi/chapter03/knock21.py

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[]

no_license

tmu-nlp/100knock2017

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refs/heads/master

2021-01-19T17:36:53.328997

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import json, re def findTextFromTitle(title): with open('jawiki-country.json') as json_data: lines = json_data.readlines() for line in lines: data = json.loads(line) if data["title"] == title: return data["text"] def getEnglandArticle(): return findTextFromTitle("イギリス") def getCategoryRawList(content): return re.findall(r"\[\[Category:.*?\]\]", content) result = getCategoryRawList(getEnglandArticle()) print("\n".join(result)) #改行したほうが見やすい(result is list)

[ "mingchanbambina@gmail.com" ]

mingchanbambina@gmail.com

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[ "Apache-2.0" ]

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2023-09-01T19:29:43.013318

2023-08-31T08:28:59

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py

# coding: utf-8 import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class PostSentenceEmbeddingReq: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'sentences': 'list[str]', 'domain': 'str' } attribute_map = { 'sentences': 'sentences', 'domain': 'domain' } def __init__(self, sentences=None, domain=None): """PostSentenceEmbeddingReq The model defined in huaweicloud sdk :param sentences: 文本列表，文本长度为1~512，列表大小为1~1000，文本编码为UTF-8。 :type sentences: list[str] :param domain: 支持的领域类型，目前只支持通用领域，默认为general。 :type domain: str """ self._sentences = None self._domain = None self.discriminator = None self.sentences = sentences if domain is not None: self.domain = domain @property def sentences(self): """Gets the sentences of this PostSentenceEmbeddingReq. 文本列表，文本长度为1~512，列表大小为1~1000，文本编码为UTF-8。 :return: The sentences of this PostSentenceEmbeddingReq. :rtype: list[str] """ return self._sentences @sentences.setter def sentences(self, sentences): """Sets the sentences of this PostSentenceEmbeddingReq. 文本列表，文本长度为1~512，列表大小为1~1000，文本编码为UTF-8。 :param sentences: The sentences of this PostSentenceEmbeddingReq. :type sentences: list[str] """ self._sentences = sentences @property def domain(self): """Gets the domain of this PostSentenceEmbeddingReq. 支持的领域类型，目前只支持通用领域，默认为general。 :return: The domain of this PostSentenceEmbeddingReq. :rtype: str """ return self._domain @domain.setter def domain(self, domain): """Sets the domain of this PostSentenceEmbeddingReq. 支持的领域类型，目前只支持通用领域，默认为general。 :param domain: The domain of this PostSentenceEmbeddingReq. :type domain: str """ self._domain = domain def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): """For `print`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, PostSentenceEmbeddingReq): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "hwcloudsdk@huawei.com" ]

hwcloudsdk@huawei.com

d0f6f3767403a0426f929d980d9fc2eff41bd052

8eabf994204c8dcd4fa2e1903ee3bc413779fd5e

/Cleaning_Merging/Preprocessing.py

6a64fba645740d82e5e462ec632c155028ad73d5

[]

no_license

rajdua22/tennis_betting

f12eff365420a7103e286050383568e057419c62

d09acee14576eb19e12fed2bd41e7f16a9eb4dbf

refs/heads/master

2020-07-26T06:54:06.467898

2019-12-21T10:52:44

208,569,545

2

0

null

UTF-8

Python

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7,610

py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jul 1 10:52:54 2019 @author: rajdua Description: Loads in dataset and completes basic data cleaning and preprocessing. """ import pandas as pd import numpy as np import matplotlib.pyplot as plt import time import sys data = pd.read_csv('Final Merged.csv', encoding = "ISO-8859-1", low_memory=False) data['matches'] = 1 data['Date'] = pd.to_datetime(data['Date']) def DSW(x): if (x > 0) & (x < 3): return 1; else: return 0 def CSW(x): if (x > 2) & (x < 6): return 1; else: return 0 def Tie(x,y): if (x == 7) & (y == 6): return 1 else: return 0 data['DSW_W1'] = data['L1'].apply(DSW) data['DSW_W2'] = data['L2'].apply(DSW) data['DSW_W3'] = data['L3'].apply(DSW) data['DSW_W4'] = data['L4'].apply(DSW) data['DSW_W5'] = data['L5'].apply(DSW) data['DSW'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data['L1'].apply(CSW) data['DSW_W2'] = data['L2'].apply(CSW) data['DSW_W3'] = data['L3'].apply(CSW) data['DSW_W4'] = data['L4'].apply(CSW) data['DSW_W5'] = data['L5'].apply(CSW) data['CSW'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data['W1'].apply(DSW) data['DSW_W2'] = data['W2'].apply(DSW) data['DSW_W3'] = data['W3'].apply(DSW) data['DSW_W4'] = data['W4'].apply(DSW) data['DSW_W5'] = data['W5'].apply(DSW) data['DSL'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data['W1'].apply(CSW) data['DSW_W2'] = data['W2'].apply(CSW) data['DSW_W3'] = data['W3'].apply(CSW) data['DSW_W4'] = data['W4'].apply(CSW) data['DSW_W5'] = data['W5'].apply(CSW) data['CSL'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data['L1'].apply(DSW) data['DSW_W2'] = data['L2'].apply(DSW) data['DSW_W3'] = data['L3'].apply(DSW) data['DSW_W4'] = data['L4'].apply(DSW) data['DSW_W5'] = data['L5'].apply(DSW) data['DSW'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data['L1'].apply(CSW) data['DSW_W2'] = data['L2'].apply(CSW) data['DSW_W3'] = data['L3'].apply(CSW) data['DSW_W4'] = data['L4'].apply(CSW) data['DSW_W5'] = data['L5'].apply(CSW) data['CSW'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data.apply(lambda row: Tie(row['W1'], row['L1']), axis = 1) data['DSW_W2'] = data.apply(lambda row: Tie(row['W2'], row['L2']), axis = 1) data['DSW_W3'] = data.apply(lambda row: Tie(row['W3'], row['L3']), axis = 1) data['DSW_W4'] = data.apply(lambda row: Tie(row['W4'], row['L4']), axis = 1) data['DSW_W5'] = data.apply(lambda row: Tie(row['W5'], row['L5']), axis = 1) data['TieW'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['DSW_W1'] = data.apply(lambda row: Tie(row['L1'], row['W1']), axis = 1) data['DSW_W2'] = data.apply(lambda row: Tie(row['L2'], row['W2']), axis = 1) data['DSW_W3'] = data.apply(lambda row: Tie(row['L3'], row['W3']), axis = 1) data['DSW_W4'] = data.apply(lambda row: Tie(row['L4'], row['W4']), axis = 1) data['DSW_W5'] = data.apply(lambda row: Tie(row['L5'], row['W5']), axis = 1) data['TieL'] = data.apply(lambda row: row.DSW_W1 + row.DSW_W2 + row.DSW_W3 + row.DSW_W4 + row.DSW_W5, axis=1) data['SetsCompleted'] = data['DSW'] + data['DSL'] + data['CSW'] + data['CSL'] + data['TieW'] + data['TieL'] # '1' indicates quarterfinal or more important match def stage(x): if (x == 'F') | (x == 'SF') | (x == 'QF'): return 1; else: return 0 data['SOT'] = data['round'].apply(stage) data['Major'] = (data['Series'] == 'Grand Slam').astype(int) data = data.drop(columns = ['Unnamed: 0', 'DSW_W1', 'DSW_W2', 'DSW_W3', 'DSW_W4', 'DSW_W5']) data['games'] = data[['W1', 'L1', 'W2', 'L2', 'W3', 'L3', 'W4','L4', 'W5', 'L5']].sum(axis = 1) data['oddsw'] = data[['CBW', 'GBW', 'IWW', 'SBW', 'B365W', 'B&WW', 'EXW', 'PSW', 'UBW', 'LBW', 'SJW']].mean(axis = 1) data['oddsl'] = data[['CBL', 'GBL', 'IWL', 'SBL', 'B365L', 'B&WL', 'EXL', 'PSL', 'UBL', 'LBL', 'SJL']].mean(axis = 1) data = data.dropna(subset = ['oddsw']) data = data.dropna(subset = ['oddsl']) data = data.reset_index(drop = True) def underdog (x): if (x.oddsl > x.oddsw): return 0 else: return 1 data['underdogWon'] = data.apply(underdog, axis = 1) # Copp data= data[~data['WRank'].isnull()] data= data[~data['LRank'].isnull()] data= data[data['WRank'].str.isnumeric()] data= data[data['LRank'].str.isnumeric()] data['WRank'] = pd.to_numeric(data['WRank']) data['LRank'] = pd.to_numeric(data['LRank']) data = data.reset_index(drop = True) # Added 6/16 - Get win percentage for all ranks agasint each other winners = data['WRank'] winners = pd.unique(winners) winners = winners.tolist() winners.sort() losers = data['LRank'] losers = pd.unique(losers) losers = losers.tolist() losers.sort() players = list (set(winners) | set(losers)) players.sort() winners = data['WRank'] losers = data['LRank'] old_dict = dict(enumerate(players)) new_dict = dict([(value, key) for key, value in old_dict.items()]) winners = winners.map(new_dict) losers = losers.map(new_dict) matches = pd.concat([winners, losers], axis=1) m = len(players) results = np.zeros(shape=(m,m)) for index, row in matches.iterrows(): results[row['WRank'], row['LRank']] += 1 n = len(matches) percent = np.zeros(n) totalM = np.zeros(n) for index, row in matches.iterrows(): currentM = 0 wins = 0 losses = 0 wrank = row['WRank'] print("wrank: ", wrank) lrank = row['LRank'] print ("lrank: ", lrank) if (wrank < m) & (wrank >= 0): if (lrank < m) & (lrank >= 0): wins = results[wrank, lrank] print("wins: ", wins) losses = results[lrank, wrank] print("losses: ", losses) currentM += wins currentM += losses print("currentM: ", currentM) i = 1 while currentM < 20: twins = 0 tlosses = 0 for j in range(wrank - i, wrank + i): for k in range(lrank - i, lrank + i): if ((j < m) & (j >= 0)): if ((k < m) & (k >= 0)): twins += results[j, k] tlosses += results[k, j] currentM += twins currentM += tlosses wins += twins losses += tlosses i+= 1 print ("wins: ", wins) print ("losses ", losses) print(currentM) if index < n: totalM[index] = wins + losses if wins + losses == 0: percent[index] = np.nan elif losses == 0: percent[index] = 1 elif wins == 0: percent[index] = 0 elif (wins>0) & (losses > 0): percent[index] = wins / (wins + losses) data['CoppW'] = percent data['CoppL'] = 1 - percent data['MatchesPlayed'] = totalM # Create a 0/1 column for surface data['Clay'] = data.Surface == 'Clay' data['Clay'] *= 1 data['Hard'] = data.Surface == 'Hard' data['Hard'] *= 1 data['Grass'] = data.Surface == 'Grass' data['Grass'] *= 1 data['Carpet'] = data.Surface == 'Carpet' data['Carpet'] *= 1 data['WInverseRank'] = data['WRank'].apply(lambda x: 1 /x) data['LInverseRank'] = data['LRank'].apply(lambda x: 1 /x) data.to_csv('Final Merged1.csv')

[ "rajvirdu@usc.edu" ]

rajvirdu@usc.edu

43ef6671cbd2943a73a2201439c31fdfc5c0ad9c

54a745510b16111f5e5f610a07be49ea1e79fccf

/py1810/hello_mysql_01.py

086070e7afa84b55e4f9c256485a983058c32dcc

[]

no_license

SonDog0/bigdata

84a5b7c58ad9680cdc0e49ac6088f482e09118a5

e6cd1e3bbb0bfec0c89a31b3fb4ef66d50c272be

refs/heads/master

2020-04-22T02:24:16.469718

2019-03-13T08:59:26

170,047,330

0

null

UTF-8

Python

false

2,590

py

# 파이썬으로 MySQL, MariaDB 다루기 # python에서 MySQL 데이터베이스를 지원하려면 # python DB API 규약에 맞게 작성된 mySQL DB 모듈 필요 # 일반적으로 pyMySQL 모듈을 많이 사용 import pymysql # # mysql connection 생성 # conn = pymysql.connect(host='13.209.88.188', user= 'son', password= '931027',db='SON_MARIADB', charset='utf8') # # curs = conn.cursor() # # curs.execute('DROP TABLE items') # curs.execute('''create table items( item_id INTEGER PRIMARY KEY AUTO_INCREMENT, name TEXT, price INTEGER)''' ) # # sql 질의문 실행 # sql = 'select * from books' # curs.execute(sql) # # # 결과 집합 처리 # for rs in curs.fetchall(): # print(rs[0], rs[1], rs[2], rs[3]) #배열 기반 커서 # # # # # #mysql connection 닫기 # conn.close() # # mysql connection 생성 # conn = pymysql.connect(host='13.209.88.188', user= 'son', password= '931027',db='SON_MARIADB', charset='utf8') # # connection 으로부터 dict cursor 생성 # curs = conn.cursor(pymysql.cursors.DictCursor) # # # sql 질의문 실행 # sql = 'select * from books' # curs.execute(sql) # # # 결과 집합 처리 # for rs in curs.fetchall(): # print(rs['bno'], rs['bname'], rs['bpub'], rs['bprice']) #사전기반 커서 # # #mysql connection 닫기 # conn.close() # 1~100 까지 2배수, 3배수, 5배수 저장 # 테이블 이름은 numbers # 필드는 no, no2, no3, no5 # mysql connection 생성 conn = pymysql.connect(host='13.209.88.188', user= 'son', password= '931027',db='SON_MARIADB', charset='utf8') # connection 으로부터 cursor 생성 curs = conn.cursor(pymysql.cursors.DictCursor) # sql 질의문 실행 create_sql = 'create table numbers( no2 int, no3 int, no5 int )' drop_sql = 'drop table numbers' sql = 'insert into numbers values(%s,%s,%s)' # sql = 'select * from books' curs.execute(drop_sql) curs.execute(create_sql) # 1~ 100까지 2배수, 3배수, 5배수 num1 = 0 num2 = 0 num3 = 0 for i in range (1,101): if i % 2 == 0: num1 = i else: num1 = 0 if i % 3 == 0: num2 = i else: num2 = 0 if i % 5 == 0: num3 = i else: num3 = 0 curs.execute(sql, (num1, num2, num3)) #변경사항 서버에 적용하기 conn.commit() # 결과 집합 처리 select_sql = 'select * from numbers' curs.execute(select_sql) for rs in curs.fetchall(): print(rs['no2'], rs['no3'], rs['no5']) #사전기반 커서 #mysql connection 닫기 conn.close()

[ "noreply@github.com" ]

noreply@github.com

efc5d69a2a469d2882f61cce39f0d465c7cad7f4

38b843fd84b1c7c8b23b302538d27fdb77520ef7

/testclient.py

48efa3b5b1087835dbef38913cf55a58fc7feda9

[ "WTFPL" ]

permissive

David-OC/IngredStore

5ce1f81078dacdf4983f6863b25f35355fe4a42b

584f6a13176529a6b89405d4ca18f95a4407d2b8

refs/heads/master

2021-01-10T18:01:30.394800

2016-02-19T11:01:55

51,767,118

0

null

UTF-8

Python

false

247

py

import sys from ingredstore import IngredStore def main(argv): ingredinst = IngredStore(argv[1]) ingredients = ingredinst.get_ingredients() for item in ingredients: print item if __name__ == "__main__": main(sys.argv)

[ "mrdavidoc@gmail.com" ]

mrdavidoc@gmail.com

83f1e135d224d46ae1f5b6a914d4e8ae20c41a87

5d10705fda6f9c38cc28f33a052eb1981eef825d

/modeling/input/gen_grid.py

d2bc32b3e4d9b175041c5b63fcb143fe7a6f6c42

[]

no_license

raryskin/division_of_labor

11140b0a37c47512ce5404af0a4b13268bf20d47

30ed0b161782e83170438965ceb3e74a2bc949d9

refs/heads/master

2023-02-18T22:36:04.695745

2021-01-07T07:08:32

null

0

null

UTF-8

Python

false

754

py

import csv import numpy as np chainNum = 1 with open('coarse-grid.csv', 'w') as csv_file : writer = csv.writer(csv_file, delimiter=',') for alpha in [x for x in np.linspace(1,10,num=9)] : for perspectiveCost in np.linspace(0, 1, num=21) : for uttCost in [0.0001, 0.001, 0.009, 0.01, 0.02, 0.03, 0.05, 0.075, 0.1] : writer.writerow([round(perspectiveCost, 2), round(alpha,2), round(uttCost, 2), chainNum]) chainNum = chainNum + 1 chainNum = 40000 with open('fine-grid.csv', 'w') as csv_file : writer = csv.writer(csv_file, delimiter=',') for perspectiveCost in np.linspace(0, 0.5, num=21) : writer.writerow([perspectiveCost, 2, 0.03, chainNum]) chainNum = chainNum + 1

[ "hawkrobe@gmail.com" ]

hawkrobe@gmail.com

89ed2675d9419d6d488d990f069ccd3e7cb482ae

141b3c810ab8612cd6d9bb89f79d7ce9ac3ce716

/parsetab.py

7efe81f50dd3a07918d30dbe52b7bdfa16f40a35

[]

