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c7e6dd2366a172288c3918b66ff432ec716ed190 | 10,596 | py | Python | handlers.py | hulingfeng211/sshwall | 5369fd76b9f3e1540349bca61da86b11b22f536e | [
"MIT"
] | 1 | 2018-02-18T16:20:30.000Z | 2018-02-18T16:20:30.000Z | handlers.py | hulingfeng211/sshwall | 5369fd76b9f3e1540349bca61da86b11b22f536e | [
"MIT"
] | null | null | null | handlers.py | hulingfeng211/sshwall | 5369fd76b9f3e1540349bca61da86b11b22f536e | [
"MIT"
] | null | null | null | # -*- coding:utf-8 -*-
import uuid
import tornado.websocket
from daemon import Bridge
from data import ClientData
from utils import check_ip, check_port
from tornado.log import gen_log
from tornado.gen import coroutine
from tornado.web import escape,authenticated
from models import *
class BaseHandler(tornado.web.RequestHandler):
@property
def db(self):
return self.application.db
#@authenticated
def prepare(self,*args,**kwargs):
pass
def get_current_user(self):
user_id = self.get_secure_cookie("user_id")
gen_log.info(user_id)
if not user_id:
return None
user= self.db.query(User).get(int(user_id))
return user
def send_client(self,data,status_code=0):
self.set_header('content-type','application/json')
self.write(escape.json_encode({'data':data,'status':status_code}))
def has_login(self):
return current_user!=None
class UserServerHandler(BaseHandler):
def post(self,*args,**kwargs):
gen_log.info(self.request.body)
user_id=self.get_argument('userid')
server_id=self.get_argument('serverid')
is_add=self.get_argument('is_add')
if all([user_id,server_id,is_add]):
user=self.db.query(User).get(int(user_id))
server=self.db.query(Server).get(int(server_id))
#gen_log.info(is_add)
if is_add=='false':
#gen_log.info('remove')
user.servers.remove(server)
else:
user.servers.append(server)
self.db.commit()
self.send_client('ok')
else:
self.send_client('error',status_code=1)
class UserHandler(BaseHandler):
def get(self,*args,**kwargs):
groups=self.db.query(Group).all()
servers=self.db.query(Server).all()
users=self.db.query(User).filter(User.is_super==False).all()
self.render('user.mgt.html',groups=groups,servers=servers,users=users)
def post(self,*args,**kwargs):
userid=self.get_argument('id',None)
servers=self.get_arguments('server')
username=self.get_argument('username',None)
password=self.get_argument('password',None)
if all([username,password]):
if userid:
user=self.db.query(User).get(int(userid))
user.username=username
user.password=password
else:
user=User(username=username,password=password)
if servers:
server_list=self.db.query(Server).filter(Server.id.in_([int(server_id) for server_id in servers])).all()
user.servers=server_list
self.db.add(user)
self.db.commit()
self.redirect(self.reverse_url('user'))
else:
self.flush('username and password are not allow empty.')
def delete(self,*args,**kwargs):
user_id=self.get_argument("userid",None)
if user_id:
user=self.db.query(User).get(int(user_id))
self.db.delete(user)
self.db.commit()
self.send_client('ok')
else:
self.send_client('error',status_code=1)
class GroupHandler(BaseHandler):
def get(self,*args,**kwargs):
groups=self.db.query(Group).all()
self.render('group.mgt.html',groups=groups)
def post(self,*args,**kwargs):
group_name=self.get_argument('groupname',None)
if group_name:
group=Group(name=group_name)
self.db.add(group)
self.db.commit()
self.redirect(self.reverse_url('group'))
def delete(self,*args,**kwargs):
id=self.get_argument('id',None)
if id:
group=self.db.query(Group).get(int(id))
self.db.delete(group)
self.db.commit()
#self.redirect(self.reverse_url('group'))
self.write('OK')
class IndexHandler(BaseHandler):
@coroutine
def get(self):
groups=self.db.query(Group).all()
group_id=self.get_argument('group',None)
if self.current_user is None:
self.render('index.html',server_list=[],groups=groups)
return
if group_id:
if self.current_user.is_super:
servers=self.db.query(Server).filter(Server.group_id==int(group_id))
else:
servers=[server for server in self.current_user.servers if server.group_id==int(group_id)]
else:
if self.current_user.is_super:
servers=self.db.query(Server).all()
else:
servers=self.current_user.servers
self.render("index.html",server_list=servers,groups=groups)
class TerminalHandler(BaseHandler):
def get(self,*args,**kwargs):
id=self.get_argument('id',-1)
server=self.db.query(Server).get(int(id))
#get_server(id)
if server:
self.render('server.html',server=server)
else:
self.write('Server {} is not exists'.format(id));
class ServerHandler(BaseHandler):
def get(self,*args,**kwargs):
id=self.get_argument('id',-1)
server=self.db.query(Server).get(int(id))
#get_server(id)
if server:
self.write(escape.json_encode({
"id":server.id,
"host":server.host,
"port":server.port,
"username":server.username,
"secret":server.secret,
"remark":server.remark,
"name":server.name
}))
#self.write(escape.json_encode(server))
else:
self.write({})
def delete(self,*args,**kwargs):
server_id=self.get_argument('server',None)
if server_id:
server=self.db.query(Server).get(int(server_id))
self.db.delete(server)
self.db.commit()
self.write('ok')
def post(self,*args,**kwargs):
gen_log.info(self.request.body);
server_id=self.get_argument('id',None)
name=self.get_argument('name',None)
host=self.get_argument('host',None)
port=self.get_argument('port',None)
username=self.get_argument('username',None)
password=self.get_argument('password',None)
group_id=self.get_argument('group',None)
remark=self.get_argument('remark',None)
if all([name,host,port,username,password]):
if server_id:#do update
#server=get_server(server_id)
#server_list.remove(server)
#server=Server(server_id,name,host,int(port),username,password,remark)
#server_list.append(server)
server=self.db.query(Server).get(int(server_id))
server.host=host
server.port=port
server.username=username
server.secret=password
if group_id:
group=self.db.query(Group).get(int(group_id))
server.group=group
self.db.commit()
else:
if group_id:
group=self.db.query(Group).get(int(group_id))
server=Server(name=name,host=host,port=int(port),username=username,secret=password,remark=remark)
server.group=group
self.db.add(server)
self.db.commit()
#server=Server(uuid.uuid1().hex,name,host,int(port),username,password,remark)
#server_list.append(server)
#save data to pickle
#save_server_list()
else:
self.write('Some argument are not allow empty,please check.!!')
self.redirect(self.reverse_url('home'))
class WSHandler(tornado.websocket.WebSocketHandler):
clients = dict()
def initialize(self):
self.command_history=[]
def get_client(self):
return self.clients.get(self._id(), None)
def put_client(self):
bridge = Bridge(self)
self.clients[self._id()] = bridge
def remove_client(self):
bridge = self.get_client()
if bridge:
bridge.destroy()
del self.clients[self._id()]
@staticmethod
def _check_init_param(data):
return 'server_id' in data #check_ip(data["server_id"]) and check_port(data["port"])
@staticmethod
def _is_init_data(data):
return data.get_type() == 'init'
def _id(self):
return id(self)
def open(self):
self.put_client()
def on_message(self, message):
bridge = self.get_client()
gen_log.info(message)
client_data = ClientData(message)
if self._is_init_data(client_data):
if self._check_init_param(client_data.data):
server_id=client_data.data['server_id']
db=self.application.db
user_id=self.get_secure_cookie('user_id')
if user_id:
user=db.query(User).get(int(user_id)) # todo