no_license

TeddyDotNet/pyNase

61c2cb47432e983f00f8e80f113606b2ede32453

42d5b2ed131c2b52a4b98c6160ef6126f607ba6b

refs/heads/master

2020-05-18T16:39:03.747470

2012-12-05T09:28:13

7,014,726

1

0

null

UTF-8

Python

false

12,688

py

# parsetab.py # This file is automatically generated. Do not edit. _lr_method = 'SLR' _lr_signature = '\xce\xfe\xae\xcd\xa2<`\xbc\xd1\x0f\x9c\xe9\xd9\xa8\xef\n' _lr_action_items = {'INLINE_ELSE_SYMBOL':([36,72,32,28,70,35,69,12,48,71,84,38,55,83,37,73,16,68,58,39,29,50,67,57,0,21,49,44,34,51,],[-50,79,-50,-24,-26,-29,-50,-50,-47,-50,-50,-50,-21,-35,-50,-20,-50,-48,-50,-25,-31,-49,-30,-18,-50,-50,-50,-27,-28,-19,]),'PLUS_SYMBOL':([29,67,16,39,49,58,84,36,0,35,68,50,37,70,34,69,12,38,48,21,71,83,44,28,32,],[-31,-30,-50,-25,-50,-50,-50,53,-50,-29,-48,-49,-50,-26,-28,53,-50,53,-47,-50,-50,-35,-27,-24,-50,]),'LE_SYMBOL':([44,32,39,51,49,16,58,35,84,36,0,70,80,45,37,73,50,28,69,21,57,71,83,34,12,38,48,68,29,55,67,],[-27,-50,-25,-19,-50,-50,-50,-29,-50,-50,-50,-26,60,60,-50,-20,-49,-24,-50,-50,-18,-50,-35,-28,-50,-50,-47,-48,-31,-21,-30,]),'LT_SYMBOL':([68,12,44,34,21,57,32,45,80,51,71,83,16,48,29,35,69,50,37,0,38,55,49,58,67,39,84,28,70,73,36,],[-48,-50,-27,-28,-50,-18,-50,61,61,-19,-50,-35,-50,-47,-31,-29,-50,-49,-50,-50,-50,-21,-50,-50,-30,-25,-50,-24,-26,-20,-50,]),'INLINE_IF_SYMBOL':([52,42,77,54,79,31,62,65,40,59,22,76,61,66,82,43,30,74,60,41,27,63,53,64,],[-23,30,-39,30,30,30,30,-44,-32,-46,30,-40,-41,30,30,-33,30,30,-42,-34,30,-43,-22,-45,]),'DIVIDE_SYMBOL':([38,84,71,49,0,12,48,21,67,34,29,28,16,37,35,36,50,69,58,83,68,32,],[-50,-50,-50,-50,-50,-50,-47,-50,-30,-28,-31,43,-50,-50,-29,-50,-49,-50,43,-35,-48,-50,]),'COMMA_SYMBOL':([37,16,],[23,23,]),'ANY_DIGIT':([61,54,53,49,79,59,40,60,62,76,41,74,42,52,31,43,82,32,27,65,77,22,30,63,64,66,],[-41,32,-22,49,32,-46,-32,-42,32,-40,-34,32,32,-23,32,-33,32,49,32,-44,-39,32,32,-43,-45,32,]),'$':([49,28,71,12,16,58,84,36,18,0,20,37,19,8,38,26,21,69,10,32,],[-50,-50,-50,-50,-50,-50,-50,-50,-5,-50,-4,-50,-2,-1,-50,-3,-50,-50,0,-50,]),'MINUS_SYMBOL':([34,16,65,29,63,22,76,64,28,71,48,0,83,60,66,37,59,70,77,12,50,36,67,31,49,84,35,44,30,79,61,62,74,69,32,82,68,39,58,38,21,],[-28,-50,-44,-31,-43,27,-40,-45,-24,-50,-47,-50,-35,-42,27,-50,-46,-26,-39,-50,-49,52,-30,27,-50,-50,-29,-27,27,27,-41,27,27,52,-50,27,-48,-25,-50,52,-50,]),'CLOSE_PARAENTHESIS_SYMBOL':([39,84,69,67,49,51,0,12,83,38,71,55,16,50,36,37,21,44,28,48,70,29,35,32,68,34,47,73,57,58,],[-25,-50,-50,-30,-50,-19,-50,-50,-35,-50,-50,-21,-50,-49,-50,-50,-50,-27,-24,-47,-26,-31,-29,-50,-48,-28,67,-20,-18,-50,]),'EQ_SYMBOL':([70,28,80,44,73,37,21,58,67,51,0,29,39,34,68,57,83,38,48,12,49,84,50,16,45,71,55,69,36,32,35,],[-26,-24,63,-27,-20,-50,-50,-50,-30,-19,-50,-31,-25,-28,-48,-18,-35,-50,-47,-50,-50,-50,-49,-50,63,-50,-21,-50,-50,-50,-29,]),'MODULO_SYMBOL':([38,37,48,35,29,49,16,84,69,32,83,71,34,68,36,21,12,28,58,67,0,50,],[-50,-50,-47,-29,-31,-50,-50,-50,-50,-50,-35,-50,-28,-48,-50,-50,-50,41,41,-30,-50,-49,]),'IDENTIFIER':([9,82,12,21,76,52,27,62,74,63,1,54,61,3,64,40,43,77,79,66,41,30,23,59,42,31,60,18,53,65,5,22,0,],[-16,35,14,14,-40,-23,35,35,35,-43,15,35,-41,16,-45,-32,-33,-39,35,35,-34,35,37,-46,35,35,-42,-5,-22,-44,17,35,14,]),'DELIMITER_SYMBOL':([71,17,24,21,35,11,44,25,32,50,38,33,73,28,29,36,51,2,58,68,48,37,49,0,56,55,39,6,69,57,7,84,4,67,18,15,12,70,16,13,20,83,34,],[-50,-11,-15,-50,-29,18,-27,-13,-50,-49,-50,-17,-20,-24,-31,-50,-19,-7,-50,-48,-47,-50,-50,-50,-14,-21,-25,-10,-50,-18,-9,-50,-8,-30,-5,-12,-50,-26,-50,-6,-10,-35,-28,]),'ASSIGN_SYMBOL':([14,],[22,]),'OPEN_PARAENTHESIS_SYMBOL':([27,52,41,76,42,77,30,66,54,53,61,31,59,79,60,43,22,82,74,62,40,64,63,65,],[31,-23,-34,-40,31,-39,31,31,31,-22,-41,31,-46,31,-42,-33,31,31,31,31,-32,-45,-43,-44,]),'TIMES_SYMBOL':([29,21,34,0,69,36,35,58,83,84,71,32,28,68,49,37,50,16,38,67,48,12,],[-31,-50,-28,-50,-50,-50,-29,40,-35,-50,-50,-50,40,-48,-50,-50,-49,-50,-50,-30,-47,-50,]),'INLINE_FI_SYMBOL':([39,36,50,68,37,55,69,70,83,0,12,58,44,71,28,29,16,73,57,48,35,32,51,84,34,21,38,49,67,81,],[-25,-50,-49,-48,-50,-21,-50,-26,-35,-50,-50,-50,-27,-50,-24,-31,-50,-20,-18,-47,-29,-50,-19,-50,-28,-50,-50,-50,-30,83,]),'WRITE_SYMBOL':([21,0,12,18,],[1,1,1,-5,]),'NE_SYMBOL':([68,32,12,36,55,57,37,16,51,38,84,48,50,45,80,21,70,73,35,44,39,0,71,28,49,69,34,29,58,67,83,],[-48,-50,-50,-50,-21,-18,-50,-50,-19,-50,-50,-47,-49,59,59,-50,-26,-20,-29,-27,-25,-50,-50,-24,-50,-50,-28,-31,-50,-30,-35,]),'GT_SYMBOL':([39,68,45,49,38,0,67,51,57,44,34,50,32,69,29,35,21,37,73,28,80,71,36,55,84,12,48,16,58,70,83,],[-25,-48,64,-50,-50,-50,-30,-19,-18,-27,-28,-49,-50,-50,-31,-29,-50,-50,-20,-24,64,-50,-50,-21,-50,-50,-47,-50,-50,-26,-35,]),'GE_SYMBOL':([68,38,21,57,37,35,80,45,0,28,83,69,49,73,29,32,12,48,70,58,55,71,16,84,44,36,51,39,67,34,50,],[-48,-50,-50,-18,-50,-29,65,65,-50,-24,-35,-50,-50,-20,-31,-50,-50,-47,-26,-50,-21,-50,-50,-50,-27,-50,-19,-25,-30,-28,-49,]),'READ_SYMBOL':([12,21,0,18,],[5,5,5,-5,]),'INT_TYPE_SYMBOL':([0,18,12,21,],[9,-5,9,9,]),'OR_SYMBOL':([21,57,49,34,67,71,12,48,16,36,0,73,35,50,44,39,70,68,29,37,51,58,69,55,28,32,84,83,38,],[-50,-18,-50,-28,-30,76,-50,-47,-50,-50,-50,-20,-29,-49,-27,-25,-26,-48,-31,-50,-19,-50,-50,-21,-24,-50,76,-35,-50,]),'INLINE_THEN_SYMBOL':([36,57,34,37,68,70,78,46,0,69,73,39,50,84,35,16,85,55,48,12,75,51,32,44,71,58,28,67,29,38,49,83,21,],[-50,-18,-28,-50,-48,-26,-37,66,-50,-50,-20,-25,-49,-50,-29,-50,-36,-21,-47,-50,-38,-19,-50,-27,-50,-50,-24,-30,-31,-50,-50,-35,-50,]),'AND_SYMBOL':([69,39,32,84,35,0,73,44,12,50,55,51,48,16,83,57,70,36,37,38,34,29,49,58,67,28,71,68,21,],[-50,-25,-50,77,-29,-50,-20,-27,-50,-49,-21,-19,-47,-50,-35,-18,-26,-50,-50,-50,-28,-31,-50,-50,-30,-24,77,-48,-50,]),} _lr_action = { } for _k, _v in _lr_action_items.items(): for _x,_y in zip(_v[0],_v[1]): _lr_action[(_x,_k)] = _y del _lr_action_items _lr_goto_items = {'inlineIfStatement':([54,74,62,82,30,31,79,42,66,22,27,],[29,29,29,29,29,29,29,29,29,29,29,]),'typeName':([12,0,21,],[3,3,3,]),'boolExpr':([30,],[46,]),'partialDeclaration':([37,16,],[56,25,]),'partialIntTerm':([28,58,],[39,70,]),'addOp':([69,38,36,],[54,54,54,]),'partialStatementSequence':([21,12,],[26,19,]),'boolOp':([71,84,],[74,74,]),'write':([0,12,21,],[7,7,7,]),'program':([0,],[10,]),'statement':([21,0,12,],[21,12,21,]),'empty':([12,32,69,37,38,58,16,28,84,71,21,49,0,36,],[20,50,55,24,55,44,24,44,78,78,20,50,6,55,]),'multOp':([58,28,],[42,42,]),'partialIntExpr':([36,38,69,],[51,57,73,]),'intFactor':([54,62,30,31,82,74,27,42,66,22,79,],[28,28,28,28,28,28,28,58,28,28,28,]),'read':([12,0,21,],[4,4,4,]),'assignment':([12,0,21,],[2,2,2,]),'declaration':([12,21,0,],[13,13,13,]),'integer':([54,74,31,42,66,30,22,82,62,79,27,],[34,34,34,34,34,34,34,34,34,34,34,]),'digits':([32,49,],[48,68,]),'intExpr':([31,30,62,79,74,22,82,66,],[47,45,71,81,80,33,84,72,]),'relationOp':([45,80,],[62,82,]),'partialBoolExpr':([84,71,],[85,75,]),'intTerm':([31,66,30,54,79,27,74,62,22,82,],[36,36,36,69,36,38,36,36,36,36,]),'statementSequence':([0,],[8,]),'partialStatement':([12,21,0,],[11,11,11,]),} _lr_goto = { } for _k, _v in _lr_goto_items.items(): for _x,_y in zip(_v[0],_v[1]): _lr_goto[(_x,_k)] = _y del _lr_goto_items _lr_productions = [ ("S'",1,None,None,None), ('program',1,'p_program','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',11), ('statementSequence',2,'p_statementSequence','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',14), ('partialStatementSequence',2,'p_partialStatementSequence','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',17), ('partialStatementSequence',1,'p_partialStatementSequence','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',18), ('statement',2,'p_statement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',21), ('partialStatement',1,'p_partialStatement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',24), ('partialStatement',1,'p_partialStatement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',25), ('partialStatement',1,'p_partialStatement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',26), ('partialStatement',1,'p_partialStatement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',27), ('partialStatement',1,'p_partialStatement','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',28), ('read',2,'p_read','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',31), ('write',2,'p_write','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',34), ('declaration',3,'p_declaration','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',37), ('partialDeclaration',3,'p_partialDeclaration','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',40), ('partialDeclaration',1,'p_partialDeclaration','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',41), ('typeName',1,'p_typeName','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',44), ('assignment',3,'p_assignment','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',47), ('intExpr',3,'p_intExpr','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',50), ('intExpr',2,'p_intExpr','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',51), ('partialIntExpr',3,'p_partialIntExpr','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',54), ('partialIntExpr',1,'p_partialIntExpr','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',55), ('addOp',1,'p_addOp','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',58), ('addOp',1,'p_addOp','/Users/christopheruldack/Documents/FH/Compilerbau/pyNase2/naseParser.py',59), 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import flask import requests import subprocess import time import threading from flask_cors import CORS import os app = flask.Flask(__name__) CORS(app) path_to_run = './' #directory here py_name = 'RF(Master).py' #fileName here args = ["python3", "{}{}".format(path_to_run, py_name)] lrm=None #iplist=["http://127.0.0.1:3000","http://127.0.0.1:6000"] s = 'http://worker' iplist = [s+str(i)+':4000' for i in range(0,3)] sesh=requests.Session() os.system("touch out") os.system("mkdir -p /dev/core/files") @app.route('/') def hello(): a= "<html><meta http-equiv=\"refresh\" content=\"5\" ><h1>Master</h1>" proc = subprocess.Popen(["tac", "out"], stdout=subprocess.PIPE) (out, err) = proc.communicate() a = a + ""+str(out.decode('ascii'))+"</html>" return a @app.route('/api/master/start', methods = ['GET']) def start(): global lrm global sesh global iplist if lrm is not None: #if process is running return flask.Response(status=409) #code:conflict else: #process never run lrm=subprocess.Popen(args) #start lr(master) api time.sleep(2) with open("out",'a') as standardout: print("Starting Tasks ",file=standardout) for ip in iplist: url = ip+'/api/worker/start' initw = threading.Thread(target=sesh.get, args=(url,)) initw.start() #start lr(worker) api time.sleep(2) url='http://localhost:5000/api/master/rf/start' initmodel = threading.Thread(target=sesh.get, args=(url,)) initmodel.start() #begin training return flask.Response(status=202) #code:accepted @app.route('/api/master/stop', methods = ['GET']) def stop(): global lrm global sesh global iplist if lrm is not None: #process not completed for ip in iplist: url = ip+'/api/worker/stop' stopw = threading.Thread(target=sesh.get, args=(url,)) stopw.start() lrm.terminate() lrm=None with open("out",'a') as standardout: print("Stopping the entire operation\n",file=standardout) return flask.Response(status=200) #code:ok else: #process never run return flask.Response(status=403) #code:forbidden if __name__ == '__main__': app.run(host='0.0.0.0', port=4000)

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""" path.py - An object representing a path to a file or directory. Authors: Jason Orendorff <jason.orendorff\x40gmail\x2ecom> Mikhail Gusarov <dottedmag@dottedmag.net> Others - unfortunately attribution is lost Example: from path import path d = path('/home/guido/bin') for f in d.files('*.py'): f.chmod(0755) This module requires Python 2.2 or later. """ # TODO # - Tree-walking functions don't avoid symlink loops. Matt Harrison # sent me a patch for this. # - Bug in write_text(). It doesn't support Universal newline mode. # - Better error message in listdir() when self isn't a # directory. (On Windows, the error message really sucks.) # - Make sure everything has a good docstring. # - Add methods for regex find and replace. # - guess_content_type() method? # - Perhaps support arguments to touch(). from __future__ import generators import sys, warnings, os, fnmatch, glob, shutil, codecs, hashlib, errno __version__ = '2.2.2' __all__ = ['path'] # Platform-specific support for path.owner if os.name == 'nt': try: import win32security except ImportError: win32security = None else: try: import pwd except ImportError: pwd = None # Pre-2.3 support. Are unicode filenames supported? _base = str _getcwd = os.getcwd try: if os.path.supports_unicode_filenames: _base = unicode _getcwd = os.getcwdu except AttributeError: pass # Pre-2.3 workaround for booleans try: True, False except NameError: True, False = 1, 0 # Pre-2.3 workaround for basestring. try: basestring except NameError: basestring = (str, unicode) # Universal newline support _textmode = 'r' if hasattr(file, 'newlines'): _textmode = 'U' class TreeWalkWarning(Warning): pass class path(_base): """ Represents a filesystem path. For documentation on individual methods, consult their counterparts in os.path. """ # --- Special Python methods. def __repr__(self): return 'path(%s)' % _base.__repr__(self) # Adding a path and a string yields a path. def __add__(self, more): try: resultStr = _base.__add__(self, more) except TypeError: #Python bug resultStr = NotImplemented if resultStr is NotImplemented: return resultStr return self.__class__(resultStr) def __radd__(self, other): if isinstance(other, basestring): return self.__class__(other.__add__(self)) else: return NotImplemented # The / operator joins paths. def __div__(self, rel): """ fp.__div__(rel) == fp / rel == fp.joinpath(rel) Join two path components, adding a separator character if needed. """ return self.__class__(os.path.join(self, rel)) # Make the / operator work even when true division is enabled. __truediv__ = __div__ def getcwd(cls): """ Return the current working directory as a path object. """ return cls(_getcwd()) getcwd = classmethod(getcwd) # --- Operations on path strings. isabs = os.path.isabs def abspath(self): return self.__class__(os.path.abspath(self)) def normcase(self): return self.__class__(os.path.normcase(self)) def normpath(self): return self.__class__(os.path.normpath(self)) def realpath(self): return self.__class__(os.path.realpath(self)) def expanduser(self): return self.__class__(os.path.expanduser(self)) def expandvars(self): return self.__class__(os.path.expandvars(self)) def dirname(self): return self.__class__(os.path.dirname(self)) basename = os.path.basename def expand(self): """ Clean up a filename by calling expandvars(), expanduser(), and normpath() on it. This is commonly everything needed to clean up a filename read from a configuration file, for example. """ return self.expandvars().expanduser().normpath() def _get_namebase(self): base, ext = os.path.splitext(self.name) return base def _get_ext(self): f, ext = os.path.splitext(_base(self)) return ext def _get_drive(self): drive, r = os.path.splitdrive(self) return self.__class__(drive) parent = property( dirname, None, None, """ This path's parent directory, as a new path object. For example, path('/usr/local/lib/libpython.so').parent == path('/usr/local/lib') """) name = property( basename, None, None, """ The name of this file or directory without the full path. For example, path('/usr/local/lib/libpython.so').name == 'libpython.so' """) namebase = property( _get_namebase, None, None, """ The same as path.name, but with one file extension stripped off. For example, path('/home/guido/python.tar.gz').name == 'python.tar.gz', but path('/home/guido/python.tar.gz').namebase == 'python.tar' """) ext = property( _get_ext, None, None, """ The file extension, for example '.py'. """) drive = property( _get_drive, None, None, """ The drive specifier, for example 'C:'. This is always empty on systems that don't use drive specifiers. """) def splitpath(self): """ p.splitpath() -> Return (p.parent, p.name). """ parent, child = os.path.split(self) return self.__class__(parent), child def splitdrive(self): """ p.splitdrive() -> Return (p.drive, <the rest of p>). Split the drive specifier from this path. If there is no drive specifier, p.drive is empty, so the return value is simply (path(''), p). This is always the case on Unix. """ drive, rel = os.path.splitdrive(self) return self.__class__(drive), rel def splitext(self): """ p.splitext() -> Return (p.stripext(), p.ext). Split the filename extension from this path and return the two parts. Either part may be empty. The extension is everything from '.' to the end of the last path segment. This has the property that if (a, b) == p.splitext(), then a + b == p. """ filename, ext = os.path.splitext(self) return self.__class__(filename), ext def stripext(self): """ p.stripext() -> Remove one file extension from the path. For example, path('/home/guido/python.tar.gz').stripext() returns path('/home/guido/python.tar'). """ return self.splitext()[0] if hasattr(os.path, 'splitunc'): def splitunc(self): unc, rest = os.path.splitunc(self) return self.__class__(unc), rest def _get_uncshare(self): unc, r = os.path.splitunc(self) return self.__class__(unc) uncshare = property( _get_uncshare, None, None, """ The UNC mount point for this path. This is empty for paths on local drives. """) def joinpath(self, *args): """ Join two or more path components, adding a separator character (os.sep) if needed. Returns a new path object. """ return self.__class__(os.path.join(self, *args)) def splitall(self): r""" Return a list of the path components in this path. The first item in the list will be a path. Its value will be either os.curdir, os.pardir, empty, or the root directory of this path (for example, '/' or 'C:\\'). The other items in the list will be strings. path.path.joinpath(*result) will yield the original path. """ parts = [] loc = self while loc != os.curdir and loc != os.pardir: prev = loc loc, child = prev.splitpath() if loc == prev: break parts.append(child) parts.append(loc) parts.reverse() return parts def relpath(self): """ Return this path as a relative path, based from the current working directory. """ cwd = self.__class__(os.getcwd()) return cwd.relpathto(self) def relpathto(self, dest): """ Return a relative path from self to dest. If there is no relative path from self to dest, for example if they reside on different drives in Windows, then this returns dest.abspath(). """ origin = self.abspath() dest = self.__class__(dest).abspath() orig_list = origin.normcase().splitall() # Don't normcase dest! We want to preserve the case. dest_list = dest.splitall() if orig_list[0] != os.path.normcase(dest_list[0]): # Can't get here from there. return dest # Find the location where the two paths start to differ. i = 0 for start_seg, dest_seg in zip(orig_list, dest_list): if start_seg != os.path.normcase(dest_seg): break i += 1 # Now i is the point where the two paths diverge. # Need a certain number of "os.pardir"s to work up # from the origin to the point of divergence. segments = [os.pardir] * (len(orig_list) - i) # Need to add the diverging part of dest_list. segments += dest_list[i:] if len(segments) == 0: # If they happen to be identical, use os.curdir. relpath = os.curdir else: relpath = os.path.join(*segments) return self.__class__(relpath) # --- Listing, searching, walking, and matching def listdir(self, pattern=None): """ D.listdir() -> List of items in this directory. Use D.files() or D.dirs() instead if you want a listing of just files or just subdirectories. The elements of the list are path objects. With the optional 'pattern' argument, this only lists items whose names match the given pattern. """ names = os.listdir(self) if pattern is not None: names = fnmatch.filter(names, pattern) return [self / child for child in names] def dirs(self, pattern=None): """ D.dirs() -> List of this directory's subdirectories. The elements of the list are path objects. This does not walk recursively into subdirectories (but see path.walkdirs). With the optional 'pattern' argument, this only lists directories whose names match the given pattern. For example, d.dirs('build-*'). """ return [p for p in self.listdir(pattern) if p.isdir()] def files(self, pattern=None): """ D.files() -> List of the files in this directory. The elements of the list are path objects. This does not walk into subdirectories (see path.walkfiles). With the optional 'pattern' argument, this only lists files whose names match the given pattern. For example, d.files('*.pyc'). """ return [p for p in self.listdir(pattern) if p.isfile()] def walk(self, pattern=None, errors='strict'): """ D.walk() -> iterator over files and subdirs, recursively. The iterator yields path objects naming each child item of this directory and its descendants. This requires that D.isdir(). This performs a depth-first traversal of the directory tree. Each directory is returned just before all its children. The errors= keyword argument controls behavior when an error occurs. The default is 'strict', which causes an exception. The other allowed values are 'warn', which reports the error via warnings.warn(), and 'ignore'. """ if errors not in ('strict', 'warn', 'ignore'): raise ValueError("invalid errors parameter") try: childList = self.listdir() except Exception: if errors == 'ignore': return elif errors == 'warn': warnings.warn( "Unable to list directory '%s': %s" % (self, sys.exc_info()[1]), TreeWalkWarning) return else: raise for child in childList: if pattern is None or child.fnmatch(pattern): yield child try: isdir = child.isdir() except Exception: if errors == 'ignore': isdir = False elif errors == 'warn': warnings.warn( "Unable to access '%s': %s" % (child, sys.exc_info()[1]), TreeWalkWarning) isdir = False else: raise if isdir: for item in child.walk(pattern, errors): yield item def walkdirs(self, pattern=None, errors='strict'): """ D.walkdirs() -> iterator over subdirs, recursively. With the optional 'pattern' argument, this yields only directories whose names match the given pattern. For example, mydir.walkdirs('*test') yields only directories with names ending in 'test'. The errors= keyword argument controls behavior when an error occurs. The default is 'strict', which causes an exception. The other allowed values are 'warn', which reports the error via warnings.warn(), and 'ignore'. """ if errors not in ('strict', 'warn', 'ignore'): raise ValueError("invalid errors parameter") try: dirs = self.dirs() except Exception: if errors == 'ignore': return elif errors == 'warn': warnings.warn( "Unable to list directory '%s': %s" % (self, sys.exc_info()[1]), TreeWalkWarning) return else: raise for child in dirs: if pattern is None or child.fnmatch(pattern): yield child for subsubdir in child.walkdirs(pattern, errors): yield subsubdir def walkfiles(self, pattern=None, errors='strict'): """ D.walkfiles() -> iterator over files in D, recursively. The optional argument, pattern, limits the results to files with names that match the pattern. For example, mydir.walkfiles('*.tmp') yields only files with the .tmp extension. """ if errors not in ('strict', 'warn', 'ignore'): raise ValueError("invalid errors parameter") try: childList = self.listdir() except Exception: if errors == 'ignore': return elif errors == 'warn': warnings.warn( "Unable to list directory '%s': %s" % (self, sys.exc_info()[1]), TreeWalkWarning) return else: raise for child in childList: try: isfile = child.isfile() isdir = not isfile and child.isdir() except: if errors == 'ignore': continue elif errors == 'warn': warnings.warn( "Unable to access '%s': %s" % (self, sys.exc_info()[1]), TreeWalkWarning) continue else: raise if isfile: if pattern is None or child.fnmatch(pattern): yield child elif isdir: for f in child.walkfiles(pattern, errors): yield f def fnmatch(self, pattern): """ Return True if self.name matches the given pattern. pattern - A filename pattern with wildcards, for example '*.py'. """ return fnmatch.fnmatch(self.name, pattern) def glob(self, pattern): """ Return a list of path objects that match the pattern. pattern - a path relative to this directory, with wildcards. For example, path('/users').glob('*/bin/*') returns a list of all the files users have in their bin directories. """ cls = self.__class__ return [cls(s) for s in glob.glob(_base(self / pattern))] # --- Reading or writing an entire file at once. def open(self, mode='r'): """ Open this file. Return a file object. """ return file(self, mode) def bytes(self): """ Open this file, read all bytes, return them as a string. """ f = self.open('rb') try: return f.read() finally: f.close() def write_bytes(self, bytes, append=False): """ Open this file and write the given bytes to it. Default behavior is to overwrite any existing file. Call p.write_bytes(bytes, append=True) to append instead. """ if append: mode = 'ab' else: mode = 'wb' f = self.open(mode) try: f.write(bytes) finally: f.close() def text(self, encoding=None, errors='strict'): r""" Open this file, read it in, return the content as a string. This uses 'U' mode in Python 2.3 and later, so '\r\n' and '\r' are automatically translated to '\n'. Optional arguments: encoding - The Unicode encoding (or character set) of the file. If present, the content of the file is decoded and returned as a unicode object; otherwise it is returned as an 8-bit str. errors - How to handle Unicode errors; see help(str.decode) for the options. Default is 'strict'. """ if encoding is None: # 8-bit f = self.open(_textmode) try: return f.read() finally: f.close() else: # Unicode f = codecs.open(self, 'r', encoding, errors) # (Note - Can't use 'U' mode here, since codecs.open # doesn't support 'U' mode, even in Python 2.3.) try: t = f.read() finally: f.close() return (t.replace(u'\r\n', u'\n') .replace(u'\r\x85', u'\n') .replace(u'\r', u'\n') .replace(u'\x85', u'\n') .replace(u'\u2028', u'\n')) def write_text(self, text, encoding=None, errors='strict', linesep=os.linesep, append=False): r""" Write the given text to this file. The default behavior is to overwrite any existing file; to append instead, use the 'append=True' keyword argument. There are two differences between path.write_text() and path.write_bytes(): newline handling and Unicode handling. See below. Parameters: - text - str/unicode - The text to be written. - encoding - str - The Unicode encoding that will be used. This is ignored if 'text' isn't a Unicode string. - errors - str - How to handle Unicode encoding errors. Default is 'strict'. See help(unicode.encode) for the options. This is ignored if 'text' isn't a Unicode string. - linesep - keyword argument - str/unicode - The sequence of characters to be used to mark end-of-line. The default is os.linesep. You can also specify None; this means to leave all newlines as they are in 'text'. - append - keyword argument - bool - Specifies what to do if the file already exists (True: append to the end of it; False: overwrite it.) The default is False. --- Newline handling. write_text() converts all standard end-of-line sequences ('\n', '\r', and '\r\n') to your platform's default end-of-line sequence (see os.linesep; on Windows, for example, the end-of-line marker is '\r\n'). If you don't like your platform's default, you can override it using the 'linesep=' keyword argument. If you specifically want write_text() to preserve the newlines as-is, use 'linesep=None'. This applies to Unicode text the same as to 8-bit text, except there are three additional standard Unicode end-of-line sequences: u'\x85', u'\r\x85', and u'\u2028'. (This is slightly different from when you open a file for writing with fopen(filename, "w") in C or file(filename, 'w') in Python.) --- Unicode If 'text' isn't Unicode, then apart from newline handling, the bytes are written verbatim to the file. The 'encoding' and 'errors' arguments are not used and must be omitted. If 'text' is Unicode, it is first converted to bytes using the specified 'encoding' (or the default encoding if 'encoding' isn't specified). The 'errors' argument applies only to this conversion. """ if isinstance(text, unicode): if linesep is not None: # Convert all standard end-of-line sequences to # ordinary newline characters. text = (text.replace(u'\r\n', u'\n') .replace(u'\r\x85', u'\n') .replace(u'\r', u'\n') .replace(u'\x85', u'\n') .replace(u'\u2028', u'\n')) text = text.replace(u'\n', linesep) if encoding is None: encoding = sys.getdefaultencoding() bytes = text.encode(encoding, errors) else: # It is an error to specify an encoding if 'text' is # an 8-bit string. assert encoding is None if linesep is not None: text = (text.replace('\r\n', '\n') .replace('\r', '\n')) bytes = text.replace('\n', linesep) self.write_bytes(bytes, append) def lines(self, encoding=None, errors='strict', retain=True): r""" Open this file, read all lines, return them in a list. Optional arguments: encoding - The Unicode encoding (or character set) of the file. The default is None, meaning the content of the file is read as 8-bit characters and returned as a list of (non-Unicode) str objects. errors - How to handle Unicode errors; see help(str.decode) for the options. Default is 'strict' retain - If true, retain newline characters; but all newline character combinations ('\r', '\n', '\r\n') are translated to '\n'. If false, newline characters are stripped off. Default is True. This uses 'U' mode in Python 2.3 and later. """ if encoding is None and retain: f = self.open(_textmode) try: return f.readlines() finally: f.close() else: return self.text(encoding, errors).splitlines(retain) def write_lines(self, lines, encoding=None, errors='strict', linesep=os.linesep, append=False): r""" Write the given lines of text to this file. By default this overwrites any existing file at this path. This puts a platform-specific newline sequence on every line. See 'linesep' below. lines - A list of strings. encoding - A Unicode encoding to use. This applies only if 'lines' contains any Unicode strings. errors - How to handle errors in Unicode encoding. This also applies only to Unicode strings. linesep - The desired line-ending. This line-ending is applied to every line. If a line already has any standard line ending ('\r', '\n', '\r\n', u'\x85', u'\r\x85', u'\u2028'), that will be stripped off and this will be used instead. The default is os.linesep, which is platform-dependent ('\r\n' on Windows, '\n' on Unix, etc.) Specify None to write the lines as-is, like file.writelines(). Use the keyword argument append=True to append lines to the file. The default is to overwrite the file. Warning: When you use this with Unicode data, if the encoding of the existing data in the file is different from the encoding you specify with the encoding= parameter, the result is mixed-encoding data, which can really confuse someone trying to read the file later. """ if append: mode = 'ab' else: mode = 'wb' f = self.open(mode) try: for line in lines: isUnicode = isinstance(line, unicode) if linesep is not None: # Strip off any existing line-end and add the # specified linesep string. if isUnicode: if line[-2:] in (u'\r\n', u'\x0d\x85'): line = line[:-2] elif line[-1:] in (u'\r', u'\n', u'\x85', u'\u2028'): line = line[:-1] else: if line[-2:] == '\r\n': line = line[:-2] elif line[-1:] in ('\r', '\n'): line = line[:-1] line += linesep if isUnicode: if encoding is None: encoding = sys.getdefaultencoding() line = line.encode(encoding, errors) f.write(line) finally: f.close() def read_md5(self): """ Calculate the md5 hash for this file. This reads through the entire file. """ return self.read_hash('md5') def _hash(self, hash_name): f = self.open('rb') try: m = hashlib.new(hash_name) while True: d = f.read(8192) if not d: break m.update(d) return m finally: f.close() def read_hash(self, hash_name): """ Calculate given hash for this file. List of supported hashes can be obtained from hashlib package. This reads the entire file. """ return self._hash(hash_name).digest() def read_hexhash(self, hash_name): """ Calculate given hash for this file, returning hexdigest. List of supported hashes can be obtained from hashlib package. This reads the entire file. """ return self._hash(hash_name).hexdigest() # --- Methods for querying the filesystem. exists = os.path.exists isdir = os.path.isdir isfile = os.path.isfile islink = os.path.islink ismount = os.path.ismount if hasattr(os.path, 'samefile'): samefile = os.path.samefile getatime = os.path.getatime atime = property( getatime, None, None, """ Last access time of the file. """) getmtime = os.path.getmtime mtime = property( getmtime, None, None, """ Last-modified time of the file. """) if hasattr(os.path, 'getctime'): getctime = os.path.getctime ctime = property( getctime, None, None, """ Creation time of the file. """) getsize = os.path.getsize size = property( getsize, None, None, """ Size of the file, in bytes. """) if hasattr(os, 'access'): def access(self, mode): """ Return true if current user has access to this path. mode - One of the constants os.F_OK, os.R_OK, os.W_OK, os.X_OK """ return os.access(self, mode) def stat(self): """ Perform a stat() system call on this path. """ return os.stat(self) def lstat(self): """ Like path.stat(), but do not follow symbolic links. """ return os.lstat(self) def get_owner(self): r""" Return the name of the owner of this file or directory. This follows symbolic links. On Windows, this returns a name of the form ur'DOMAIN\User Name'. On Windows, a group can own a file or directory. """ if os.name == 'nt': if win32security is None: raise Exception("path.owner requires win32all to be installed") desc = win32security.GetFileSecurity( self, win32security.OWNER_SECURITY_INFORMATION) sid = desc.GetSecurityDescriptorOwner() account, domain, typecode = win32security.LookupAccountSid(None, sid) return domain + u'\\' + account else: if pwd is None: raise NotImplementedError("path.owner is not implemented on this platform.") st = self.stat() return pwd.getpwuid(st.st_uid).pw_name owner = property( get_owner, None, None, """ Name of the owner of this file or directory. """) if hasattr(os, 'statvfs'): def statvfs(self): """ Perform a statvfs() system call on this path. """ return os.statvfs(self) if hasattr(os, 'pathconf'): def pathconf(self, name): return os.pathconf(self, name) # --- Modifying operations on files and directories def utime(self, times): """ Set the access and modified times of this file. """ os.utime(self, times) def chmod(self, mode): os.chmod(self, mode) if hasattr(os, 'chown'): def chown(self, uid, gid): os.chown(self, uid, gid) def rename(self, new): os.rename(self, new) def renames(self, new): os.renames(self, new) # --- Create/delete operations on directories def mkdir(self, mode=0777): os.mkdir(self, mode) def mkdir_p(self, mode=0777): try: self.mkdir(mode) except OSError, e: if e.errno != errno.EEXIST: raise def makedirs(self, mode=0777): os.makedirs(self, mode) def makedirs_p(self, mode=0777): try: self.makedirs(mode) except OSError, e: if e.errno != errno.EEXIST: raise def rmdir(self): os.rmdir(self) def removedirs(self): os.removedirs(self) # --- Modifying operations on files def touch(self): """ Set the access/modified times of this file to the current time. Create the file if it does not exist. """ fd = os.open(self, os.O_WRONLY | os.O_CREAT, 0666) os.close(fd) os.utime(self, None) def remove(self): os.remove(self) def remove_p(self): try: self.unlink() except OSError, e: if e.errno != errno.ENOENT: raise def unlink(self): os.unlink(self) def unlink_p(self): self.remove_p() # --- Links if hasattr(os, 'link'): def link(self, newpath): """ Create a hard link at 'newpath', pointing to this file. """ os.link(self, newpath) if hasattr(os, 'symlink'): def symlink(self, newlink): """ Create a symbolic link at 'newlink', pointing here. """ os.symlink(self, newlink) if hasattr(os, 'readlink'): def readlink(self): """ Return the path to which this symbolic link points. The result may be an absolute or a relative path. """ return self.__class__(os.readlink(self)) def readlinkabs(self): """ Return the path to which this symbolic link points. The result is always an absolute path. """ p = self.readlink() if p.isabs(): return p else: return (self.parent / p).abspath() # --- High-level functions from shutil copyfile = shutil.copyfile copymode = shutil.copymode copystat = shutil.copystat copy = shutil.copy copy2 = shutil.copy2 copytree = shutil.copytree if hasattr(shutil, 'move'): move = shutil.move rmtree = shutil.rmtree # --- Special stuff from os if hasattr(os, 'chroot'): def chroot(self): os.chroot(self) if hasattr(os, 'startfile'): def startfile(self): os.startfile(self)

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Count.Zr0@gmail.com

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/PhotovaristorSensor.py

b4751d15783bc5dcc6b517eacac39a95f7d109f5

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wenyan0622/RasberryPiTest

a57eca4249e39373a63fd2205745b8ee205c16ad

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refs/heads/master

2020-03-27T03:30:20.302813

2018-08-24T16:41:52

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842

py

import RPi.GPIO as GPIO import time #程序目的：检测光敏传感器的输入信号，检测到光则小灯亮，否则灯灭 #分别指定传感器接口和LED灯接口 LightSensor_PIN=7 LED_PIN=12 #初始化 def init(): GPIO.setmode(GPIO.BOARD) GPIO.setup(LightSensor_PIN,GPIO.IN) GPIO.setup(LED_PIN,GPIO.OUT) pass #循环检测100次 def detect(): count=1 for count in range(1,100): if GPIO.input(LightSensor_PIN) == True: GPIO.output(LED_PIN,True) #检测到灯光则灯亮 print("light on ...",count) else: GPIO.output(LED_PIN,False) #没有检测到光则灯灭 print("light off ...",count) count=count+1 time.sleep(1)#1秒循环 try: init() detect() except KeyboardInterrupt: pass #清理工作 GPIO.cleanup()

[ "wuyiliang@e-lead.cn" ]

wuyiliang@e-lead.cn

6073cfcd12cdbdbc9d81c5279f878f6ed3e2491a

f1dad4036a93b0ae3fb48841c3f1ae1440210085

/emarket/views.py

39baf90e3f5d1892dbfa7337958aae37f41a76bf

[ "MIT" ]

permissive

MerlinEmris/ebazar

2af740fe7d15ca111b5cf7a923464a26cc937206

664d093b5addc2612083a7f8e5d5d9441c536c8b

refs/heads/master

2023-02-09T22:13:04.862064

2023-01-21T06:07:34

322,202,655

7

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# from traceback import TracebackException from django.contrib.auth.forms import UserCreationForm # from django.contrib.auth.models import User from django.contrib.auth import login, authenticate from django.contrib.auth.decorators import login_required from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger from django.contrib.postgres.search import SearchVector from django.core import serializers from django.http import JsonResponse from django.views import View # import os # from django.contrib.sites.shortcuts import get_current_site # from django.utils.encoding import force_bytes # from django.utils.encoding import force_text # from django.utils.http import urlsafe_base64_encode # from django.utils.http import urlsafe_base64_decode # from django.template.loader import render_to_string from django.http import HttpResponse import django_filters.rest_framework from django.shortcuts import render, redirect from .forms import ProfilePhotoForm, PhotoForm, SignUpForm, ProfileForm, ItemForm, SearchForm from .models import User, Profile, Item, Category, Item_Image, Favorite_item from ebazar import settings from .serializers import ( CategorySerializer, ItemSerializer, UserSerializer, Item_ImageSerializer,) from rest_framework.decorators import api_view from rest_framework.response import Response from rest_framework.permissions import IsAuthenticated from rest_framework import viewsets, status # import django_filters.rest_framework from rest_framework.generics import ( DestroyAPIView, ListAPIView, UpdateAPIView, RetrieveAPIView, CreateAPIView ) from rest_framework.views import APIView import shutil import os import datetime import json # print console logs log_prefix = '['+datetime.datetime.now().strftime("%d-%m-%y %H:%M:%S")+']' log_end = '********' log_date = datetime.datetime.now().strftime("%d-%m-%y_%H:%M") # redirect to create user (url(r'^$')) def index(request): if request.user: return redirect('home') else: return redirect('home') # create user with min information def create_user(request): if request.method == 'POST': form = SignUpForm(request.POST) # form = UserCreationForm(request.POST) if form.is_valid(): user = form.save() print(log_prefix+'user '+form.cleaned_data['username']+'is created'+log_end) # user.is_active = False # user.refresh_from_db() # user.profile.birth_date = form.cleaned_data.get('birth_date') # user.profile.bio = form.cleaned_data.get('bio') # user.profile.location = form.cleaned_data.get('location') # current_site = get_current_site(request) # subject = 'Activate Your MySite Account' # message = render_to_string('account_activation_email.html', { # 'user': user, # 'domain': current_site.domain, # 'uid': urlsafe_base64_encode(force_bytes(user.pk)), # 'token': account_activation_token.make_token(user), # }) # user.email_user(subject, message) # return redirect('account_activation_sent') username = form.cleaned_data.get('username') raw_password = form.cleaned_data.get('password1') user = authenticate(username=username, password=raw_password) login(request, user) print(log_prefix + 'user ' + username + 'is logged in' + log_end) return redirect('home') else: form = SignUpForm(request.POST) return render(request, 'registration/create_user.html', {'form': form}) else: form = SignUpForm() return render(request, 'registration/create_user.html', {'form': form}) @login_required def edit_profile(request): exist = 0 try: profile = request.user.profile exist = 1 except Profile.DoesNotExist: profile = Profile(user=request.user) if request.method == 'POST': form = ProfileForm(request.POST, request.FILES, instance=profile) if form.is_valid(): form.save() print(log_prefix + ' user ' + request.user.username + ' profile is changed ' + log_end) return redirect('home') else: return render(request, 'emarket/profile.html', {'form': form}) else: form = ProfileForm(instance=profile) return render(request, 'emarket/profile.html', {'form': form,'exist':exist}) def profile_change_photo(request, prof_id): if request.method == 'POST': profile = Profile.objects.filter(user_id=prof_id)[0] form = ProfilePhotoForm(request.POST, request.FILES, instance=profile) profile.img.delete(False) if form.is_valid(): form.save() return redirect('profile') else: form = ProfilePhotoForm() return render(request, 'emarket/profile_add_image.html', {'form':form,}) print(log_prefix + 'user ' + prof_id + 'profile img is changed' + log_end) def user(request, user_id): items = Item.objects.filter(user_id=user_id) pics = Item_Image.objects.all() if items: paginator = Paginator(items, 9) page = request.GET.get('page') try: items = paginator.page(page) except PageNotAnInteger: items = paginator.page(1) except EmptyPage: items = paginator.page(paginator.num_pages) return render(request, 'emarket/user.html', {'items': items, 'pics': pics, }) @login_required def create_item(request): if request.method == 'POST': item = Item(user=request.user) form = ItemForm(request.POST, instance=item) if form.is_valid(): form.save() print(log_prefix+'item:'+form.cleaned_data['name']+' is created at '+log_date+log_end) return redirect('add_item_img', item.id) else: return render(request, 'emarket/item_create.html', {'form': form}) else: form = ItemForm() return render(request, 'emarket/item_create.html', {'form': form}) @login_required def edit_item(request, it_id): try: item = Item.objects.filter(id=it_id)[0] except Item.DoesNotExist: redirect('home') if request.method == 'POST': form = ItemForm(request.POST, instance=item) if form.is_valid(): form.save() print(log_prefix + ' item ' + it_id + ' is changed ' + log_end) return redirect('show_item',it_id) else: form = ItemForm(instance=item) return render(request, 'emarket/item_edit.html',{'form':form}) else: form = ItemForm(instance=item) return render(request, 'emarket/item_edit.html',{'form':form}) def show_item(request, item_id): user = request.user exist = 1 # if user and request.method == "GET": # favs = Favorite_item.objects.filter(user=user) # # for fav in favs: # if fav.item_id == int(item_id): # print(fav.item_id) # exist = 1 # else: # exist = 0 item = Item.objects.filter(id=item_id)[0] item_images = Item_Image.objects.filter() return render(request, 'emarket/item_detail.html', {'item': item, 'pics': item_images, 'exist': exist}) @login_required def favorite_items(request, user_id): user = User.objects.filter(id=user_id) fav_items = Favorite_item.objects.filter(user = user) item_images = Item_Image.objects.filter() return render(request, 'emarket/favorite_items.html', {'fav_items': fav_items, 'pics': item_images}) # @login_required # def add_to_fav(request): # return redirect('home') def show_category(request, cat_id): cat = Category.objects.get(id=cat_id) items = Item.objects.filter(category=cat) pics = Item_Image.objects.all() if items: paginator = Paginator(items, 9) page = request.GET.get('page') try: items = paginator.page(page) except PageNotAnInteger: items = paginator.page(1) except EmptyPage: items = paginator.page(paginator.num_pages) return render(request, 'emarket/show_category.html', {'cat':cat, 'items':items, 'pics':pics}) def home(request): cats = Category.objects.all() # item_pic = {} items = Item.objects.order_by('-price')[0:9] item_images = Item_Image.objects.filter() # print(item_images) # print(items) # print(categories) return render(request, 'emarket/home.html', {'cats': cats, 'items': items, 'pics': item_images, }) def search(request, search_word=None): message = 'Ähli goşlar:' pics = Item_Image.objects.all() items = Item.objects.all() form = SearchForm if request.method == 'POST': form = SearchForm(request.POST) search_word = request.POST.get('search') location = request.POST.get('location') user = request.POST.get('user') if location and user: items = Item.objects.filter(name__icontains=search_word).filter(user=user).filter(location=location) elif user: items = Item.objects.filter(name__icontains=search_word).filter(user=user) elif location: items = Item.objects.filter(name__icontains=search_word).filter(location=location) else: items = Item.objects.filter(name__icontains=search_word) if items: message = 'Netijeler:' else: message = 'Hiç zat ýok' items = None if items: paginator = Paginator(items, 18) page = request.GET.get('page') try: items = paginator.page(page) except PageNotAnInteger: items = paginator.page(1) except EmptyPage: items = paginator.page(paginator.num_pages) return render(request, 'emarket/expo.html', {'items': items, 'pics': pics, 'ms': message, 's_word': search_word, 'form':form}) @login_required def add_item_img(request, it_id): photos = Item_Image.objects.filter() if request.method == 'POST': item_img = Item_Image(item_id=it_id) form = PhotoForm(request.POST, request.FILES, instance=item_img) if form.is_valid(): form.save() print(log_prefix+'item_'+it_id+' added image'+str(form.cleaned_data['img'])+log_end) return redirect('show_item', it_id) else: return render(request, 'emarket/item_add_image.html', {'form': form, 'photos': photos}) else: form = PhotoForm() return render(request, 'emarket/item_add_image.html', {'form':form, 'photos': photos}) @login_required def delete_item(request, it_id): item = Item.objects.filter(id=it_id) if item: item.delete() items_path = os.path.join(settings.MEDIA_ROOT, 'items') item_id = 'item_'+str(it_id) item_path = os.path.join(items_path, item_id) shutil.rmtree(item_path) print(log_prefix+item_id+' is deleted'+log_end) return redirect('home') else: return redirect('home') class UserCreate(APIView): def post(selfs, request, format='json'): serializer = UserSerializer(data=request.data) if serializer.is_valid(): user = serializer.save() if user: print(user) username = serializer.data.get('username') print(username) raw_password = serializer.data.get('password') print(raw_password) user_log = authenticate(username=username, password=raw_password) login(request, user_log) return Response(serializer.data, status=status.HTTP_201_CREATED) else: print('user create error') else: print('user validation failed') # api for item class ItemViewSet(ListAPIView): filter_backends = (django_filters.rest_framework.DjangoFilterBackend,) queryset = Item.objects.all() serializer_class = ItemSerializer search_fields = ('name',) ordering_fields = '__all__' class Item_ImageViewSet(ListAPIView): filter_backends = (django_filters.rest_framework.DjangoFilterBackend,) queryset = Item_Image.objects.all() serializer_class = Item_ImageSerializer class Item_ImageDetailViewSet(ListAPIView): queryset = Item_Image.objects.all() serializer_class = Item_ImageSerializer def get_queryset(self): item = self.kwargs['item'] return Item_Image.objects.filter(item=item) class ItemCreateViewSet(CreateAPIView): queryset = Item.objects.all() serializer_class = ItemSerializer class ItemDetailViewSet(RetrieveAPIView): queryset = Item.objects.all() serializer_class = ItemSerializer class ItemUpdateViewSet(UpdateAPIView): queryset = Item.objects.all() serializer_class = ItemSerializer class ItemDeleteViewSet(DestroyAPIView): queryset = Item.objects.all() serializer_class = ItemSerializer # api for category class CategoryViewSet(viewsets.ModelViewSet): queryset = Category.objects.all() serializer_class = CategorySerializer

[ "merdanchariyarov@gmail.com" ]

merdanchariyarov@gmail.com

639b97de6a8ed56bc002ebcf1f711245cbb5584e

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iafjayoza/Python

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#!/Users/jo049566/Desktop/Jay/Jay_Data/Study_Repo/Python/Projects/Dynamic_Programming/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install-3.7' __requires__ = 'setuptools==40.8.0' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install-3.7')() )

[ "jaykumar.oza@cerner.com" ]

jaykumar.oza@cerner.com

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/django/academy/api/view/views_course_fbv.py

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[]

no_license

aidoka22/Web-project

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refs/heads/main

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from rest_framework.decorators import api_view from rest_framework.request import Request from rest_framework.response import Response from api.models import Course from api.serializers import CourseSerializer2 @api_view(['GET', 'POST']) def course_list(request): if request.method == 'GET': courses = Course.objects.all() serializer = CourseSerializer2(courses, many=True) return Response(serializer.data) elif request.method == 'POST': serializer = CourseSerializer2(data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors) @api_view(['GET']) def teacher_courses(request,teacher_id): if request.method == 'GET': courses = Course.objects.get(author=teacher_id) serializer = CourseSerializer2(courses, many=True) return Response(serializer.data) @api_view(['GET', 'PUT', 'DELETE']) def course_detail(request, course_id): try: course = Course.objects.get(id=course_id) except Course.DoesNotExist as e: return Response({'message': str(e)}, status=400) if request.method == 'GET': serializer = CourseSerializer2(course) return Response(serializer.data) elif request.method == 'PUT': serializer = CourseSerializer2(instance=course, data=request.data) if serializer.is_valid(): serializer.save() return Response(serializer.data) return Response(serializer.errors) elif request.method == 'DELETE': course.delete() return Response({'message': 'deleted'}, status=204)

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noreply@github.com

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/125_algorithms/_exercises/templates/_algorithms_challenges/leetcode/leetCode/Backtracking/90_SubsetsII.py

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syurskyi/Python_Topics

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refs/heads/master

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2023-05-29T17:09:11

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#! /usr/bin/env python # -*- coding: utf-8 -*- c.. Solution o.. ___ subsetsWithDup nums """ :type nums: List[int] :rtype: List[List[int]] """ __ n.. nums: r_ [] nums.s.. ) nums_len = l..(nums) # Keep the subsets without duplicate subsets subsets = [[nums[0]]] # Keep the previous subsets which contains previous nums. pre_subset = [[nums[0]]] ___ i __ r..(1, nums_len # Combine current num with the previous subsets, # Then update the previous subsets __ nums[i] __ nums[i-1]: ___ j __ r..(l..(pre_subset)): one_set = pre_subset[j][:] one_set.a.. nums[i]) subsets.a.. one_set) pre_subset[j] = one_set # Combine current num with all the subsets before. # Then update the previous subsets ____ pre_subset # list ___ j __ r..(l..(subsets)): one_set = subsets[j][:] one_set.a.. nums[i]) subsets.a.. one_set) pre_subset.a.. one_set) pre_subset.a.. [nums[i]]) subsets.a.. [nums[i]]) subsets.a.. []) r_ subsets """ [] [1,2] [1,2,2] [1,2,2,3,3,4,5] """

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

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/redirect_service/redirector/interface.py

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iamandesai/ccet-ia-2

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import traceback from time import time from uuid import uuid4 from redirector import datastore, tools, fetcher def get_redirect_url(urlid): try: return datastore.get_redirect_url(urlid) except Exception as e: print(e) return None def push_data_to_store(data): try: data['dataid'] = str(uuid4()) super_ip = tools.supernetter(data['ipaddress']) snet_values = datastore.get_snet_val(super_ip) if snet_values: data['latitude'] = snet_values['latitude'] data['longitude'] = snet_values['longitude'] data['pincode'] = snet_values['zip'] data['city'] = snet_values['city'] else: details = fetcher.fetch_details_for_ip(super_ip) if details: datastore.put_snet_val(details) data['latitude'] = str(details['latitude']) data['longitude'] = str(details['longitude']) data['pincode'] = str(details['zip']) data['city'] = str(details['city']) data_new = tools.transform_tpoint(data) data_new["availableRamKey"] = tools.get_ram_gbs(data.get('availableRam')) datastore.increment_respective_counts(data['urlid'], data_new['brand'], data_new['os'], data_new['availableRamKey']) data_new["time"] = str(int(time())) datastore.put_submitted_data(data_new) except Exception as e: traceback.print_exc()

[ "noreply@github.com" ]

noreply@github.com

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Sukhvsin2/Shopping-backend

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#!c:\users\sukhi\desktop\web-development\vue-apps\ecomm-backend\env\scripts\python.exe # When the django-admin.py deprecation ends, remove this script. import warnings from django.core import management try: from django.utils.deprecation import RemovedInDjango40Warning except ImportError: raise ImportError( 'django-admin.py was deprecated in Django 3.1 and removed in Django ' '4.0. Please manually remove this script from your virtual environment ' 'and use django-admin instead.' ) if __name__ == "__main__": warnings.warn( 'django-admin.py is deprecated in favor of django-admin.', RemovedInDjango40Warning, ) management.execute_from_command_line()

[ "sukhvsingh2026@gmail.com" ]

sukhvsingh2026@gmail.com

5427109878d3dc6439276ff55eb29fd7bb028859

f43fe4fd349a45fdca005c7e210c19c180a64a36

/run_citation_need_model.py

af8b826eb60647025f03406727eebef869b38399

[]

no_license

ghassanmas/citation-needed-paper

0f50704782bf97a3385019845d2343672d2003e9

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refs/heads/master

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import re import argparse import pandas as pd import pickle import numpy as np import types from keras.models import load_model from keras.preprocessing.sequence import pad_sequences from sklearn.preprocessing import LabelBinarizer from sklearn.metrics import confusion_matrix from keras.utils import to_categorical from keras import backend as K K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=10, inter_op_parallelism_threads=10))) ''' Set up the arguments and parse them. ''' def get_arguments(): parser = argparse.ArgumentParser( description='Use this script to determinee whether a statement needs a citation or not.') parser.add_argument('-i', '--input', help='The input file from which we read the statements. Lines contains tab-separated values: the statement, the section header, and additionally the binary label corresponding to whether the sentence has a citation or not in the original text. This can be set to 0 if no evaluation is needed.', required=True) parser.add_argument('-o', '--out_dir', help='The output directory where we store the results', required=True) parser.add_argument('-m', '--model', help='The path to the model which we use for classifying the statements.', required=True) parser.add_argument('-v', '--vocab', help='The path to the vocabulary of words we use to represent the statements.', required=True) parser.add_argument('-s', '--sections', help='The path to the vocabulary of section with which we trained our model.', required=True) parser.add_argument('-l', '--lang', help='The language that we are parsing now.', required=False, default='en') return parser.parse_args() ''' Parse and construct the word representation for a sentence. ''' def text_to_word_list(text): # check first if the statements is longer than a single sentence. sentences = re.compile('\.\s+').split(str(text)) if len(sentences) != 1: # text = sentences[random.randint(0, len(sentences) - 1)] text = sentences[0] text = str(text).lower() # Clean the text text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text) text = re.sub(r"what's", "what is ", text) text = re.sub(r"\'s", " ", text) text = re.sub(r"\'ve", " have ", text) text = re.sub(r"can't", "cannot ", text) text = re.sub(r"n't", " not ", text) text = re.sub(r"i'm", "i am ", text) text = re.sub(r"\'re", " are ", text) text = re.sub(r"\'d", " would ", text) text = re.sub(r"\'ll", " will ", text) text = re.sub(r",", " ", text) text = re.sub(r"\.", " ", text) text = re.sub(r"!", " ! ", text) text = re.sub(r"\/", " ", text) text = re.sub(r"\^", " ^ ", text) text = re.sub(r"\+", " + ", text) text = re.sub(r"\-", " - ", text) text = re.sub(r"\=", " = ", text) text = re.sub(r"'", " ", text) text = re.sub(r"(\d+)(k)", r"\g<1>000", text) text = re.sub(r":", " : ", text) text = re.sub(r" e g ", " eg ", text) text = re.sub(r" b g ", " bg ", text) text = re.sub(r" u s ", " american ", text) text = re.sub(r"\0s", "0", text) text = re.sub(r" 9 11 ", "911", text) text = re.sub(r"e - mail", "email", text) text = re.sub(r"j k", "jk", text) text = re.sub(r"\s{2,}", " ", text) text = text.strip().split() return text ''' Compute P/R/F1 from the confusion matrix. ''' ''' Create the instances from our datasets ''' def construct_instance_reasons(statement_path, section_dict_path, vocab_w2v_path, max_len=-1): # Load the vocabulary vocab_w2v = pickle.load(open(vocab_w2v_path, 'rb')) # load the section dictionary. section_dict = pickle.load(open(section_dict_path, 'rb')) # Load the statements, the first column is the statement and the second is the label (True or False) statements = pd.read_csv(statement_path, sep='\t', index_col=None, error_bad_lines=False, warn_bad_lines=False) # construct the training data X = [] sections = [] y = [] outstring=[] for index, row in statements.iterrows(): try: statement_text = text_to_word_list(row['statement']) X_inst = [] for word in statement_text: if max_len != -1 and len(X_inst) >= max_len: continue if word not in vocab_w2v: X_inst.append(vocab_w2v['UNK']) else: X_inst.append(vocab_w2v[word]) # extract the section, and in case the section does not exist in the model, then assign UNK section = row['section'].strip().lower() sections.append(np.array([section_dict[section] if section in section_dict else 0])) label = row['citations'] # some of the rows are corrupt, thus, we need to check if the labels are actually boolean. if type(label) != types.BooleanType: continue y.append(label) X.append(X_inst) outstring.append(str(row["statement"])) #entity_id revision_id timestamp entity_title section_id section prg_idx sentence_idx statement citations except Exception as e: print row print e.message X = pad_sequences(X, maxlen=max_len, value=vocab_w2v['UNK'], padding='pre') encoder = LabelBinarizer() y = encoder.fit_transform(y) y = to_categorical(y) return X, np.array(sections), y, encoder, outstring if __name__ == '__main__': p = get_arguments() # load the model model = load_model(p.model) # load the data max_seq_length = model.input[0].shape[1].value X, sections, y, encoder,outstring = construct_instance_reasons(p.input, p.sections, p.vocab, max_seq_length) # classify the data pred = model.predict([X, sections]) # store the predictions: printing out the sentence text, the prediction score, and original citation label. outstr = 'Text\tPrediction\tCitation\n' for idx, y_pred in enumerate(pred): outstr += outstring[idx]+'\t'+str(y_pred[0])+ '\t' + str(y[idx]) + '\n' fout = open(p.out_dir + '/' + p.lang + '_predictions_sections.tsv', 'wt') fout.write(outstr) fout.flush() fout.close()

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############################################################################## #copyright 2012, Hamid MEDJAHED & Elyes ZEKRI (hmedjahed@prologue.fr) # # Prologue # #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. # ############################################################################## #!/usr/bin/env python # -*- coding: latin-1 -*- # Implementation of category CRUD functions import sys import pycompdev import pypacksrc srcdirectory=pypacksrc.srcpydir+"/pyaccords/pysrc/" sys.path.append(srcdirectory) from amazonEc2Class import * """ Note: amazonEc2 is a python class to interface the accords category :amazonEc2. -Attributes of this category are members of this class. -List of attributes: - name - flavor - image - original - profile - node - price - account - number - rootpass - reference - network - access - accessip - floating - floatingid - publicaddr - privateaddr - firewall - group - zone - hostname - workload - when - state """ def amazonEc2_create(amazonEc2): """Implement here your function""" return amazonEc2 def amazonEc2_retrieve(amazonEc2): """Implement here your function""" return amazonEc2 def amazonEc2_update(amazonEc2): """Implement here your function""" return amazonEc2 def amazonEc2_delete(amazonEc2): """Implement here your function""" return amazonEc2

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Import('env') localEnv = env.Clone() source = ['si70x.c'] si70x = localEnv.Object(source=source) Return('si70x')

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# Databricks notebook source # processing functions def categorical_indexing(df, column, indexer=None): # string index if (indexer == None): stringIndexer = StringIndexer(inputCol=column, outputCol='{}_INDEXED'.format(column)) indexer = stringIndexer.fit(df) indexed = indexer.transform(df) return indexed, indexer def categorical_hotencoding(df, column, categories=None): if(categories == None): categories = df.select(column).distinct().rdd.flatMap(lambda x : x).collect() categories.sort() for category in categories: function = udf(lambda item: 1 if item == category else 0, IntegerType()) new_column_name = column+'_'+category.replace('"', '').upper() df = df.withColumn(new_column_name, function(col(column))) df = df.drop(column) return df, categories def numerical_standardize(df, column, mean_value=None, sttdev_value=None): if(mean_value == None and sttdev_value == None): mean_value, sttdev_value = df.select(mean(column), stddev(column)).first() df = df.withColumn('{}_STANDARD'.format(column), (col(column) - mean_value) / sttdev_value) df = df.drop(column) return df, mean_value, sttdev_value def classify_target(df, column): function = udf(lambda item: 1 if item > 0 else 0, IntegerType()) df = df.withColumn('{}_CLASS'.format(column), function(col(column))) return df def split_data_classification(df, train_percent, test_percent, validate_percent, random_state): # normalize percentage train_percent = train_percent/100 test_percent = test_percent/100 validate_percent = validate_percent/100 # convert data into numpy data = np.array(df.collect(), dtype=np.float64) # get number of records records = data.shape[0] validate_size = int(validate_percent * records) test_size = int(test_percent * records) train_size = records - (validate_size + test_size) # split data data_train, data_remains = train_test_split(data, train_size=train_size, random_state=random_state, stratify=data[:, -1:]) if validate_size == 0: data_test = data_remains data_validate = None else: data_validate, data_test = train_test_split(data_remains, train_size=validate_size, random_state=random_state, stratify=data_remains[:, -1:]) # get x and y arrays x_train = data_train[:, :-1] y_train = data_train[:, -1:] x_validate = data_validate[:, :-1] if data_validate is not None else None y_validate = data_validate[:, -1:] if data_validate is not None else None x_test = data_test[:, :-1] y_test = data_test[:, -1:] # function return return x_train, y_train, x_validate, y_validate, x_test, y_test def split_data_regression(df, train_percent, test_percent, validate_percent, random_state): # normalize percentage train_percent = train_percent/100 test_percent = test_percent/100 validate_percent = validate_percent/100 # convert data into numpy data = np.array(df.collect(), dtype=np.float64) # get number of records records = data.shape[0] validate_size = int(validate_percent * records) test_size = int(test_percent * records) train_size = records - (validate_size + test_size) # split data data_train, data_remains = train_test_split(data, train_size=train_size, random_state=random_state, stratify=data[:, -1:]) if validate_size == 0: data_test = data_remains data_validate = None else: data_validate, data_test = train_test_split(data_remains, train_size=validate_size, random_state=random_state, stratify=data_remains[:, -1:]) # get x and y arrays x_train = data_train[:, :-2] y_train = data_train[:, -2:-1] x_validate = data_validate[:, :-2] if data_validate is not None else None y_validate = data_validate[:, -2:-1] if data_validate is not None else None x_test = data_test[:, :-2] y_test = data_test[:, -2:-1] # function return return x_train, y_train, x_validate, y_validate, x_test, y_test # COMMAND ---------- # dbfs data functions def save_datasets(dataset_name, x_train, y_train, x_validate, y_validate, x_test, y_test, data_columns): # Save data to a local file first. data_filename = '{}.npz'.format(dataset_name) local_data_dir = tempfile.mkdtemp() local_data_path = os.path.join(local_data_dir, data_filename) np.savez(local_data_path, x_train=x_train, y_train=y_train, x_validate=x_validate, y_validate=y_validate, x_test=x_test, y_test=y_test, data_columns=data_columns) # Move it to DBFS, which is shared among cluster nodes. dbfs_tmp_dir = '/dbfs/ml/tmp/hyperopt' os.makedirs(dbfs_tmp_dir, exist_ok=True) dbfs_data_dir = tempfile.mkdtemp(dir=dbfs_tmp_dir) dbfs_data_path = os.path.join(dbfs_data_dir, data_filename) shutil.move(local_data_path, dbfs_data_path) return dbfs_data_path def load_datasets(path): dataset = np.load(path, allow_pickle=True) x_train = dataset['x_train'] y_train = dataset['y_train'] x_validate = dataset['x_validate'] y_validate = dataset['y_validate'] x_test = dataset['x_test'] y_test = dataset['y_test'] data_columns = dataset['data_columns'] return x_train, y_train, x_validate, y_validate, x_test, y_test, data_columns # COMMAND ---------- # general ML training functions def generate_weights(vector, threshold, value_1, value_2): output = np.copy(vector) output[output == threshold] = value_1 output[output != threshold] = value_2 return output def generate_class(vector, threshold, value_1, value_2): output = np.copy(vector) output[output > threshold] = value_1 output[output <= threshold] = value_2 return output def save_results(y_validate, y_test, pred_validate, pred_test): df_validate = pd.DataFrame({'Target': y_validate.reshape(-1), 'Predictions': pred_validate.reshape(-1)}) df_test = pd.DataFrame({'Target': y_test.reshape(-1), 'Predictions': pred_test.reshape(-1)}) df_validate.to_csv('validation_sample.csv', index=False) df_test.to_csv('test_sample.csv', index=False) mlflow.log_artifact('validation_sample.csv') mlflow.log_artifact('test_sample.csv') def log_tags(params): mlflow.set_tag('model_type', params['model_type']) mlflow.set_tag('model_name', params['model_name']) mlflow.set_tag('training_log_path', params['training_log_path']) mlflow.set_tag('dataset_size', params['dataset_size']) mlflow.set_tag('data_query', params['data_query']) def log_pre_processing(params): with open('pre_processing.pickle', 'wb') as file: pickle.dump(params['pre_processing'], file) mlflow.log_artifact('pre_processing.pickle') def metrics(pred, actual, suffix=None, monitoring = False): # define class of predections pred_class = generate_class(pred, 0, 1, 0) actual_class = generate_class(actual, 0, 1, 0) # calculate metrics rmse = mean_squared_error(actual, pred, squared=False) mae = mean_absolute_error(actual, pred) r2 = r2_score(actual, pred) p_c_1, r_c_1, f_c_1, _ = precision_recall_fscore_support(actual_class, pred_class, average='binary', pos_label=1, warn_for = tuple()) p_c_0, r_c_0, f_c_0, _ = precision_recall_fscore_support(actual_class, pred_class, average='binary', pos_label=0, warn_for = tuple()) output = {'rmse' if suffix == None else 'rmse_{}'.format(suffix): rmse, 'mae' if suffix == None else 'mae_{}'.format(suffix): rmse, 'r2' if suffix == None else 'r2_{}'.format(suffix): r2, 'p_c_1' if suffix == None else 'p_c_1_{}'.format(suffix): p_c_1, 'r_c_1' if suffix == None else 'r_c_1_{}'.format(suffix): r_c_1, 'f_c_1' if suffix == None else 'f_c_1_{}'.format(suffix): f_c_1, 'p_c_0' if suffix == None else 'p_c_0_{}'.format(suffix): p_c_0, 'r_c_0' if suffix == None else 'r_c_0_{}'.format(suffix): r_c_0, 'f_c_0' if suffix == None else 'f_c_0_{}'.format(suffix): f_c_0} # log metrics if monitoring == False: for key in output: mlflow.log_metric(key, output[key]) return output def confidence(v_metrics, t_metrics): output = dict() output['confidence_rmse'] = 100 - abs(100 * ((t_metrics['rmse_test'] - v_metrics['rmse_validate']) / v_metrics['rmse_validate'])) output['confidence_rmse'] = 100 - abs(100 * ((t_metrics['mae_test'] - v_metrics['mae_validate']) / v_metrics['mae_validate'])) output['confidence_r2'] = 100 - abs(100 * ((t_metrics['r2_test'] - v_metrics['r2_validate']) / v_metrics['r2_validate'])) output['confidence_p_c_1'] = 100 - abs(100 * ((t_metrics['p_c_1_test'] - v_metrics['p_c_1_validate']) / v_metrics['p_c_1_validate'])) output['confidence_p_c_0'] = 100 - abs(100 * ((t_metrics['p_c_0_test'] - v_metrics['p_c_0_validate']) / v_metrics['p_c_0_validate'])) output['confidence_r_c_1'] = 100 - abs(100 * ((t_metrics['r_c_1_test'] - v_metrics['r_c_1_validate']) / v_metrics['r_c_1_validate'])) output['confidence_r_c_0'] = 100 - abs(100 * ((t_metrics['r_c_0_test'] - v_metrics['r_c_0_validate']) / v_metrics['r_c_0_validate'])) output['confidence_f_c_1'] = 100 - abs(100 * ((t_metrics['f_c_1_test'] - v_metrics['f_c_1_validate']) / v_metrics['f_c_1_validate'])) output['confidence_f_c_0'] = 100 - 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from Tkinter import * import webbrowser url = 'http://www.sampleurl.com' root = Tk() frame = Frame(root) frame.pack() def OpenUrl(): webbrowser.open(url) button = Button(frame, text="CLICK", command=OpenUrl) button.pack() root.mainloop()

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import face_recognition image = face_recognition.load_image_file("/mnt/hgfs/share/raw1.jpg") face_locations = face_recognition.face_locations(image)

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# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import proto # type: ignore __protobuf__ = proto.module( package='google.ads.googleads.v4.enums', marshal='google.ads.googleads.v4', manifest={ 'TargetingDimensionEnum', }, ) class TargetingDimensionEnum(proto.Message): r"""The dimensions that can be targeted. """ class TargetingDimension(proto.Enum): r"""Enum describing possible targeting dimensions.""" UNSPECIFIED = 0 UNKNOWN = 1 KEYWORD = 2 AUDIENCE = 3 TOPIC = 4 GENDER = 5 AGE_RANGE = 6 PLACEMENT = 7 PARENTAL_STATUS = 8 INCOME_RANGE = 9 __all__ = tuple(sorted(__protobuf__.manifest))

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my_list=[1,2,4,5,6,7,8] # def binary_search(arr,num): # first=0 # last=len(arr)-1 # while first <=last: # mid=(first+last)//2 # if arr[mid]==num: # return "Found" # else: # if arr[mid]<num: # first=mid+1 # else: # last=mid-1 # return "Not found" # print(binary_search(my_list,10)) # print(binary_search(my_list,10)) # def search(arr,num): # lower=0 # upper=len(arr)-1 # while(lower<=upper): # mid=(lower+upper)//2 # if arr[mid]==num: # return True # else: # if arr[mid]<num: # lower=mid+1 # else: # upper=mid-1 # return False # print(search(my_list,10)) # print(search(my_list,2))

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import arcpy arcpy.env.workspace=r'D:\Grad_Spring2019\610-Programming\Exercise3\Exercise 3.gdb' recordCount=arcpy.GetCount_management('CallsforService') print(recordCount)

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"""Tests to ensure that the lxml tree builder generates good trees.""" import warnings try: import lxml.etree LXML_PRESENT = True LXML_VERSION = lxml.etree.LXML_VERSION except ImportError as e: LXML_PRESENT = False LXML_VERSION = (0,) if LXML_PRESENT: from bs4.builder import LXMLTreeBuilder, LXMLTreeBuilderForXML from bs4 import ( BeautifulStoneSoup, ) from bs4.testing import ( HTMLTreeBuilderSmokeTest, XMLTreeBuilderSmokeTest, SoupTest, skipIf, ) @skipIf( not LXML_PRESENT, "lxml seems not to be present, not testing its tree builder.") class LXMLTreeBuilderSmokeTest(SoupTest, HTMLTreeBuilderSmokeTest): """See ``HTMLTreeBuilderSmokeTest``.""" @property def default_builder(self): return LXMLTreeBuilder() def test_out_of_range_entity(self): self.assertSoupEquals( "foo&#10000000000000;bar", "foobar") self.assertSoupEquals( "foo&#x10000000000000;bar", "foobar") self.assertSoupEquals( "foo&#1000000000;bar", "foobar") # In lxml < 2.3.5, an empty doctype causes a segfault. Skip this # test if an old version of lxml is installed. @skipIf( not LXML_PRESENT or LXML_VERSION < (2, 3, 5, 0), "Skipping doctype test for old version of lxml to avoid segfault.") def test_empty_doctype(self): soup = self.soup("<!DOCTYPE>") doctype = soup.contents[0] self.assertEqual("", doctype.strip()) def test_beautifulstonesoup_is_xml_parser(self): # Make sure that the deprecated BSS class uses an xml builder # if one is installed. with warnings.catch_warnings(record=True) as w: soup = BeautifulStoneSoup("") self.assertEqual("", str(soup.b)) self.assertTrue("BeautifulStoneSoup class is deprecated" in str(w[0].message)) @skipIf( not LXML_PRESENT, "lxml seems not to be present, not testing its XML tree builder.") class LXMLXMLTreeBuilderSmokeTest(SoupTest, XMLTreeBuilderSmokeTest): """See ``HTMLTreeBuilderSmokeTest``.""" @property def default_builder(self): return LXMLTreeBuilderForXML()

[ "drshah96@gmail.com" ]

drshah96@gmail.com

b1a2ca351fec444118b688bab22debb105cc3f40

05c955a8007b5845228968f3de1b92e4db3c8b1a

/bin/reserve_urn

3da9863173d4631dcfe28189070d811c2b802243

[ "MIT" ]

permissive

runelk/NB_URN_Client_Python

c9c36e5130c6a48db1c4f22cc6c254414179042f

8b58309f9c48f3f5c10065cee02d9ebe2b20ee2c

refs/heads/master

2022-07-31T05:59:47.367500

2014-06-26T12:43:03

20,129,688

0

1

MIT

2022-07-08T19:12:53

2014-05-24T13:05:43

Python

UTF-8

Python

false

1,008

#!/usr/bin/env python import os, sys from optparse import OptionParser import nb_urn_client default_config = os.path.join(os.path.dirname( os.path.abspath(__file__)), '..', 'config', 'config.yml' ) parser = OptionParser() parser.add_option("--urn", dest="urn", help="the URN to reserve") parser.add_option("--username", dest="username", help="Username (if not provided by the config file)") parser.add_option("--password", dest="password", help="Password (if not provided by the config file)") parser.add_option("-c", "--config", dest="config", help="A YAML config file") (options, args) = parser.parse_args() if options.urn: c = nb_urn_client.NbUrnClient( username=options.username if options.username else None, password=options.password if options.password else None, config_file=options.config if options.config else default_config ) c.login() print c.reserve_urn(options.urn) c.logout() else: sys.stderr.write("Usage: reserve_urn --urn URN\n")

[ "rune.knudsen@uib.no" ]

rune.knudsen@uib.no

bb8bfb8faa740e6ec8025c6db5e79e0e5ff83ddd

95b0f53429835929da4bca6d5b5480f96e7514ae

/chrome-screenshot/yahoo_screenshot.py

4a03ce0640f2b944a37244862e42e16464ca2c14

[]

no_license

tyogoutomo/web-scraping

1b23c831f2ed9940ee99d30a8fe65ddaaf36e808

ddcaa0cb8e08b60ec23d356e9915e15bbe01f36b

refs/heads/master

2021-03-07T05:05:41.068849

2020-03-17T10:39:46

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0

null

2020-03-17T10:39:48

2020-03-10T08:35:56

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py

import os, time, errno from datetime import datetime from optparse import OptionParser from selenium import webdriver from selenium.webdriver.chrome.options import Options CHROME_PATH = '/usr/bin/google-chrome' CHROMEDRIVER_PATH = '/usr/bin/chromedriver' WINDOW_SIZE = "2560,1440" chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.add_argument("--window-size=%s" % WINDOW_SIZE) chrome_options.binary_location = CHROME_PATH def YahooCrawler(keyword, out_path): driver = webdriver.Chrome( executable_path=CHROMEDRIVER_PATH, chrome_options=chrome_options ) driver.get("https://images.search.yahoo.com/") time.sleep(3) first_search_bar = driver.find_element_by_class_name("yschsp") first_search_bar.send_keys(keyword) submit_button = driver.find_element_by_class_name("ygbt") submit_button.click() time.sleep(3) scroll_iterator = 0 height = 0 for i in range(10): next_height = driver.execute_script("return document.body.scrollHeight") if next_height == height: time.sleep(3) # break try: driver.execute_script("window.scrollBy(0,512)") time.sleep(3) find_more = driver.find_element_by_name("more-res") find_more.click() print("find more clicked") time.sleep(3) except: print("reached end of page") # driver.close() break height = driver.execute_script("return document.body.scrollHeight") save_path = os.path.join(out_path, keyword) try: os.makedirs(save_path) except OSError as e: if e.errno != errno.EEXIST: raise now = datetime.now() timestamp = datetime.timestamp(now) print('making Yahoo screenshots for', keyword, (i+1)) driver.save_screenshot(os.path.join(save_path, str(timestamp)+'.png')) driver.execute_script("window.scrollBy(0,1280)") time.sleep(5) scroll_iterator += 1 driver.close()

[ "luqman.rahardjo@gmail.com" ]

luqman.rahardjo@gmail.com

83c9ffb1329c04e16bd0ef763824c6cb54b19f7b

3848175d566e8cbd21f66bdca57882706ed6d4ca

/src/7_3_mnist_deep_cnn.py

72946f6e40a43033f7756e217a03658568372ebf

[]

no_license

kasha-seo/ML_forEveryone

a4b519b002843423dad4feecb9b1e02c3ba23e97

0fa42b497995079f2f60fe51f5d03274946ec034

refs/heads/master

2023-08-08T13:13:41.365363

2018-12-30T10:34:26

null

0

null

UTF-8

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py

import tensorflow as tf import random # import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data tf.set_random_seed(777) # reproducibility mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # Check out https://www.tensorflow.org/get_started/mnist/beginners for # more information about the mnist dataset # hyper parameters learning_rate = 0.001 training_epochs = 15 batch_size = 100 # dropout (keep_prob) rate 0.7~0.5 on training, but should be 1 for testing keep_prob = tf.placeholder(tf.float32) # input place holders X = tf.placeholder(tf.float32, [None, 784]) X_img = tf.reshape(X, [-1, 28, 28, 1]) # 이미지 입력으로 넣기 위해 reshape. -1은 N개의 값. img 28x28x1 (black/white) Y = tf.placeholder(tf.float32, [None, 10]) # L1 ImgIn shape=(?, 28, 28, 1) W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01)) # Conv -> (?, 28, 28, 32) # Pool -> (?, 14, 14, 32) L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME') # VALID : not using padding L1 = tf.nn.relu(L1) L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') L1 = tf.nn.dropout(L1, keep_prob=keep_prob) ''' Tensor("Conv2D:0", shape=(?, 28, 28, 32), dtype=float32) Tensor("Relu:0", shape=(?, 28, 28, 32), dtype=float32) Tensor("MaxPool:0", shape=(?, 14, 14, 32), dtype=float32) Tensor("dropout/mul:0", shape=(?, 14, 14, 32), dtype=float32) ''' # L2 ImgIn shape=(?, 14, 14, 32) W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01)) # Conv ->(?, 14, 14, 64) # Pool ->(?, 7, 7, 64) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.relu(L2) L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') L2 = tf.nn.dropout(L2, keep_prob=keep_prob) ''' Tensor("Conv2D_1:0", shape=(?, 14, 14, 64), dtype=float32) Tensor("Relu_1:0", shape=(?, 14, 14, 64), dtype=float32) Tensor("MaxPool_1:0", shape=(?, 7, 7, 64), dtype=float32) Tensor("dropout_1/mul:0", shape=(?, 7, 7, 64), dtype=float32) ''' # L3 ImgIn shape=(?, 7, 7, 64) W3 = tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.01)) # Conv ->(?, 7, 7, 128) # Pool ->(?, 4, 4, 128) # Reshape ->(?, 4 * 4 * 128) # Flatten them for FC L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.relu(L3) L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1], strides=[ 1, 2, 2, 1], padding='SAME') L3 = tf.nn.dropout(L3, keep_prob=keep_prob) L3_flat = tf.reshape(L3, [-1, 128 * 4 * 4]) ''' Tensor("Conv2D_2:0", shape=(?, 7, 7, 128), dtype=float32) Tensor("Relu_2:0", shape=(?, 7, 7, 128), dtype=float32) Tensor("MaxPool_2:0", shape=(?, 4, 4, 128), dtype=float32) Tensor("dropout_2/mul:0", shape=(?, 4, 4, 128), dtype=float32) Tensor("Reshape_1:0", shape=(?, 2048), dtype=float32) ''' # L4 FC 4x4x128 inputs -> 625 outputs W4 = tf.get_variable("W4", shape=[128 * 4 * 4, 625], initializer=tf.contrib.layers.xavier_initializer()) b4 = tf.Variable(tf.random_normal([625])) L4 = tf.nn.relu(tf.matmul(L3_flat, W4) + b4) L4 = tf.nn.dropout(L4, keep_prob=keep_prob) ''' Tensor("Relu_3:0", shape=(?, 625), dtype=float32) Tensor("dropout_3/mul:0", shape=(?, 625), dtype=float32) ''' # L5 Final FC 625 inputs -> 10 outputs W5 = tf.get_variable("W5", shape=[625, 10], initializer=tf.contrib.layers.xavier_initializer()) b5 = tf.Variable(tf.random_normal([10])) logits = tf.matmul(L4, W5) + b5 ''' Tensor("add_1:0", shape=(?, 10), dtype=float32) ''' # define cost/loss & optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # initialize sess = tf.Session() sess.run(tf.global_variables_initializer()) # train my model print('Learning started. It takes sometime.') for epoch in range(training_epochs): avg_cost = 0 total_batch = int(mnist.train.num_examples / batch_size) for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7} c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) avg_cost += c / total_batch print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) print('Learning Finished!') # Test model and check accuracy # if you have a OOM error, please refer to lab-11-X-mnist_deep_cnn_low_memory.py correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print('Accuracy:', sess.run(accuracy, feed_dict={ X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1})) # Get one and predict r = random.randint(0, mnist.test.num_examples - 1) print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1))) print("Prediction: ", sess.run( tf.argmax(logits, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1})) # plt.imshow(mnist.test.images[r:r + 1]. # reshape(28, 28), cmap='Greys', interpolation='nearest') # plt.show()

[ "suhjh14@naver.com" ]

suhjh14@naver.com

adb2babffe1e8af59930020f6c17f6d45db5f76f

5a52ccea88f90dd4f1acc2819997fce0dd5ffb7d

/alipay/aop/api/request/KoubeiTradeOrderConsultRequest.py

2defd325c725861c41724ed3832b3e090ad2407b

[ "Apache-2.0" ]

permissive

alipay/alipay-sdk-python-all

8bd20882852ffeb70a6e929038bf88ff1d1eff1c

1fad300587c9e7e099747305ba9077d4cd7afde9

refs/heads/master

2023-08-27T21:35:01.778771

2023-08-23T07:12:26

133,338,689

247

70

Apache-2.0

2023-04-25T04:54:02

2018-05-14T09:40:54

Python

UTF-8

Python

false

3,936

py

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.KoubeiTradeOrderConsultModel import KoubeiTradeOrderConsultModel class KoubeiTradeOrderConsultRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, KoubeiTradeOrderConsultModel): self._biz_content = value else: self._biz_content = KoubeiTradeOrderConsultModel.from_alipay_dict(value) @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'koubei.trade.order.consult' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() return multipart_params

[ "liuqun.lq@alibaba-inc.com" ]

liuqun.lq@alibaba-inc.com

1545c927376751fd9a43d184b304383c8362e632

24372e13ed3d8b6f11fb3681421ac95d571e6320

/python-plotting-api-master/test/test_app.py

02ae58a884523c96da72687b688850f45e009aea

[]

no_license

aokada228/pythonplotting

9ab54eb617d9b317ab4199b4f63dc116f11c8e4a

235527b4c80e3b0914bff15413d7e29d42043c73

refs/heads/master

2023-02-05T12:06:28.473671

2019-12-05T20:42:46

226,185,986

0

null

2023-02-02T05:14:44

2019-12-05T20:45:00

Jupyter Notebook

UTF-8

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py

from python_plotting_api.app import app test_client = app.test_client() def test_root(): response = test_client.get('/') assert 200 == response.status_code def test_get_correlation_matrix(): response = test_client.get('/plots/breast_cancer_data/correlation_matrix') assert 200 == response.status_code assert 'image/png' == response.content_type def test_get_pairplot_matrix(): cols = ['worst concave points', 'mean concavity', 'worst perimeter', 'worst radius', 'worst area'] query_string = ','.join(cols) response = test_client.get(f'/plots/breast_cancer_data/pairplot/features/{query_string}') assert 200 == response.status_code assert 'image/png' == response.content_type cols = ['worst concave points', 'mean concavity', 'worst perimeter', 'worst radius', 'worst area', 'wrong_feature'] query_string = ','.join(cols) response = test_client.get(f'/plots/breast_cancer_data/pairplot/features/{query_string}') assert 400 == response.status_code

[ "azusaokada@bobcat-124-191.bates.edu" ]

azusaokada@bobcat-124-191.bates.edu

6315e17d884b08aa11eab2a3d71e667e140f18bc

54f352a242a8ad6ff5516703e91da61e08d9a9e6

/Source Codes/AtCoder/abc040/A/4812227.py

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[]

no_license

Kawser-nerd/CLCDSA

5cbd8a4c3f65173e4e8e0d7ed845574c4770c3eb

aee32551795763b54acb26856ab239370cac4e75

refs/heads/master

2022-02-09T11:08:56.588303

2022-01-26T18:53:40

211,783,197

23

9

null

UTF-8

Python

false

48

py

n,x=map(int,input().split());print(min(x-1,n-x))

[ "kwnafi@yahoo.com" ]

kwnafi@yahoo.com

5cf987b549ccdf421a9487686fc93e73f7fe1de1

729212d61f20666b5cef0aa65e17ddd3a326c58e

/legacy/servolib2.py

eb961278a68739a9f1d9966445df345528b5a767

[]

no_license

anatolyilin/OpenCV-bot

8dce147bf28fa0f4bd828770781d09b9a1dd0974

05351c7d1a733ddf667bbf023e56b41618e462dc

refs/heads/master

2020-03-17T14:01:34.671407

2018-06-06T20:27:51

133,654,458

0

null

UTF-8

Python

false

1,888

py

import RPi.GPIO as GPIO import pickle pwm = 0 debug_info = 0 #zero_pos = 1.2 oude waarde zero_pos = 0 zero_pos_man = 0 rico = 0 left_val = 0 right_val = 0 def init_pickle(file ='servo.conf', req_debug_info = 0): global debug_info, zero_pos , rico, left_val, right_val, zero_pos_man , pwm debug_info = req_debug_info # [ rico , zero_pos, left_val, right_val , manual zero , pin ] with open(file, 'rb') as fp: listRead = pickle.load(fp) if listRead[4] != -1: zero_pos_man = listRead[4] else: zero_pos_man = listRead[1] zero_pos= listRead[1] if debug_info == 1: print "Pickle data" + str(listRead) pin = listRead[5] GPIO.setmode(GPIO.BOARD) GPIO.setup(pin, GPIO.OUT) pwm = GPIO.PWM(pin, 50) pwm.start(zero_pos) rico = listRead[0] left_val =listRead[2] right_val = listRead[3] def init_servo(pin=8,req_debug_info = 0, req_zero_pos = 7.5, left_val_req = 2, right_val_req=12.5): GPIO.setmode(GPIO.BOARD) global debug_info, zero_pos, rico, left_val, right_val, zero_pos, pwm debug_info = req_debug_info left_val = left_val_req right_val = right_val_req zero_pos = req_zero_pos rico = (right_val-left_val)/180 GPIO.setup(pin, GPIO.OUT) pwm =GPIO.PWM(pin, 50) pwm.start(zero_pos) if debug_info == 1: print "Servo initialled on pin %d \n start postion is set to %f (default: 7.5) \n Call debug(False) to disable" %(pin, zero_pos ) def moveto(posDeg, req_debug_info=0): pos = -rico*posDeg + zero_pos pwm.ChangeDutyCycle(pos) if debug_info == 1 or req_debug_info == 1: print "Servo set on %f pos, based on start position %f (default: 7.5)" % (pos, zero_pos) def movetoZero(): moveto(zero_pos_man) def debug(value = False): debug_info == 0 if value: debug_info == 1 def cleanup(): GPIO.cleanup()

[ "user43671@user43671s-MacBook-Pro.local" ]

user43671@user43671s-MacBook-Pro.local

99006543ae64f269e68e80e36cfcd49436d904c2

7a571842e05ce6bdb75a7f247a5113235d4d24f3

/users/migrations/0004_auto_20200424_1647.py

200309a89122cc65a210e07821fb8351bc7c89f3

[]

no_license

bneeland/meal-hippo

7cb9ad75e32913f809d32070facf0f8318e270e9

f9ace432b163b2e921a838620696629a0d1be9b2

refs/heads/master

2023-01-03T21:02:44.831870

2020-10-21T15:39:48

254,940,611

0

null

UTF-8

Python

false

555

py

# Generated by Django 3.0.4 on 2020-04-24 22:47 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('users', '0003_customuser_instructions'), ] operations = [ migrations.RemoveField( model_name='customuser', name='address', ), migrations.RemoveField( model_name='customuser', name='instructions', ), migrations.RemoveField( model_name='customuser', name='phone', ), ]

[ "brian.neeland@protonmail.com" ]

brian.neeland@protonmail.com

b2801badf5bd6284bd289b522b327b3edbb347b5

6131b2738a7c087dfa6907c624453576f6f0e393

/银行转账pmysql版本/Bank_Transfer.py

34c606ac76f8f664750def22983d82d05855ec09

[]

no_license

heheddff/myPythonProcess

60ef240130cd02906dc500eedb397a9662c02e5a

885a25dd2a9cd43801306d9e70b9ce89daec4406

refs/heads/master

2020-04-08T19:09:18.192738

2019-08-06T02:52:54

159,642,468

4

5

null

GB18030

Python

false

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py

# coding=gbk import pymysql class Money(): def __init__(self,sid,tid,mon): self.conn = pymysql.connect( host="127.0.0.1", port=3306, user='root', passwd='****', db='test' ) self.cursor = self.conn.cursor(); self.table = "money" self.sid = sid self.tid = tid self.mon = mon def checkuser(self,userid): try: sql = "select userid from "+self.table+" where userid=%s" self.cursor.execute(sql,(userid,)) res = self.cursor.fetchone() if res is None: raise Exception("账号{}不存在".format(userid)) finally: pass #self.cursor.close() #self.conn.close() def reducemoney(self,userid,money): try: sql = "update "+self.table+" set money=money-%s where userid=%s" self.cursor.execute(sql,(money,userid)) if self.cursor.rowcount != 1: raise Exception("账号{}转账失败".format(userid)) finally: pass #self.cursor.close() #self.conn.close() def addmoney(self,userid,money): try: sql = "update "+self.table+" set money=money+%s where userid=%s" self.cursor.execute(sql,(money,userid,)) if self.cursor.rowcount != 1: raise Exception("账号{}收账失败".format(userid)) finally: pass #self.cursor.close() #self.conn.close() def checkmoney(self,userid,money): try: sql = "select userid from "+self.table+" where userid=%s and money>%s" self.cursor.execute(sql,(userid,money)) res = self.cursor.fetchone() if res is None: raise Exception("账号{}余额小于{}".format(userid,money)) finally: pass #self.cursor.close() #self.conn.close() def run(self): try: self.checkuser(self.sid) self.checkuser(self.tid) self.checkmoney(self.sid,self.mon) self.reducemoney(self.sid,self.mon) self.addmoney(self.tid,self.mon) self.conn.commit() except Exception as e: self.conn.rollback() raise e finally: #pass self.cursor.close() self.conn.close() try: m = Money(11,13,100) m.run() except Exception as e: #pass print(e) else: print("转账成功")

[ "qq2003qq@126.com" ]

qq2003qq@126.com

f819b4533712c554f55d0b889a5f81e78c9c0c59

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refs/heads/master

2021-05-04T10:21:57.163472

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""" WSGI config for PyHtmlDebuger project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "PyHtmlDebuger.settings") application = get_wsgi_application()

[ "wing.gao@live.com" ]

wing.gao@live.com

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/intro-ansible/venv3/lib/python3.8/site-packages/ansible_test/_internal/import_analysis.py

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SimonFangCisco/dne-dna-code

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refs/heads/master

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"""Analyze python import statements.""" from __future__ import (absolute_import, division, print_function) __metaclass__ = type import ast import os import re from . import types as t from .io import ( read_binary_file, ) from .util import ( display, ApplicationError, is_subdir, ) from .data import ( data_context, ) VIRTUAL_PACKAGES = set([ 'ansible.module_utils.six', ]) def get_python_module_utils_imports(compile_targets): """Return a dictionary of module_utils names mapped to sets of python file paths. :type compile_targets: list[TestTarget] :rtype: dict[str, set[str]] """ module_utils = enumerate_module_utils() virtual_utils = set(m for m in module_utils if any(m.startswith('%s.' % v) for v in VIRTUAL_PACKAGES)) module_utils -= virtual_utils imports_by_target_path = {} for target in compile_targets: imports_by_target_path[target.path] = extract_python_module_utils_imports(target.path, module_utils) def recurse_import(import_name, depth=0, seen=None): # type: (str, int, t.Optional[t.Set[str]]) -> t.Set[str] """Recursively expand module_utils imports from module_utils files.""" display.info('module_utils import: %s%s' % (' ' * depth, import_name), verbosity=4) if seen is None: seen = set([import_name]) results = set([import_name]) # virtual packages depend on the modules they contain instead of the reverse if import_name in VIRTUAL_PACKAGES: for sub_import in sorted(virtual_utils): if sub_import.startswith('%s.' % import_name): if sub_import in seen: continue seen.add(sub_import) matches = sorted(recurse_import(sub_import, depth + 1, seen)) for result in matches: results.add(result) import_path = get_import_path(import_name) if import_path not in imports_by_target_path: import_path = get_import_path(import_name, package=True) if import_path not in imports_by_target_path: raise ApplicationError('Cannot determine path for module_utils import: %s' % import_name) # process imports in reverse so the deepest imports come first for name in sorted(imports_by_target_path[import_path], reverse=True): if name in virtual_utils: continue if name in seen: continue seen.add(name) matches = sorted(recurse_import(name, depth + 1, seen)) for result in matches: results.add(result) return results for module_util in module_utils: # recurse over module_utils imports while excluding self module_util_imports = recurse_import(module_util) module_util_imports.remove(module_util) # add recursive imports to all path entries which import this module_util for target_path in imports_by_target_path: if module_util in imports_by_target_path[target_path]: for module_util_import in sorted(module_util_imports): if module_util_import not in imports_by_target_path[target_path]: display.info('%s inherits import %s via %s' % (target_path, module_util_import, module_util), verbosity=6) imports_by_target_path[target_path].add(module_util_import) imports = dict([(module_util, set()) for module_util in module_utils | virtual_utils]) for target_path in imports_by_target_path: for module_util in imports_by_target_path[target_path]: imports[module_util].add(target_path) # for purposes of mapping module_utils to paths, treat imports of virtual utils the same as the parent package for virtual_util in virtual_utils: parent_package = '.'.join(virtual_util.split('.')[:-1]) imports[virtual_util] = imports[parent_package] display.info('%s reports imports from parent package %s' % (virtual_util, parent_package), verbosity=6) for module_util in sorted(imports): if not imports[module_util]: package_path = get_import_path(module_util, package=True) if os.path.exists(package_path) and not os.path.getsize(package_path): continue # ignore empty __init__.py files display.warning('No imports found which use the "%s" module_util.' % module_util) return imports def get_python_module_utils_name(path): # type: (str) -> str """Return a namespace and name from the given module_utils path.""" base_path = data_context().content.module_utils_path if data_context().content.collection: prefix = 'ansible_collections.' + data_context().content.collection.prefix + 'plugins.module_utils' else: prefix = 'ansible.module_utils' if path.endswith('/__init__.py'): path = os.path.dirname(path) if path == base_path: name = prefix else: name = prefix + '.' + os.path.splitext(os.path.relpath(path, base_path))[0].replace(os.path.sep, '.') return name def enumerate_module_utils(): """Return a list of available module_utils imports. :rtype: set[str] """ module_utils = [] for path in data_context().content.walk_files(data_context().content.module_utils_path): ext = os.path.splitext(path)[1] if ext != '.py': continue module_utils.append(get_python_module_utils_name(path)) return set(module_utils) def extract_python_module_utils_imports(path, module_utils): """Return a list of module_utils imports found in the specified source file. :type path: str :type module_utils: set[str] :rtype: set[str] """ # Python code must be read as bytes to avoid a SyntaxError when the source uses comments to declare the file encoding. # See: https://www.python.org/dev/peps/pep-0263 # Specifically: If a Unicode string with a coding declaration is passed to compile(), a SyntaxError will be raised. code = read_binary_file(path) try: tree = ast.parse(code) except SyntaxError as ex: # Treat this error as a warning so tests can be executed as best as possible. # The compile test will detect and report this syntax error. display.warning('%s:%s Syntax error extracting module_utils imports: %s' % (path, ex.lineno, ex.msg)) return set() finder = ModuleUtilFinder(path, module_utils) finder.visit(tree) return finder.imports def get_import_path(name, package=False): # type: (str, bool) -> str """Return a path from an import name.""" if package: filename = os.path.join(name.replace('.', '/'), '__init__.py') else: filename = '%s.py' % name.replace('.', '/') if name.startswith('ansible.module_utils.') or name == 'ansible.module_utils': path = os.path.join('lib', filename) elif data_context().content.collection and ( name.startswith('ansible_collections.%s.plugins.module_utils.' % data_context().content.collection.full_name) or name == 'ansible_collections.%s.plugins.module_utils' % data_context().content.collection.full_name): path = '/'.join(filename.split('/')[3:]) else: raise Exception('Unexpected import name: %s' % name) return path def path_to_module(path): # type: (str) -> str """Convert the given path to a module name.""" module = os.path.splitext(path)[0].replace(os.path.sep, '.') if module.endswith('.__init__'): module = module[:-9] return module def relative_to_absolute(name, level, module, path, lineno): # type: (str, int, str, str, int) -> str """Convert a relative import to an absolute import.""" if level <= 0: absolute_name = name elif not module: display.warning('Cannot resolve relative import "%s%s" in unknown module at %s:%d' % ('.' * level, name, path, lineno)) absolute_name = 'relative.nomodule' else: parts = module.split('.') if level >= len(parts): display.warning('Cannot resolve relative import "%s%s" above module "%s" at %s:%d' % ('.' * level, name, module, path, lineno)) absolute_name = 'relative.abovelevel' else: absolute_name = '.'.join(parts[:-level] + [name]) return absolute_name class ModuleUtilFinder(ast.NodeVisitor): """AST visitor to find valid module_utils imports.""" def __init__(self, path, module_utils): """Return a list of module_utils imports found in the specified source file. :type path: str :type module_utils: set[str] """ self.path = path self.module_utils = module_utils self.imports = set() # implicitly import parent package if path.endswith('/__init__.py'): path = os.path.split(path)[0] if path.startswith('lib/ansible/module_utils/'): package = os.path.split(path)[0].replace('/', '.')[4:] if package != 'ansible.module_utils' and package not in VIRTUAL_PACKAGES: self.add_import(package, 0) self.module = None if data_context().content.is_ansible: # Various parts of the Ansible source tree execute within diffent modules. # To support import analysis, each file which uses relative imports must reside under a path defined here. # The mapping is a tuple consisting of a path pattern to match and a replacement path. # During analyis, any relative imports not covered here will result in warnings, which can be fixed by adding the appropriate entry. path_map = ( ('^hacking/build_library/build_ansible/', 'build_ansible/'), ('^lib/ansible/', 'ansible/'), ('^test/lib/ansible_test/_data/sanity/validate-modules/', 'validate_modules/'), ('^test/units/', 'test/units/'), ('^test/lib/ansible_test/_internal/', 'ansible_test/_internal/'), ('^test/integration/targets/.*/ansible_collections/(?P<ns>[^/]*)/(?P<col>[^/]*)/', r'ansible_collections/\g<ns>/\g<col>/'), ('^test/integration/targets/.*/library/', 'ansible/modules/'), ) for pattern, replacement in path_map: if re.search(pattern, self.path): revised_path = re.sub(pattern, replacement, self.path) self.module = path_to_module(revised_path) break else: # This assumes that all files within the collection are executed by Ansible as part of the collection. # While that will usually be true, there are exceptions which will result in this resolution being incorrect. self.module = path_to_module(os.path.join(data_context().content.collection.directory, self.path)) # noinspection PyPep8Naming # pylint: disable=locally-disabled, invalid-name def visit_Import(self, node): """ :type node: ast.Import """ self.generic_visit(node) # import ansible.module_utils.MODULE[.MODULE] # import ansible_collections.{ns}.{col}.plugins.module_utils.module_utils.MODULE[.MODULE] self.add_imports([alias.name for alias in node.names], node.lineno) # noinspection PyPep8Naming # pylint: disable=locally-disabled, invalid-name def visit_ImportFrom(self, node): """ :type node: ast.ImportFrom """ self.generic_visit(node) if not node.module: return module = relative_to_absolute(node.module, node.level, self.module, self.path, node.lineno) if not module.startswith('ansible'): return # from ansible.module_utils import MODULE[, MODULE] # from ansible.module_utils.MODULE[.MODULE] import MODULE[, MODULE] # from ansible_collections.{ns}.{col}.plugins.module_utils import MODULE[, MODULE] # from ansible_collections.{ns}.{col}.plugins.module_utils.MODULE[.MODULE] import MODULE[, MODULE] self.add_imports(['%s.%s' % (module, alias.name) for alias in node.names], node.lineno) def add_import(self, name, line_number): """ :type name: str :type line_number: int """ import_name = name while self.is_module_util_name(name): if name in self.module_utils: if name not in self.imports: display.info('%s:%d imports module_utils: %s' % (self.path, line_number, name), verbosity=5) self.imports.add(name) return # duplicate imports are ignored name = '.'.join(name.split('.')[:-1]) if is_subdir(self.path, data_context().content.test_path): return # invalid imports in tests are ignored # Treat this error as a warning so tests can be executed as best as possible. # This error should be detected by unit or integration tests. display.warning('%s:%d Invalid module_utils import: %s' % (self.path, line_number, import_name)) def add_imports(self, names, line_no): # type: (t.List[str], int) -> None """Add the given import names if they are module_utils imports.""" for name in names: if self.is_module_util_name(name): self.add_import(name, line_no) @staticmethod def is_module_util_name(name): # type: (str) -> bool """Return True if the given name is a module_util name for the content under test. External module_utils are ignored.""" if data_context().content.is_ansible and name.startswith('ansible.module_utils.'): return True if data_context().content.collection and name.startswith('ansible_collections.%s.plugins.module_utils.' % data_context().content.collection.full_name): return True return False

[ "sifang@cisco.com" ]

sifang@cisco.com

636c7ba4934f2730f0e4c252572cf5d8c46d814b

6016100d6707eb8c3a4c53df81b40a0e235e04fd

/whois.py

f5898a69cdda0807e69111fbea760a3b8f6959b9

[]

no_license

Ulate11/whois-assignment

86d10cbec1cf472b93f60b9ae264c1cb0068e6e0

8537bc231d73d064485a5cea2ef342a2e111d2b7

refs/heads/main

2023-08-21T18:49:58.267983

2021-09-15T16:22:19

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import bz2, json, pickle, yaml import smtplib, ssl from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from whoisapi import * # file to store the info provided by whoisxmlapi. SAVED_DATA_FILE = '/app/yesterday.pic' # list of domains to verify. DOMAINS_LIST_FILE = '/app/domains.yml' # the following is sensitive data, please clean values when publishing the code on public sities like github. #api key for whoisxmlapi apiKey = 'your whoisxmlapi key' # email credentials for sending emails. smtpPort = 587 smtpServer = 'smtp.gmail.com' senderMail = 'your email' senderPw = 'your password' recipients = ["<mail1>", "<mail2>"] client = None # old data loaded from file yesterdayData = {} # current data provided by whoisxmlapi currentData = {} # domains with updated information, these will be send by e-mail. updatedData = {} def loadSettings(): global senderMail, senderPw, smtpServer, smtpPort, recipients, apiKey y = loadYaml("/app/appSettings.yaml") senderMail = y['senderMail'] senderPw = y['senderPassword'] recipients = y['recipients'] smtpServer = y['smtpServer'] smtpPort = y['smtpPort'] apiKey = y['whoisApiKey'] def setWhoisClient(): global client client = Client(api_key = apiKey) def loadPickle(path:str): """ loads a compressed pickle file @param path: the string path to the file to be loaded @return: the pyhton object from the file. """ b = bz2.BZ2File(path, 'rb') return pickle.load(b) def savePickle(d, path:str): """ saves the provided data in the specified path, pickled and compressed in bz2 format. @param d: the python object to be saved. @param path: the file to the file in the disk. This file will be replaced if exist. """ f= bz2.BZ2File(path, 'wb') pickle.dump(d, f, 4) f.close() def getDomainInfo(domain:str): """ ask to whoisxmlapi for the information of the domain specified. note: set the client variable before using this function. @param domain: a str with the domain @return whois object. """ return client.data(domain) def loadYaml(path:str): """ loads a yaml file from the specified path. @param path: path to te yaml file. @return the loaded yaml in memory. """ with open(path) as f: return yaml.full_load(f) def setEmail(d, customField, w): """ this function checks if the field 'email' exist in the specified object, and checks if the field is not blank. If previous conditions are true, add the value to the provided dictionary in the specified customField. @param d: the dictionary to add the field, if this field exist. @param customField: the name of the field as the value will be added to the dictionary. @param w: the object to be verified. """ try: email = w.email if email: d[customField] = email except: pass def processDomain (domain:str): """ this function obtains the domain information, then takes the needed values used in this script. @param domain: the string domain used to obtain the info from whoisxmlapi @return: dictionary with the needed information for the assignment. """ print ("processing: ", domain) w = getDomainInfo(domain) data = { 'whoisCreatedDate': w.created_date, 'whoisUpdatedDate': w.updated_date, 'whoisExpiresDate': w.expires_date, 'domainName': w.domain_name } # sometimes registrant is not present. if w.registrant: try: data['registrantName'] = w.registrant.name except: pass # look for available emails. emails = {} contactEmail = w.contact_email if contactEmail: emails['contactEmail'] = contactEmail setEmail(emails, 'registrant', w.registrant) setEmail(emails, 'administrativeContact', w.administrative_contact) setEmail(emails, 'technicalContact', w.technical_contact) setEmail(emails, 'billingContact', w.billing_contact) setEmail(emails, 'zoneContact', w.zone_contact) data['emails'] = emails return data def processYmlDomains(path:str): """ this function obtains the information from whoisxmlapi, for each domain present in the specified path file. the info will be stored in the global dictionary currentData. @param path: the yaml file with the domains list. """ d = loadYaml(path) for k in d['domains']: currentData[k] = processDomain(k) def checkUpdatedInfo(oldInfo, newInfo): """ this function compares the old and new information. If its different, then returns True. @param oldInfo: old information, typically the stored in SAVED_DATA_FILE @param newInfo: the new information, typically the provided by whoisxmlapi. @return: True if the new information is updated, False otherwise. """ # check all keys from newInfo compared with oldInfo keys. for k in newInfo: # ignore emails and registrantName keys, those shouldn't be checked. if k in ('emails', 'registrantName'): continue if oldInfo[k] != newInfo[k]: return True # the same as above but for emails. oldEmails = oldInfo['emails'] newEmails = newInfo['emails'] for k in newEmails: if k not in oldEmails: return True if oldEmails[k] != newEmails[k]: return True # since blank fields aren't registered, we need to check if some email address aren't present in the new information. for k in oldEmails: if k not in newEmails: return True return False def runProcess(): """ the main process of the script """ loadSettings() global yesterdayData # try to load the file with the old info, the first time the script is executed, this file doesn't exist. try: yesterdayData = loadPickle(SAVED_DATA_FILE) except: print('unable to load the saved data from yesterday') setWhoisClient() processYmlDomains(DOMAINS_LIST_FILE) # check if any domains information have been updated. for k in yesterdayData: if (k in currentData) and checkUpdatedInfo(yesterdayData[k], currentData[k]): updatedData[k] = currentData[k] if updatedData: sendMail("domains were updated!", 'See the info in the attached json', RECIPIENTS, updatedData) savePickle(currentData, SAVED_DATA_FILE) print ("Success!") def sendMail(title, body, recipients, data = None, dataName = 'data.json'): """ sends an e-mail with the provided info. @param title: email Subject. @param body: email body. @param recipients: recipients email addresses. @param data: python object to be send in the e-mail as json format. @param dataName: the json file name to be attached in the email. It will contain the json data. """ msg = MIMEMultipart('alternative') msg['Subject'] = title msg['From'] = senderMail msg['To'] = ', '.join(recipients) msg.attach(MIMEText(body, 'plain')) if (data): attachment = MIMEText(json.dumps(data, default=str)) attachment.add_header('Content-Disposition', 'attachment', filename=dataName) msg.attach(attachment) context = ssl.create_default_context() with smtplib.SMTP(smtpServer, smtpPort) as server: server.starttls(context=context) server.login(senderMail, senderPw) server.sendmail(senderMail, recipients, msg.as_string()) if __name__ == "__main__": runProcess()

[ "noreply@github.com" ]

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/scripts/generator.py

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[ "Apache-2.0" ]

permissive

ChrisStedman/graduate_coding

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refs/heads/main

2023-05-06T06:50:47.199930

2021-05-30T11:12:54

371,291,739

0

Apache-2.0

2021-05-27T07:55:09

2021-05-27T07:55:08

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from faker import Faker import random import json fake = Faker(['en-AU']) members = [] for id in range(15000): members.append({ 'id': id, 'firstName': fake.first_name(), 'lastName': fake.last_name(), 'address': fake.address(), 'active': bool(random.getrandbits(1)) }) json_file = open('members.json', 'w') json_file.write(json.dumps(members)) json_file.close() print(fake.address())

[ "dean.baker@pexa.com.au" ]

dean.baker@pexa.com.au

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/00_simple-ml-model.py

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[ "MIT" ]

permissive

mtraina/ml-basis

3b73ab704726bbb2db849b2f32e538d5740039e2

5b6b8a7fad5314fab0b41667a3e6759046b33792

refs/heads/master

2020-03-23T15:30:57.718905

2018-07-20T20:44:29

141,753,626

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- ''' example took from https://towardsdatascience.com/simple-machine-learning-model-in-python-in-5-lines-of-code-fe03d72e78c6 ''' ''' prepare the data ''' from random import randint TRAIN_SET_LIMIT = 10000 TRAIN_SET_COUNT = 4 TRAIN_INPUT = list() TRAIN_OUTPUT = list() for i in range(TRAIN_SET_COUNT): a = randint(0, TRAIN_SET_LIMIT) b = randint(0, TRAIN_SET_LIMIT) c = randint(0, TRAIN_SET_LIMIT) op = a + 2*b + 3*c TRAIN_INPUT.append([a,b,c]) TRAIN_OUTPUT.append(op) ''' create and train the model ''' from sklearn.linear_model import LinearRegression predictor = LinearRegression() predictor.fit(X=TRAIN_INPUT, y=TRAIN_OUTPUT) ''' test the model ''' X_TEST = [[10,20,30]] outcome = predictor.predict(X=X_TEST) coefficients = predictor.coef_ print('Outcome: {}\nCoefficients:{}'.format(outcome, coefficients))

[ "matteo.traina.mail@gmail.com" ]

matteo.traina.mail@gmail.com

d47d1f916f00f4dd257ea19a842125ad1ca74602

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/tstapp/urls.py

a45267800827e9f166a03780bb2039b1f4535d90

[]

no_license

Muhsin345/TSTREP

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refs/heads/main

2023-08-15T02:37:25.647986

2021-09-15T17:08:59

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0

null

UTF-8

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false

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from django.urls import path from . import views urlpatterns=[ path('Login1/',views.fnLogin), path('Details/',views.fnDetails), path('Userhome/',views.fnUser), path('fbhome/',views.fnFB), path('prdcts/',views.fnprdcts), path('bootstrap/',views.fnnav), path('gridnav/',views.fngrid), path('facebook/',views.facebook), path('sample/',views.navsmpl) ]

[ "mzmuhzin123@gmail.com" ]

mzmuhzin123@gmail.com

d1fb7632fd9a98a58d7346669aaea4a73dda39be

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/underground/final_statistics/2.5_sort_data.py

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[]

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dcrozz/python

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refs/heads/master

2020-12-24T16:35:04.409976

2017-01-18T13:49:50

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if __name__ == '__main__': from glob import glob file_names = glob('*.txt') for file_name in file_names: with open(file_name) as data: lines = data.readlines() sort_list = [] for line in lines: cur_line = line.strip().split(',') sort_list.append(cur_line) sort_list.sort(key = lambda l : int(l[-1])) with open(file_name,'w') as out: for item in sort_list: out.write(','.join(item) + '\n')

[ "ukeyim@gmail.com" ]

ukeyim@gmail.com

cd7c58f94edaf9c24fad73a64af8e44d5887e94d

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/mifare_classic/src/python/mifare_classic.py

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# This is a generated file! Please edit source .ksy file and use kaitai-struct-compiler to rebuild from pkg_resources import parse_version import kaitaistruct from kaitaistruct import KaitaiStruct, KaitaiStream, BytesIO if parse_version(kaitaistruct.__version__) < parse_version('0.9'): raise Exception("Incompatible Kaitai Struct Python API: 0.9 or later is required, but you have %s" % (kaitaistruct.__version__)) class MifareClassic(KaitaiStruct): """You can get a dump for testing by the link: https://github.com/zhovner/mfdread/raw/master/dump.mfd .. seealso:: Source - https://github.com/nfc-tools/libnfc https://www.nxp.com/docs/en/data-sheet/MF1S70YYX_V1.pdf """ def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self._raw_sectors = [] self.sectors = [] i = 0 while not self._io.is_eof(): self._raw_sectors.append(self._io.read_bytes((((4 if i >= 32 else 1) * 4) * 16))) _io__raw_sectors = KaitaiStream(BytesIO(self._raw_sectors[-1])) self.sectors.append(MifareClassic.Sector(i == 0, _io__raw_sectors, self, self._root)) i += 1 class Key(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.key = self._io.read_bytes(6) class Sector(KaitaiStruct): def __init__(self, has_manufacturer, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.has_manufacturer = has_manufacturer self._read() def _read(self): if self.has_manufacturer: self.manufacturer = MifareClassic.Manufacturer(self._io, self, self._root) self._raw_data_filler = self._io.read_bytes(((self._io.size() - self._io.pos()) - 16)) _io__raw_data_filler = KaitaiStream(BytesIO(self._raw_data_filler)) self.data_filler = MifareClassic.Sector.Filler(_io__raw_data_filler, self, self._root) self.trailer = MifareClassic.Trailer(self._io, self, self._root) class Values(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.values = [] i = 0 while not self._io.is_eof(): self.values.append(MifareClassic.Sector.Values.ValueBlock(self._io, self, self._root)) i += 1 class ValueBlock(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.valuez = [None] * (3) for i in range(3): self.valuez[i] = self._io.read_u4le() self.addrz = [None] * (4) for i in range(4): self.addrz[i] = self._io.read_u1() @property def addr(self): if hasattr(self, '_m_addr'): return self._m_addr if hasattr(self, '_m_addr') else None if self.valid: self._m_addr = self.addrz[0] return self._m_addr if hasattr(self, '_m_addr') else None @property def addr_valid(self): if hasattr(self, '_m_addr_valid'): return self._m_addr_valid if hasattr(self, '_m_addr_valid') else None self._m_addr_valid = ((self.addrz[0] == ~(self.addrz[1])) and (self.addrz[0] == self.addrz[2]) and (self.addrz[1] == self.addrz[3])) return self._m_addr_valid if hasattr(self, '_m_addr_valid') else None @property def valid(self): if hasattr(self, '_m_valid'): return self._m_valid if hasattr(self, '_m_valid') else None self._m_valid = ((self.value_valid) and (self.addr_valid)) return self._m_valid if hasattr(self, '_m_valid') else None @property def value_valid(self): if hasattr(self, '_m_value_valid'): return self._m_value_valid if hasattr(self, '_m_value_valid') else None self._m_value_valid = ((self.valuez[0] == ~(self.valuez[1])) and (self.valuez[0] == self.valuez[2])) return self._m_value_valid if hasattr(self, '_m_value_valid') else None @property def value(self): if hasattr(self, '_m_value'): return self._m_value if hasattr(self, '_m_value') else None if self.valid: self._m_value = self.valuez[0] return self._m_value if hasattr(self, '_m_value') else None class Filler(KaitaiStruct): """only to create _io.""" def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.data = self._io.read_bytes(self._io.size()) @property def block_size(self): if hasattr(self, '_m_block_size'): return self._m_block_size if hasattr(self, '_m_block_size') else None self._m_block_size = 16 return self._m_block_size if hasattr(self, '_m_block_size') else None @property def data(self): if hasattr(self, '_m_data'): return self._m_data if hasattr(self, '_m_data') else None self._m_data = self.data_filler.data return self._m_data if hasattr(self, '_m_data') else None @property def blocks(self): if hasattr(self, '_m_blocks'): return self._m_blocks if hasattr(self, '_m_blocks') else None io = self.data_filler._io _pos = io.pos() io.seek(0) self._m_blocks = [] i = 0 while not io.is_eof(): self._m_blocks.append(io.read_bytes(self.block_size)) i += 1 io.seek(_pos) return self._m_blocks if hasattr(self, '_m_blocks') else None @property def values(self): if hasattr(self, '_m_values'): return self._m_values if hasattr(self, '_m_values') else None io = self.data_filler._io _pos = io.pos() io.seek(0) self._m_values = MifareClassic.Sector.Values(io, self, self._root) io.seek(_pos) return self._m_values if hasattr(self, '_m_values') else None class Manufacturer(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.nuid = self._io.read_u4le() self.bcc = self._io.read_u1() self.sak = self._io.read_u1() self.atqa = self._io.read_u2le() self.manufacturer = self._io.read_bytes(8) class Trailer(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.key_a = MifareClassic.Key(self._io, self, self._root) self._raw_access_bits = self._io.read_bytes(3) _io__raw_access_bits = KaitaiStream(BytesIO(self._raw_access_bits)) self.access_bits = MifareClassic.Trailer.AccessConditions(_io__raw_access_bits, self, self._root) self.user_byte = self._io.read_u1() self.key_b = MifareClassic.Key(self._io, self, self._root) class AccessConditions(KaitaiStruct): def __init__(self, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self._read() def _read(self): self.raw_chunks = [None] * (self._parent.ac_count_of_chunks) for i in range(self._parent.ac_count_of_chunks): self.raw_chunks[i] = self._io.read_bits_int_be(4) class TrailerAc(KaitaiStruct): def __init__(self, ac, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.ac = ac self._read() def _read(self): pass @property def can_read_key_b(self): """key A is required.""" if hasattr(self, '_m_can_read_key_b'): return self._m_can_read_key_b if hasattr(self, '_m_can_read_key_b') else None self._m_can_read_key_b = self.ac.inv_shift_val <= 2 return self._m_can_read_key_b if hasattr(self, '_m_can_read_key_b') else None @property def can_write_keys(self): if hasattr(self, '_m_can_write_keys'): return self._m_can_write_keys if hasattr(self, '_m_can_write_keys') else None self._m_can_write_keys = ((((self.ac.inv_shift_val + 1) % 3) != 0) and (self.ac.inv_shift_val < 6)) return self._m_can_write_keys if hasattr(self, '_m_can_write_keys') else None @property def can_write_access_bits(self): if hasattr(self, '_m_can_write_access_bits'): return self._m_can_write_access_bits if hasattr(self, '_m_can_write_access_bits') else None self._m_can_write_access_bits = self.ac.bits[2].b return self._m_can_write_access_bits if hasattr(self, '_m_can_write_access_bits') else None @property def key_b_controls_write(self): if hasattr(self, '_m_key_b_controls_write'): return self._m_key_b_controls_write if hasattr(self, '_m_key_b_controls_write') else None self._m_key_b_controls_write = not (self.can_read_key_b) return self._m_key_b_controls_write if hasattr(self, '_m_key_b_controls_write') else None class ChunkBitRemap(KaitaiStruct): def __init__(self, bit_no, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.bit_no = bit_no self._read() def _read(self): pass @property def shift_value(self): if hasattr(self, '_m_shift_value'): return self._m_shift_value if hasattr(self, '_m_shift_value') else None self._m_shift_value = (-1 if self.bit_no == 1 else 1) return self._m_shift_value if hasattr(self, '_m_shift_value') else None @property def chunk_no(self): if hasattr(self, '_m_chunk_no'): return self._m_chunk_no if hasattr(self, '_m_chunk_no') else None self._m_chunk_no = (((self.inv_chunk_no + self.shift_value) + self._parent._parent.ac_count_of_chunks) % self._parent._parent.ac_count_of_chunks) return self._m_chunk_no if hasattr(self, '_m_chunk_no') else None @property def inv_chunk_no(self): if hasattr(self, '_m_inv_chunk_no'): return self._m_inv_chunk_no if hasattr(self, '_m_inv_chunk_no') else None self._m_inv_chunk_no = (self.bit_no + self.shift_value) return self._m_inv_chunk_no if hasattr(self, '_m_inv_chunk_no') else None class DataAc(KaitaiStruct): def __init__(self, ac, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.ac = ac self._read() def _read(self): pass @property def read_key_a_required(self): if hasattr(self, '_m_read_key_a_required'): return self._m_read_key_a_required if hasattr(self, '_m_read_key_a_required') else None self._m_read_key_a_required = self.ac.val <= 4 return self._m_read_key_a_required if hasattr(self, '_m_read_key_a_required') else None @property def write_key_b_required(self): if hasattr(self, '_m_write_key_b_required'): return self._m_write_key_b_required if hasattr(self, '_m_write_key_b_required') else None self._m_write_key_b_required = (( ((not (self.read_key_a_required)) or (self.read_key_b_required)) ) and (not (self.ac.bits[0].b))) return self._m_write_key_b_required if hasattr(self, '_m_write_key_b_required') else None @property def write_key_a_required(self): if hasattr(self, '_m_write_key_a_required'): return self._m_write_key_a_required if hasattr(self, '_m_write_key_a_required') else None self._m_write_key_a_required = self.ac.val == 0 return self._m_write_key_a_required if hasattr(self, '_m_write_key_a_required') else None @property def read_key_b_required(self): if hasattr(self, '_m_read_key_b_required'): return self._m_read_key_b_required if hasattr(self, '_m_read_key_b_required') else None self._m_read_key_b_required = self.ac.val <= 6 return self._m_read_key_b_required if hasattr(self, '_m_read_key_b_required') else None @property def decrement_available(self): if hasattr(self, '_m_decrement_available'): return self._m_decrement_available if hasattr(self, '_m_decrement_available') else None self._m_decrement_available = (( ((self.ac.bits[1].b) or (not (self.ac.bits[0].b))) ) and (not (self.ac.bits[2].b))) return self._m_decrement_available if hasattr(self, '_m_decrement_available') else None @property def increment_available(self): if hasattr(self, '_m_increment_available'): return self._m_increment_available if hasattr(self, '_m_increment_available') else None self._m_increment_available = (( ((not (self.ac.bits[0].b)) and (not (self.read_key_a_required)) and (not (self.read_key_b_required))) ) or ( ((not (self.ac.bits[0].b)) and (self.read_key_a_required) and (self.read_key_b_required)) )) return self._m_increment_available if hasattr(self, '_m_increment_available') else None class Ac(KaitaiStruct): def __init__(self, index, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.index = index self._read() def _read(self): pass class AcBit(KaitaiStruct): def __init__(self, i, chunk, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.i = i self.chunk = chunk self._read() def _read(self): pass @property def n(self): if hasattr(self, '_m_n'): return self._m_n if hasattr(self, '_m_n') else None self._m_n = ((self.chunk >> self.i) & 1) return self._m_n if hasattr(self, '_m_n') else None @property def b(self): if hasattr(self, '_m_b'): return self._m_b if hasattr(self, '_m_b') else None self._m_b = self.n == 1 return self._m_b if hasattr(self, '_m_b') else None @property def bits(self): if hasattr(self, '_m_bits'): return self._m_bits if hasattr(self, '_m_bits') else None _pos = self._io.pos() self._io.seek(0) self._m_bits = [None] * (self._parent._parent.ac_bits) for i in range(self._parent._parent.ac_bits): self._m_bits[i] = MifareClassic.Trailer.AccessConditions.Ac.AcBit(self.index, self._parent.chunks[i].chunk, self._io, self, self._root) self._io.seek(_pos) return self._m_bits if hasattr(self, '_m_bits') else None @property def val(self): """c3 c2 c1.""" if hasattr(self, '_m_val'): return self._m_val if hasattr(self, '_m_val') else None self._m_val = (((self.bits[2].n << 2) | (self.bits[1].n << 1)) | self.bits[0].n) return self._m_val if hasattr(self, '_m_val') else None @property def inv_shift_val(self): if hasattr(self, '_m_inv_shift_val'): return self._m_inv_shift_val if hasattr(self, '_m_inv_shift_val') else None self._m_inv_shift_val = (((self.bits[0].n << 2) | (self.bits[1].n << 1)) | self.bits[2].n) return self._m_inv_shift_val if hasattr(self, '_m_inv_shift_val') else None class ValidChunk(KaitaiStruct): def __init__(self, inv_chunk, chunk, _io, _parent=None, _root=None): self._io = _io self._parent = _parent self._root = _root if _root else self self.inv_chunk = inv_chunk self.chunk = chunk self._read() def _read(self): pass @property def valid(self): if hasattr(self, '_m_valid'): return self._m_valid if hasattr(self, '_m_valid') else None self._m_valid = (self.inv_chunk ^ self.chunk) == 15 return self._m_valid if hasattr(self, '_m_valid') else None @property def data_acs(self): if hasattr(self, '_m_data_acs'): return self._m_data_acs if hasattr(self, '_m_data_acs') else None _pos = self._io.pos() self._io.seek(0) self._m_data_acs = [None] * ((self._parent.acs_in_sector - 1)) for i in range((self._parent.acs_in_sector - 1)): self._m_data_acs[i] = MifareClassic.Trailer.AccessConditions.DataAc(self.acs_raw[i], self._io, self, self._root) self._io.seek(_pos) return self._m_data_acs if hasattr(self, '_m_data_acs') else None @property def remaps(self): if hasattr(self, '_m_remaps'): return self._m_remaps if hasattr(self, '_m_remaps') else None _pos = self._io.pos() self._io.seek(0) self._m_remaps = [None] * (self._parent.ac_bits) for i in range(self._parent.ac_bits): self._m_remaps[i] = MifareClassic.Trailer.AccessConditions.ChunkBitRemap(i, self._io, self, self._root) self._io.seek(_pos) return self._m_remaps if hasattr(self, '_m_remaps') else None @property def acs_raw(self): if hasattr(self, '_m_acs_raw'): return self._m_acs_raw if hasattr(self, '_m_acs_raw') else None _pos = self._io.pos() self._io.seek(0) self._m_acs_raw = [None] * (self._parent.acs_in_sector) for i in range(self._parent.acs_in_sector): self._m_acs_raw[i] = MifareClassic.Trailer.AccessConditions.Ac(i, self._io, self, self._root) self._io.seek(_pos) return self._m_acs_raw if hasattr(self, '_m_acs_raw') else None @property def trailer_ac(self): if hasattr(self, '_m_trailer_ac'): return self._m_trailer_ac if hasattr(self, '_m_trailer_ac') else None _pos = self._io.pos() self._io.seek(0) self._m_trailer_ac = MifareClassic.Trailer.AccessConditions.TrailerAc(self.acs_raw[(self._parent.acs_in_sector - 1)], self._io, self, self._root) self._io.seek(_pos) return self._m_trailer_ac if hasattr(self, '_m_trailer_ac') else None @property def chunks(self): if hasattr(self, '_m_chunks'): return self._m_chunks if hasattr(self, '_m_chunks') else None _pos = self._io.pos() self._io.seek(0) self._m_chunks = [None] * (self._parent.ac_bits) for i in range(self._parent.ac_bits): self._m_chunks[i] = MifareClassic.Trailer.AccessConditions.ValidChunk(self.raw_chunks[self.remaps[i].inv_chunk_no], self.raw_chunks[self.remaps[i].chunk_no], self._io, self, self._root) self._io.seek(_pos) return self._m_chunks if hasattr(self, '_m_chunks') else None @property def ac_bits(self): if hasattr(self, '_m_ac_bits'): return self._m_ac_bits if hasattr(self, '_m_ac_bits') else None self._m_ac_bits = 3 return self._m_ac_bits if hasattr(self, '_m_ac_bits') else None @property def acs_in_sector(self): if hasattr(self, '_m_acs_in_sector'): return self._m_acs_in_sector if hasattr(self, '_m_acs_in_sector') else None self._m_acs_in_sector = 4 return self._m_acs_in_sector if hasattr(self, '_m_acs_in_sector') else None @property def ac_count_of_chunks(self): if hasattr(self, '_m_ac_count_of_chunks'): return self._m_ac_count_of_chunks if hasattr(self, '_m_ac_count_of_chunks') else None self._m_ac_count_of_chunks = (self.ac_bits * 2) return self._m_ac_count_of_chunks if hasattr(self, '_m_ac_count_of_chunks') else None

[ "kaitai-bot@kaitai.io" ]

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2021-09-30T18:33:52

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# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- Project information ----------------------------------------------------- project = 's2cholar' copyright = '2021, Luiz Otavio Vilas Boas Oliveira' author = 'Luiz Otavio Vilas Boas Oliveira' # The full version, including alpha/beta/rc tags release = '0.1.0' # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'myst_parser' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'pydata_sphinx_theme' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static']

[ "luiz.vbo@gmail.com" ]

luiz.vbo@gmail.com

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# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy class ConferencesItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() pass

[ "bionikspoon@gmail.com" ]

bionikspoon@gmail.com

160a8c1425366e30469d3987b9b3cc9387782023

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/cosmetics.py

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djoverton/whats-in-your-cosmetics

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refs/heads/master

2021-05-31T11:24:56.003869

2016-06-10T13:16:48

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""" Data source: http://www.healthdata.gov/dataset/chemicals-cosmetics """ import json import operator import matplotlib.pyplot as plt import numpy as np datafile = open("chemicals.json") data = json.load(datafile) #Count the number of times each chemical appears chemcounts = {} for item in data["data"]: if chemcounts.has_key(item[14]): chemcounts[item[14]] += 1 else: chemcounts[item[14]] = 1 #Sort by frequency sortedcounts = sorted(chemcounts.items(), key=operator.itemgetter(1), reverse=True) #Plot 10 most frequently occurring chemicals figure = plt.figure() width = .35 ind = np.arange(10) xs = [i[0] for i in sortedcounts[:10]] ys = [i[1] for i in sortedcounts[:10]] plt.bar(ind, ys, width=width) plt.xticks(ind + width / 2, xs, rotation=90) for i in range(len(xs)): print str(xs[i]) + ": " + str(ys[i]) plt.show() """ Top 10 potentially harmful chemicals found in cosmetics according to the California Safe Cosmetics Program (CSCP) in the California Department of Public Health. http://www.healthdata.gov/dataset/chemicals-cosmetics Titanium dioxide: 63864 Retinol/retinyl esters, when in daily dosages in excess of 10,000 IU, or 3,000 retinol equivalents: 2153 Butylated hydroxyanisole: 1832 Cocamide diethanolamine: 1391 Retinyl palmitate: 1042 "Trade Secret": 727 Vitamin A palmitate: 715 Mica: 512 Silica, crystalline (airborne particles of respirable size): 482 Carbon black: 474 "Worst" product categories, according to number of instances of harmful chemicals reported: Makeup Products (non-permanent): 49459 Nail Products: 9408 Skin Care Products: 5977 Sun-Related Products: 4449 Bath Products: 2324 Hair Coloring Products: 1616 Hair Care Products (non-coloring): 1302 Tattoos and Permanent Makeup: 714 Personal Care Products: 640 Fragrances: 460 "Worst" companies, according to number of instances of harmful chemicals reported: NYX: 3227 bareMinerals: 2412 Sally Hansen: 1774 Sephora: 1771 Victoria's Secret Beauty: 1721 CoverGirl: 1645 NARS: 1537 No7: 1472 CLARINS PARIS: 1401 Rimmel - London: 1362 """

[ "djoverton@gmail.com" ]

djoverton@gmail.com

d7a555b59ad57304e08e0a28c5a8d9d871648a2e

09946a669abcaf8f9b0f22b56b4b036b38f435ef

/setup.py

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permissive

vmishra-uu/pyCEDLAR

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2022-11-09T20:28:13.717403

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import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="pyCEDLAR", version="1.0.0", author="Zsolt Elter", description="pyCEDLAR: Package to estimate Cumulative Effective dose and Lifetime Attributable Risk", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/ezsolti/pyCEDLAR", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], install_requires=[ "numpy", "scipy" ] )

[ "zsolt@phy-draupnir.physics.uu.se" ]

zsolt@phy-draupnir.physics.uu.se

37dcfb2bd2200cc648ab737e317d319edfd9d269

b87f66b13293782321e20c39aebc05defd8d4b48

/maps/build/EnthoughtBase/enthought/logger/agent/attachments.py

4d8f00f577f6d82f2c2ec0b1a7f4b5a14dd94aef

[]

no_license

m-elhussieny/code

5eae020932d935e4d724c2f3d16126a0d42ebf04

5466f5858dbd2f1f082fa0d7417b57c8fb068fad

refs/heads/master

2021-06-13T18:47:08.700053

2016-11-01T05:51:06

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""" Attach relevant project files. FIXME: there are no public project plugins for Envisage 3, yet. In any case, this stuff should not be hard-coded, but extensible via extension points. The code remains here because we can reuse the zip utility code in that extensible rewrite. """ import logging import os.path from email import Encoders from email.MIMEBase import MIMEBase from enthought.traits.api import Any, HasTraits logger = logging.getLogger(__name__) class Attachments(HasTraits): application = Any() message = Any() def __init__(self, message, **traits): traits = traits.copy() traits['message'] = message super(Attachments, self).__init__(**traits) # FIXME: all of the package_*() methods refer to deprecated project plugins. def package_workspace(self): if self.application is None: pass workspace = self.application.get_service('enthought.envisage.project.IWorkspace') if workspace is not None: dir = workspace.path self._attach_directory(dir) return def package_single_project(self): if self.application is None: pass single_project = self.application.get_service('enthought.envisage.single_project.ModelService') if single_project is not None: dir = single_project.location self._attach_directory(dir) def package_any_relevant_files(self): self.package_workspace() self.package_single_project() return def _attach_directory(self, dir): relpath = os.path.basename(dir) import zipfile from cStringIO import StringIO ctype = 'application/octet-stream' maintype, subtype = ctype.split('/', 1) msg = MIMEBase(maintype, subtype) file_object = StringIO() zip = zipfile.ZipFile(file_object, 'w') _append_to_zip_archive(zip, dir, relpath) zip.close() msg.set_payload(file_object.getvalue()) Encoders.encode_base64(msg) # Encode the payload using Base64 msg.add_header('Content-Disposition', 'attachment', filename='project.zip') self.message.attach(msg) file_object.close() def _append_to_zip_archive(zip, dir, relpath): """ Add all files in and below directory dir into zip archive""" for filename in os.listdir(dir): path = os.path.join(dir, filename) if os.path.isfile(path): name = os.path.join(relpath, filename) zip.write(path, name) logger.debug('adding %s to error report' % path) else: if filename != ".svn": # skip svn files if any subdir = os.path.join(dir, filename) _append_to_zip_archive(zip, subdir, os.path.join(relpath, filename)) return

[ "fspaolo@gmail.com" ]

fspaolo@gmail.com

f6045942d8ce16ba7028a713122f43871d0a5830

71c3c9c7732c7662b2af9d533ababd21d1ef430b

/src/sVM.py

5a1b5c87aa48b71266a76cb3068090082a917067

[]

no_license

mirzaelahi/MachineLearningGenericInterface

272c30606447655300509314961e634d20ed7b5f

bf09da8a595617a422a5666ac702d7b804662260

refs/heads/master

2021-08-20T03:26:30.298136

2017-11-28T04:22:02

110,456,752

0

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#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Nov 10 09:05:26 2017 @author: Mirza Elahi """ from predictor import predictor, scoring, algo from sklearn import svm import logging import numpy as np class sVM( predictor ): def __init__(self, loggingLevel = logging.INFO, enableLoggingTime = False): # kNN class constructor super(sVM, self).__init__(loggingLevel, enableLoggingTime) self.kernel = 'linear' self.C=1 self.gamma='auto' self.max_iter = 50 # sweeping for best method with cross validation self.kernelSweep = ['linear', 'poly', 'rbf'] self.CSweep = [1, 100, 1000] self.gammaSweep = ['auto', 10, 100] def toString(self): """ Print parameters of current model """ pStr = "Current model:\n\tSVM model with \n\t\tkernel = %s\n\t\tC = %d\n\t\tgamma = %s\n" \ % (self.kernel, self.C, str(self.gamma)) return pStr def getModel(self, kernel=None, C=None, gamma=None): """ Temporary model generation """ if kernel is not None: self.kernel = kernel if C is not None: self.C = C if gamma is not None: self.gamma = gamma pModel = svm.SVC(kernel=self.kernel, C=self.C, gamma=self.gamma, max_iter=self.max_iter) return pModel def loadModel(self, kernel=None, C=None, gamma=None): """ load internal model """ if kernel is not None: self.kernel = kernel if C is not None: self.C = C if gamma is not None: self.gamma = gamma self.model = [] self.model = self.getModel(kernel=self.kernel, C=self.C, gamma=self.gamma) def makeSweepingList(self, kernelSweep=None, CSweep=None, gammaSweep=None): """ making a list with all combinations of sweeping parameters """ if kernelSweep is not None: self.kernelSweep = kernelSweep if CSweep is not None: self.CSweep = CSweep if gammaSweep is not None: self.gammaSweep = gammaSweep self.sweepingList = [[i, j, k] for i in self.kernelSweep \ for j in self.CSweep for k in self.gammaSweep] return self.sweepingList def loadParametersFromList(self, params=['linear', 1, 'auto']): """ override model parameters for the object from params """ self.kernel = params[0] self.C = params[1] self.gamma = params[2] def doubleCrossValidate(self, pfeatures, pClass, nFoldOuter=5, nFoldInner=4, fileName=None, pModel=None, scoring=scoring.ACCURACY, isStratified=False): """function for cross validation """ # if model is given, override with internal model if pModel is not None: self.model = pModel bestParamList=[] ValScoreList=[] ValScoreStdList = [] TestScoreList = [] TestConfList = [] self.makeSweepingList(self.kernelSweep, self.CSweep, self.gammaSweep) # indexes for train and test pKF = self.getKFold(pfeatures, nFold=nFoldOuter, isStratified=isStratified) foldNo = 1 print( 'Double cross validation with fold %d started ...\n' %(nFoldOuter) ) OuterInnerFoldData = [] # folds loop for train_index, test_index in pKF.split( pfeatures, pClass ): pFeatureTrain = pfeatures[train_index] pFeatureTest = pfeatures[test_index] pClassTrain= pClass[train_index] pClassTest= pClass[test_index] bestScoreMean = -1E5 eachInnerFoldData = [] # param sweeping list loop for params in self.sweepingList: # loading parameters from sweeping list self.loadParametersFromList( params=params ) # loading new model with definite parameters self.loadModel() score, \ accuracy, \ conf, \ mccs = self.mySingleCrossValidate( pFeatureTrain, pClassTrain, scoring=scoring, nFold=nFoldInner, isStratified=isStratified) scoreMean = score.mean() scoreStd = score.std() #print params print scoreMean if scoreMean > bestScoreMean: bestScoreMean = scoreMean bestScoreStd = scoreStd bestParams = params #bestModel = self.model self.saveModel(fileName='best_svm') eachInnerFoldData.append( [score, accuracy, mccs, conf] ) OuterInnerFoldData.append(eachInnerFoldData) # loading best model through inner cross validation # model in 'best_svm' self.loadSavedModel(fileName='best_svm') self.trainModel( pFeatureTrain , pClassTrain) #print(self.model) # test model classPred = self.testModel(pFeatureTest) #metrices testScore, testaccuracy, avgPrecScore, matConf, matCohenKappa, \ strClassificationReport, mcc = self.getMetrics( classTest = pClassTest, classPred = classPred, scoring=scoring, boolPrint = False) printstr1 = "Best model for fold #%d is kernel=%s, C=%d, gamma=%s with \n\t" \ % ( foldNo, bestParams[0], bestParams[1], str(bestParams[2]) ) printstr2 = "Val. Score %0.5f\n\t" % ( bestScoreMean ) printstr3 = "Test Score. %0.5f\n" % ( testScore ) print printstr1 + printstr2 + printstr3 ValScoreList.append(bestScoreMean) ValScoreStdList.append(bestScoreStd) TestScoreList.append(testScore) TestConfList.append(matConf) bestParamList.append(bestParams) foldNo += 1 if fileName is not None: # OuterInnerFoldData # [OuterFoldNo][ParamListIndex][Score, Accu, MCC, Conf][InnerFoldNo] self.saveDoubleCrossValidData( fileName=fileName, ValScoreList = ValScoreList, ValScoreStdList = ValScoreStdList, TestScoreList = TestScoreList, TestConfList = TestConfList, bestParamList = bestParamList, OuterInnerFoldData= OuterInnerFoldData, sweepingList = self.sweepingList, OuterFoldNo = nFoldOuter, InnerFoldNo = nFoldInner, scoring = scoring, algorithm = algo.SVM ) return np.array(ValScoreList), np.array(ValScoreStdList), \ np.array(TestScoreList), bestParamList, OuterInnerFoldData

[ "me5vp@virginia.edu" ]

me5vp@virginia.edu

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76e0e98a3b582763a83291350291579c5906821d

/examples/settings.py

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permissive

sehmon/python-client

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ccc641982d65b1ab217985e027a4a3e0f5557d75

refs/heads/master

2021-05-28T04:23:31.414595

2014-09-25T06:46:06

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py

API_KEY = 'Your API key here'

[ "pkernoobie@gmail.com" ]

pkernoobie@gmail.com

991d807c70d59f473ad69db316ee9c131b22f5ab

bea1eea53462b00d948e17d4f916986d9b217df9

/python/leetcode/p295.py

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[]

no_license

aloklal99/naukari

18a64f5573e7e4929d57258ec208c82a5988f467

4ed2d3d7a05890e1d39621465e57bc429ccde19b

refs/heads/master

2020-03-29T17:59:08.867062

2019-03-17T06:30:03

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import operator import collections class Heap: def __init__(self): self._store = [None] # index 0 is not used def __len__(self): return len(self._store) - 1 # 0th element isn't used def _isCorrectlyOrdered(self, parent, child): raise Exception("Child class must override this method!") def _pickSwappableChild(self, childrenTs): raise Exception("Child class must override this method!") def peekRoot(self): return self._store[1] def _swap(self, idx1, idx2): self._store[idx1], self._store[idx2] = self._store[idx2], self._store[idx1] def _getParent(self, child): return int(child/2) def _bubbleUp(self, child): parent = self._getParent(child) # last child is at @2 so we want to stop looking for parent when child is at 1 (head) while child > 1 and (not self._isCorrectlyOrdered(parent, child)): self._swap(parent, child) child = parent parent = self._getParent(child) def _percolateDown(self, parent): print(f"_percolateDown(parent={parent})") done = False while not done: children = self._getSwappableChild(parent) child = self._pickSwappableChild(children) print(f"_percolateDown \t child: {child}") if child: self._swap(parent, child) parent = child else: done = True def add(self, val): self._store.append(val) self._bubbleUp(len(self._store) - 1) def removeTop(self): if len(self._store) == 2: return self._store.pop() else: top = self._store[1] last = self._store.pop() self._store[1] = last self._percolateDown(1) return top def _getChildren(self, parent): return [2*parent+i for i in [0, 1] if 2*parent+i < len(self._store)] def _getSwappableChild(self, parent): return (child for child in self._getChildren(parent) if not self._isCorrectlyOrdered(parent, child)) class MinHeap(Heap): def _isCorrectlyOrdered(self, parent, child): return False if self._store[parent] > self._store[child] else True def _pickSwappableChild(self, children): return min(children, key=lambda x: self._store[x], default=None) class MaxHeap(Heap): def _isCorrectlyOrdered(self, parent, child): return False if self._store[parent] < self._store[child] else True def _pickSwappableChild(self, children): return max(children, key=lambda x: self._store[x], default=None) class MedianFinder0: def __init__(self): """ initialize your data structure here. """ self.rightHeap = MinHeap() self.leftHeap = MaxHeap() def addNum(self, num): """ :type num: int :rtype: void """ if len(self.leftHeap) and len(self.rightHeap): if num >= self.rightHeap.peekRoot(): if len(self.rightHeap) <= len(self.leftHeap): self.rightHeap.add(num) else: # right heap is already larger self.leftHeap.add(self.rightHeap.removeTop()) self.rightHeap.add(num) else: if len(self.leftHeap) <= len(self.rightHeap): self.leftHeap.add(num) else: # left heap is already larger if num > self.leftHeap.peekRoot(): self.rightHeap.add(num) else: self.rightHeap.add(self.leftHeap.removeTop()) self.leftHeap.add(num) elif len(self.leftHeap): if num > self.leftHeap.peekRoot(): self.rightHeap.add(num) else: self.rightHeap.add(self.leftHeap.removeTop()) self.leftHeap.add(num) elif len(self.rightHeap) > 0: if num <= self.rightHeap.peekRoot(): self.leftHeap.add(num) else: self.leftHeap.add(self.rightHeap.removeTop()) self.rightHeap.add(num) else: # both heaps are empty! self.leftHeap.add(num) def findMedian(self): """ :rtype: float """ leftL = len(self.leftHeap) rightL = len(self.rightHeap) if leftL == rightL: if leftL: return (self.rightHeap.peekRoot() + self.leftHeap.peekRoot())/2 else: # both left and right heaps are empty! return None elif leftL > rightL: return self.leftHeap.peekRoot() else: return self.rightHeap.peekRoot() # Your MedianFinder object will be instantiated and called as such: # obj = MedianFinder() # obj.addNum(num) # param_2 = obj.findMedian()

[ "allal@ebay.com" ]

allal@ebay.com

ecbe1ae9755a38424f26c42c80934e9910dfb4db

ad8a4a257d3a261a7f7a67a69418406bfb2cc7ab

/common-scrapers/common_src/scrapers/among_us_scraper.py

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[ "MIT" ]

permissive

mrPaintMan/blog-scraper

b832b72b5e0d35391055d9f2358b754629085475

9b1ff3d398bd23d799d86c9a62ec76a6950555cc

refs/heads/develop

2022-02-04T13:04:12.209678

2021-02-15T19:27:12

222,109,337

0

1

MIT

2020-12-29T08:25:58

2019-11-16T14:14:51

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from common_src.lib.model.post import Post from common_src.lib.model.source import Source from common_src.scrapers.abstract_scraper import make_soup, now, remove_dups SOURCE_CODE = "among_us" WEBSITE = "https://innersloth.itch.io/among-us/devlog" ALT_IMAGE = 'https://img.itch.zone/aW1nLzE3MzAzNTQucG5n/original/6ZlfCk.png' FILENAME = "../resources/data/among_us.txt" def get_source(): name = "Among Us" description = 'The booming murder multiplayer game everyone is talking about!' profile_image = 'https://img.itch.zone/aW1hZ2UyL3VzZXIvMTg5NzU5LzE3MzAzNTcucG5n/original/7quYQx.png' return Source(SOURCE_CODE, name, description, profile_image, ALT_IMAGE, None) def scrape(): soup = make_soup(WEBSITE) data = [] for post in soup.find("ul", {"class": "blog_post_list_widget"}): date = post.find("abbr").get("title").replace("-", "").replace(" ", "").replace(":", "")[0:-2] title = post.find("a", {"class": "title"}).text.strip() link = post.find("a", {"class": "title"}).get("href") alt_image = ALT_IMAGE image_element = post.find("img", {"class": "post_image"}) image = image_element.get("src").replace(" ", "%20") if image_element else ALT_IMAGE data.append(Post(None, date, title, link, image, alt_image, SOURCE_CODE, None)) if len(data) % 25 == 0: print(now() + f"Processed {len(data)} posts") return remove_dups(data)

[ "fille.palmqvist@icloud.com" ]

fille.palmqvist@icloud.com

2e89a9bd74c09e3531412e11b310be4b94ae18d1

2a39fe8bd203531c9bcdb470d19b80beac665eae

/model_cluster.py

288cf9409b0694d16f6334c5ea877ffeafd2e726

[]

no_license

davidharvey1986/lenstoolTools

7bf11af1a38700503a731c6fe7e83fdc92bf58c1

85bcf729603d34341f5f41c57c4e233b08055baa

refs/heads/master

2021-09-08T14:29:52.695461

2018-03-10T13:54:50

124,657,727

0

null

UTF-8

Python

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3,715

py

''' This script has 2 functions: 1. model_cluster( ra, dec, cluster, \ halos=None, \ best_file=None) This models the input cluster and returns a structure from simulate_project with shear, chi, etc. ''' import numpy as np import ipdb as pdb import astro_tools as at import idlsave as idlsave import lensing as l import copy as copy import glob as glob import os def model_cluster( ra, dec, cluster, \ halos=None, \ best_file=None): ''' Model the NFW signal of the cluster using the input from halos ''' if best_file is None: dataDir = '/Users/DavidHarvey/Documents/Work/Trails/data/rerun/'+cluster best_file = dataDir+'/best.par' runmode, potentials = l.lenstool.read_best( filename=best_file) space = l.simulations.templates.space() space.lens[0].del_profile('isothermal') space.source[0].ell_disp = 0. space.source[0].ra = ra space.source[0].dec = dec space.telescope.nGalaxies = len(dec) space.lens[0].redshift = potentials[0]['z_lens']['float'] space.source[0].redshift = 1.0 space.lens[0].ra = potentials[0]['ra']['float'] space.lens[0].dec = potentials[0]['dec']['float'] if halos is not None: space.lens[0].ra = halos['halos'][0]['gal']['ra'][0] space.lens[0].dec = halos['halos'][0]['gal']['dec'][0] space.lens[0].profiles['nfw'].args['mass'] = \ potentials[0]['m200']['str'].astype(np.double) space.lens[0].profiles['nfw'].args['conc'] = \ potentials[0]['concentration']['float'] space.lens[0].profiles['nfw'].args['ellipticity'] = \ potentials[0]['ellipticite']['float'] space.lens[0].profiles['nfw'].args['potential_angle'] = \ potentials[0]['angle_pos']['float'] scale_radius = l.profiles.nfw.scale_radius(space.lens[0].profiles['nfw'].args['mass'], \ space.lens[0].profiles['nfw'].args['conc'],\ potentials[0]['z_lens']['float']) space.lens[0].profiles['nfw'].args['scale_radius'] = scale_radius for iHalo in range(1,len(potentials)): space.add_lens() space.lens[iHalo].redshift = potentials[0]['z_lens']['float'] space.source[iHalo].redshift = 1.0 space.lens[iHalo].ra = potentials[iHalo]['ra']['float'] space.lens[iHalo].dec = potentials[iHalo]['dec']['float'] if halos is not None: space.lens[iHalo].ra = halos['halos'][iHalo]['gal']['ra'][0] space.lens[iHalo].dec = halos['halos'][iHalo]['gal']['dec'][0] space.lens[iHalo].profiles['nfw'].args['mass'] = \ potentials[iHalo]['m200']['str'].astype(np.double) space.lens[iHalo].profiles['nfw'].args['conc'] = \ potentials[iHalo]['concentration']['float'] space.lens[iHalo].profiles['nfw'].args['ellipticity'] = \ potentials[iHalo]['ellipticite']['float'] space.lens[iHalo].profiles['nfw'].args['potential_angle'] = \ potentials[iHalo]['angle_pos']['float'] scale_radius = l.profiles.nfw.scale_radius(space.lens[iHalo].profiles['nfw'].args['mass'], \ space.lens[iHalo].profiles['nfw'].args['conc'],\ potentials[iHalo]['z_lens']['float']) space.lens[iHalo].profiles['nfw'].args['scale_radius'] = scale_radius space.reload(positions=False) space.weak_lensing() return space

[ "davidharvey1986@googlemail.com" ]

davidharvey1986@googlemail.com

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/2-2_lists.py

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[]

no_license

ckoder999/Diving_into_Python

d46b9ca93075c77fd5efd1b6e6c76417659f050d

abc452d6dfaa76d3515e21b71f666171d8a5f440

refs/heads/master

2020-12-09T20:27:58.681763

2020-02-05T13:22:24

233,410,671

0

null

UTF-8

Python

false

268

py

import random numbers = [] for _ in range(10): numbers.append(random.randint(1, 20)) print(numbers) print(sorted(numbers, reverse=True)) numbers.sort(reverse=False) #print(list(reversed(numbers))) num2 = numbers.copy() print(num2) print(numbers.count(10))

[ "eugene@iMac-Eugene.local" ]

eugene@iMac-Eugene.local

cd1062321fd6e56a634e4e079cbae30019bcd44c

4619167ad190cce846812c957e279825153b243f

/singleton.py

2300b5f16db388d854417496fa0b91b51b69126c

[]

no_license

dimasert/test-designpatterns

bbdc3e006d609f50794b8b3f7bf2491ef4554d33

8bc9e6ec93437c06d0685ad58c523535c790e5d2

refs/heads/master

2022-11-22T23:51:00.437605

2020-07-22T03:30:33

281,531,123

0

null

UTF-8

Python

false

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class configValues: __instance = None @staticmethod def getInstance(): if configValues.__instance == None: configValues() return configValues.__instance def __init__(self): """ Virtually private constructor """ if configValues.__instance != None: raise Exception ("this class is a singleton") else: configValues.__instance = self s = configValues.getInstance() print(s) s = configValues.getInstance() print(s)

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import matplotlib.pyplot as plt import pandas as pd import numpy as np def kernel(point, xmat, k): m, n = np.shape(xmat) weights = np.mat(np.eye((m))) for j in range(m): diff = point - X[j] weights[j, j] = np.exp(diff * diff.T / (-2.0 * k ** 2)) return weights def localWeight(point, xmat, ymat, k): wei = kernel(point, xmat, k) W = (X.T * (wei * X)).I * (X.T * (wei * ymat.T)) return W def localWeightRegression(xmat, ymat, k): m, n = np.shape(xmat) ypred = np.zeros(m) for i in range(m): ypred[i] = xmat[i] * localWeight(xmat[i], xmat, ymat, k) return ypred # load data points data = pd.read_csv('10-dataset.csv') bill = np.array(data.total_bill) tip = np.array(data.tip) # preparing and add 1 in bill mbill = np.mat(bill) mtip = np.mat(tip) m = np.shape(mbill)[1] one = np.mat(np.ones(m)) X = np.hstack((one.T, mbill.T)) # set k here ypred = localWeightRegression(X, mtip, 0.5) SortIndex = X[:, 1].argsort(0) xsort = X[SortIndex][:, 0] fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.scatter(bill, tip, color='green') ax.plot(xsort[:, 1], ypred[SortIndex], color='red', linewidth=5) plt.xlabel('Total bill') plt.ylabel('Tip') plt.show();

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# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-09-10 05:19 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Entry', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=128)), ('img', models.CharField(max_length=256)), ('desc', models.TextField(max_length=512)), ('link', models.URLField()), ('tag', models.CharField(max_length=20)), ], ), ]

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import sys import csv from utility.loadDataSet import transpose if __name__ == '__main__': filename = sys.argv[1] with open(filename, 'r') as file_o: file_r = csv.reader(file_o, delimiter="\t") rows = [row for row in file_r] rows = transpose(rows) rows = rows[:-1] rows = transpose(rows) with open(filename+"2.csv", 'w') as file_w: writer_csv = csv.writer(file_w, delimiter='\t') for row in rows: writer_csv.writerow(row)

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#! /usr/bin/python #!-*- coding: utf-8 -*- import RPi.GPIO as gpio import time import pygame, sys import pygame.camera import time WEBCAM_DIR = "/home/pi/projeto/foto_webcam" pygame.init() pygame.camera.init() cam = pygame.camera.Camera("/dev/video0", (640,480)) gpio.setmode(gpio.BCM) gpio.setup(17, gpio.IN, pull_up_down = gpio.PUD_DOWN) while True: if(gpio.input(17) == 0): ("Botão desligado") else: print("Botão pressionado") cam.start() image = cam.get_image() cam.stop timestamp = time.strftime("%d-%m-%Y_%H-%M-%S", time.localtime()) filename = "%s/%s.jpg" % (WEBCAM_DIR, timestamp) # salvando a imagem pygame.image.save(image, filename) print "Salvo" time.sleep(1) gpio.cleanup() exit()

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"""hello URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from first import views urlpatterns = [ path('admin/', admin.site.urls), path('articles/<int:year>/', views.year_archive) ]

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#coding=utf8 import os import itchat from NetEaseMusicApi import interact_select_song HELP_MSG = u'''\ 欢迎使用微信网易云音乐帮助：显示帮助关闭：关闭歌曲歌名：按照引导播放音乐\ ''' with open('stop.mp3', 'w') as f: pass def close_music(): os.startfile('stop.mp3') @itchat.msg_register(itchat.content.TEXT) def music_player(msg): if msg['ToUserName'] != 'filehelper': return if msg['Text'] == u'关闭': close_music() itchat.send(u'音乐已关闭', 'filehelper') if msg['Text'] == u'帮助': itchat.send(HELP_MSG, 'filehelper') else: itchat.send(interact_select_song(msg['Text']), 'filehelper') itchat.auto_login(True, enableCmdQR=True) itchat.send(HELP_MSG, 'filehelper') itchat.run()

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import pandas as pd import numpy as np import matplotlib.pyplot as plt import math x=np.arange(0,30.1,0.1) s=np.sin(x) c=np.cos(x) plt.plot(x,s,'-r',label='sin(x)') plt.plot(x,c,'--b',label='cos(x)') plt.title("sin(x) i cos(x) dla x[0,30] z krokiem 0.1") plt.xlabel('x') plt.ylabel('sin(x) i cos(x)') plt.xticks(np.arange(0,31)) plt.legend() plt.show()

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#!/usr/bin/env python #-*- coding: utf-8 -*- # # pe007.py - Project Euler # prime_array = [] sum_of_array = len(prime_array) number = 1 while (len(prime_array) < 10001) : is_prime = True number += 1 if sum_of_array == 0: if number != 1: prime_array.append(number) else: for i in prime_array : if not number % i: is_prime = False break if is_prime: prime_array.append(number) sum_of_array = len(prime_array) print prime_array[len(prime_array)-1]

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#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. import dataclasses from dataclasses import dataclass from typing import Any, Dict, List, Optional, Type, TypeVar, Union, cast import numpy as np import torch @dataclass class BaseDataClass: def _replace(self, **kwargs): return cast(type(self), dataclasses.replace(self, **kwargs)) def pin_memory(self): pinned_memory = {} for field in dataclasses.fields(self): f = getattr(self, field.name) if isinstance(f, (torch.Tensor, BaseDataClass)): pinned_memory[field.name] = f.pin_memory() return self._replace(**pinned_memory) def cuda(self): cuda_tensor = {} for field in dataclasses.fields(self): f = getattr(self, field.name) if isinstance(f, torch.Tensor): cuda_tensor[field.name] = f.cuda(non_blocking=True) elif isinstance(f, BaseDataClass): cuda_tensor[field.name] = f.cuda() return self._replace(**cuda_tensor) @dataclass class ValuePresence(BaseDataClass): value: torch.Tensor presence: Optional[torch.ByteTensor] @dataclass class IdFeatureConfig(BaseDataClass): """ This describes how to map raw features to model features """ feature_id: int # integer feature ID id_mapping_name: str # key to ModelPreprocessingConfig.id_mapping_config @dataclass class IdFeatureBase(BaseDataClass): """ User should subclass this class and define each ID feature as a field w/ torch.Tensor as the type of the field. """ @classmethod # TODO: This should be marked as abstractmethod but mypi doesn't like it. # See https://github.com/python/mypy/issues/5374 # @abc.abstractmethod def get_feature_config(cls) -> Dict[str, IdFeatureConfig]: """ Returns mapping from feature name, which must be a field in this dataclass, to feature config. """ raise NotImplementedError T = TypeVar("T", bound="SequenceFeatureBase") @dataclass class FloatFeatureInfo(BaseDataClass): name: str feature_id: int @dataclass class SequenceFeatureBase(BaseDataClass): id_features: Optional[IdFeatureBase] float_features: Optional[ValuePresence] @classmethod # TODO: This should be marked as abstractmethod but mypi doesn't like it. # See https://github.com/python/mypy/issues/5374 # @abc.abstractmethod def get_max_length(cls) -> int: """ Subclass should return the max-length of this sequence. If the raw data is longer, feature extractor will truncate the front. If the raw data is shorter, feature extractor will fill the front with zero. """ raise NotImplementedError @classmethod def get_float_feature_infos(cls) -> List[FloatFeatureInfo]: """ Override this if the sequence has float features associated to it. Float features should be stored as ID-score-list, where the ID part corresponds to primary entity ID of the sequence. E.g., if this is a sequence of previously watched videos, then the key should be video ID. """ return [] @classmethod def prototype(cls: Type[T]) -> T: float_feature_infos = cls.get_float_feature_infos() float_features = ( torch.rand(1, cls.get_max_length(), len(float_feature_infos)) if float_feature_infos else None ) fields = dataclasses.fields(cls) id_features = None for field in fields: if field.name != "id_features" or not isinstance(field.type, type): continue id_feature_fields = dataclasses.fields(field.type) id_features = field.type( # noqa **{ f.name: torch.randint(1, (1, cls.get_max_length())) for f in id_feature_fields } ) break return cls(id_features=id_features, float_features=float_features) U = TypeVar("U", bound="SequenceFeatures") @dataclass class SequenceFeatures(BaseDataClass): """ A stub-class for sequence features in the model. All fileds should be subclass of SequenceFeatureBase above. """ @classmethod def prototype(cls: Type[U]) -> U: fields = dataclasses.fields(cls) return cls(**{f.name: f.type.prototype() for f in fields}) # type: ignore @dataclass class IdMapping(BaseDataClass): ids: List[int] @dataclass class ModelFeatureConfig(BaseDataClass): float_feature_infos: List[FloatFeatureInfo] id_mapping_config: Dict[str, IdMapping] sequence_features_type: Optional[Type[SequenceFeatures]] @dataclass class FeatureVector(BaseDataClass): float_features: ValuePresence # sequence_features should ideally be Mapping[str, IdListFeature]; however, # that doesn't work well with ONNX. # User is expected to dynamically define the type of id_list_features based # on the actual features used in the model. sequence_features: Optional[SequenceFeatureBase] = None # Experimental: sticking this here instead of putting it in float_features # because a lot of places derive the shape of float_features from # normalization parameters. time_since_first: Optional[torch.Tensor] = None @dataclass class ActorOutput(BaseDataClass): action: torch.Tensor log_prob: Optional[torch.Tensor] = None @dataclass class PreprocessedFeatureVector(BaseDataClass): float_features: torch.Tensor # Experimental: sticking this here instead of putting it in float_features # because a lot of places derive the shape of float_features from # normalization parameters. time_since_first: Optional[torch.Tensor] = None @dataclass class PreprocessedState(BaseDataClass): """ This class makes it easier to plug modules into predictor """ state: PreprocessedFeatureVector @classmethod def from_tensor(cls, state: torch.Tensor): assert isinstance(state, torch.Tensor) return cls(state=PreprocessedFeatureVector(float_features=state)) def __init__(self, state): super().__init__() if isinstance(state, torch.Tensor): raise ValueError("Use from_tensor()") self.state = state @dataclass class PreprocessedStateAction(BaseDataClass): state: PreprocessedFeatureVector action: PreprocessedFeatureVector @classmethod def from_tensors(cls, state: torch.Tensor, action: torch.Tensor): assert isinstance(state, torch.Tensor) assert isinstance(action, torch.Tensor) return cls( state=PreprocessedFeatureVector(float_features=state), action=PreprocessedFeatureVector(float_features=action), ) def __init__(self, state, action): super().__init__() if isinstance(state, torch.Tensor) or isinstance(action, torch.Tensor): raise ValueError(f"Use from_tensors() {type(state)} {type(action)}") self.state = state self.action = action @dataclass class RawStateAction(BaseDataClass): state: FeatureVector action: FeatureVector @dataclass class CommonInput(BaseDataClass): """ Base class for all inputs, both raw and preprocessed """ reward: torch.Tensor time_diff: torch.Tensor step: Optional[torch.Tensor] not_terminal: torch.Tensor @dataclass class PreprocessedBaseInput(CommonInput): state: PreprocessedFeatureVector next_state: PreprocessedFeatureVector @dataclass class PreprocessedDiscreteDqnInput(PreprocessedBaseInput): action: torch.Tensor next_action: torch.Tensor possible_actions_mask: torch.Tensor possible_next_actions_mask: torch.Tensor @dataclass class PreprocessedParametricDqnInput(PreprocessedBaseInput): action: PreprocessedFeatureVector next_action: PreprocessedFeatureVector possible_actions: PreprocessedFeatureVector possible_actions_mask: torch.ByteTensor possible_next_actions: PreprocessedFeatureVector possible_next_actions_mask: torch.ByteTensor tiled_next_state: PreprocessedFeatureVector @dataclass class PreprocessedPolicyNetworkInput(PreprocessedBaseInput): action: PreprocessedFeatureVector next_action: PreprocessedFeatureVector @dataclass class PreprocessedMemoryNetworkInput(PreprocessedBaseInput): action: Union[torch.Tensor, torch.Tensor] @dataclass class RawBaseInput(CommonInput): state: FeatureVector next_state: FeatureVector @dataclass class RawDiscreteDqnInput(RawBaseInput): action: torch.ByteTensor next_action: torch.ByteTensor possible_actions_mask: torch.ByteTensor possible_next_actions_mask: torch.ByteTensor def preprocess( self, state: PreprocessedFeatureVector, next_state: PreprocessedFeatureVector ): assert isinstance(state, PreprocessedFeatureVector) assert isinstance(next_state, PreprocessedFeatureVector) return PreprocessedDiscreteDqnInput( self.reward, self.time_diff, self.step, self.not_terminal.float(), state, next_state, self.action.float(), self.next_action.float(), self.possible_actions_mask.float(), self.possible_next_actions_mask.float(), ) def preprocess_tensors(self, state: torch.Tensor, next_state: torch.Tensor): assert isinstance(state, torch.Tensor) assert isinstance(next_state, torch.Tensor) return PreprocessedDiscreteDqnInput( self.reward, self.time_diff, self.step, self.not_terminal.float(), PreprocessedFeatureVector(float_features=state), PreprocessedFeatureVector(float_features=next_state), self.action.float(), self.next_action.float(), self.possible_actions_mask.float(), self.possible_next_actions_mask.float(), ) @dataclass class RawParametricDqnInput(RawBaseInput): action: FeatureVector next_action: FeatureVector possible_actions: FeatureVector possible_actions_mask: torch.ByteTensor possible_next_actions: FeatureVector possible_next_actions_mask: torch.ByteTensor tiled_next_state: FeatureVector def preprocess( self, state: PreprocessedFeatureVector, next_state: PreprocessedFeatureVector, action: PreprocessedFeatureVector, next_action: PreprocessedFeatureVector, possible_actions: PreprocessedFeatureVector, possible_next_actions: PreprocessedFeatureVector, tiled_next_state: PreprocessedFeatureVector, ): assert isinstance(state, PreprocessedFeatureVector) assert isinstance(next_state, PreprocessedFeatureVector) assert isinstance(action, PreprocessedFeatureVector) assert isinstance(next_action, PreprocessedFeatureVector) assert isinstance(possible_actions, PreprocessedFeatureVector) assert isinstance(possible_next_actions, PreprocessedFeatureVector) assert isinstance(tiled_next_state, PreprocessedFeatureVector) return PreprocessedParametricDqnInput( self.reward, self.time_diff, self.step, self.not_terminal, state, next_state, action, next_action, possible_actions, self.possible_actions_mask, possible_next_actions, self.possible_next_actions_mask, tiled_next_state, ) def preprocess_tensors( self, state: torch.Tensor, next_state: torch.Tensor, action: torch.Tensor, next_action: torch.Tensor, possible_actions: torch.Tensor, possible_next_actions: torch.Tensor, tiled_next_state: torch.Tensor, ): assert isinstance(state, torch.Tensor) assert isinstance(next_state, torch.Tensor) assert isinstance(action, torch.Tensor) assert isinstance(next_action, torch.Tensor) assert isinstance(possible_actions, torch.Tensor) assert isinstance(possible_next_actions, torch.Tensor) assert isinstance(tiled_next_state, torch.Tensor) return PreprocessedParametricDqnInput( self.reward, self.time_diff, self.step, self.not_terminal, PreprocessedFeatureVector(float_features=state), PreprocessedFeatureVector(float_features=next_state), PreprocessedFeatureVector(float_features=action), PreprocessedFeatureVector(float_features=next_action), PreprocessedFeatureVector(float_features=possible_actions), self.possible_actions_mask, PreprocessedFeatureVector(float_features=possible_next_actions), self.possible_next_actions_mask, PreprocessedFeatureVector(float_features=tiled_next_state), ) @dataclass class RawPolicyNetworkInput(RawBaseInput): action: FeatureVector next_action: FeatureVector def preprocess( self, state: PreprocessedFeatureVector, next_state: PreprocessedFeatureVector, action: PreprocessedFeatureVector, next_action: PreprocessedFeatureVector, ): assert isinstance(state, PreprocessedFeatureVector) assert isinstance(next_state, PreprocessedFeatureVector) assert isinstance(action, PreprocessedFeatureVector) assert isinstance(next_action, PreprocessedFeatureVector) return PreprocessedPolicyNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, state, next_state, action, next_action, ) def preprocess_tensors( self, state: torch.Tensor, next_state: torch.Tensor, action: torch.Tensor, next_action: torch.Tensor, ): assert isinstance(state, torch.Tensor) assert isinstance(next_state, torch.Tensor) assert isinstance(action, torch.Tensor) assert isinstance(next_action, torch.Tensor) return PreprocessedPolicyNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, PreprocessedFeatureVector(float_features=state), PreprocessedFeatureVector(float_features=next_state), PreprocessedFeatureVector(float_features=action), PreprocessedFeatureVector(float_features=next_action), ) @dataclass class RawMemoryNetworkInput(RawBaseInput): action: Union[FeatureVector, torch.ByteTensor] def preprocess( self, state: PreprocessedFeatureVector, next_state: PreprocessedFeatureVector, action: Optional[torch.Tensor] = None, ): assert isinstance(state, PreprocessedFeatureVector) assert isinstance(next_state, PreprocessedFeatureVector) if action is not None: assert isinstance(action, torch.Tensor) return PreprocessedMemoryNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, state, next_state, action, ) else: assert isinstance(self.action, torch.ByteTensor) return PreprocessedMemoryNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, state, next_state, self.action.float(), ) def preprocess_tensors( self, state: torch.Tensor, next_state: torch.Tensor, action: Optional[torch.Tensor] = None, ): assert isinstance(state, torch.Tensor) assert isinstance(next_state, torch.Tensor) if action is not None: assert isinstance(action, torch.Tensor) return PreprocessedMemoryNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, PreprocessedFeatureVector(float_features=state), PreprocessedFeatureVector(float_features=next_state), action, ) else: assert isinstance(self.action, torch.ByteTensor) return PreprocessedMemoryNetworkInput( self.reward, self.time_diff, self.step, self.not_terminal, PreprocessedFeatureVector(float_features=state), PreprocessedFeatureVector(float_features=next_state), self.action.float(), ) @dataclass class ExtraData(BaseDataClass): mdp_id: Optional[ np.ndarray ] = None # Need to use a numpy array because torch doesn't support strings sequence_number: Optional[torch.Tensor] = None action_probability: Optional[torch.Tensor] = None max_num_actions: Optional[int] = None metrics: Optional[torch.Tensor] = None @dataclass class PreprocessedTrainingBatch(BaseDataClass): training_input: Union[ PreprocessedBaseInput, PreprocessedDiscreteDqnInput, PreprocessedParametricDqnInput, PreprocessedMemoryNetworkInput, PreprocessedPolicyNetworkInput, ] extras: Any def batch_size(self): return self.training_input.state.float_features.size()[0] @dataclass class RawTrainingBatch(BaseDataClass): training_input: Union[ RawBaseInput, RawDiscreteDqnInput, RawParametricDqnInput, RawPolicyNetworkInput ] extras: Any def batch_size(self): return self.training_input.state.float_features.value.size()[0] def preprocess( self, training_input: Union[ PreprocessedBaseInput, PreprocessedDiscreteDqnInput, PreprocessedParametricDqnInput, PreprocessedMemoryNetworkInput, PreprocessedPolicyNetworkInput, ], ) -> PreprocessedTrainingBatch: return PreprocessedTrainingBatch( training_input=training_input, extras=self.extras ) @dataclass class SingleQValue(BaseDataClass): q_value: torch.Tensor @dataclass class AllActionQValues(BaseDataClass): q_values: torch.Tensor @dataclass class MemoryNetworkOutput(BaseDataClass): mus: torch.Tensor sigmas: torch.Tensor logpi: torch.Tensor reward: torch.Tensor not_terminal: torch.Tensor last_step_lstm_hidden: torch.Tensor last_step_lstm_cell: torch.Tensor all_steps_lstm_hidden: torch.Tensor @dataclass class DqnPolicyActionSet(BaseDataClass): greedy: int softmax: int @dataclass class SacPolicyActionSet: greedy: torch.Tensor greedy_propensity: float

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facebook-github-bot@users.noreply.github.com

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/qq/models.py

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tuchuanchuan/qq-spider

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2021-05-14T05:52:02.577402

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# coding: utf-8 import pymysql from sqlalchemy import create_engine, Column, Integer, String from sqlalchemy.ext.declarative import declarative_base Base = declarative_base() engine = create_engine("mysql+pymysql://root:root@192.168.0.13:3306/albums_from_web?charset=utf8") class TencentTable(Base): __tablename__ = 'tencent_albums' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True) url = Column(String(100), nullable=False, unique=True) name = Column(String(1000), nullable=False) artist_name = Column(String(1000), nullable=False) release_company = Column(String(100)) release_date = Column(String(10), default='', ) company_id = Column(Integer) class TencentCompanyTable(Base): __tablename__ = 'tencent_companies' id = Column(Integer, primary_key=True) company = Column(String(100), nullable=False) company_id = Column(Integer, nullable=True) album_total = Column(Integer, default=0) mv_total = Column(Integer, default=0) song_total = Column(Integer, default=0) singer_total = Column(Integer, default=0) # class TencentUrl(Base): # __tablename__ = 'tencent_url' # id = Column(Integer, primary_key=True) # url = Column(String(100), nullable=False, unique=True) # artist_mid = Column(String(50), nullable=False) class TencentArtist(Base): __tablename__ = 'tencent_artists' id = Column(Integer, primary_key=True) artist = Column(String(100), nullable=False) artist_id = Column(Integer, nullable=False) artist_mid = Column(String(50), nullable=False) page = Column(Integer, nullable=False) class TencentGetAlbum(Base): __tablename__ = 'tencent_get_album' id = Column(Integer, primary_key=True) album_mid = Column(String(100), nullable=False) page = Column(Integer, nullable=False) release_date = Column(String, default='') Base.metadata.create_all(engine)

[ "tuchuanchuan@kanjian.com" ]

tuchuanchuan@kanjian.com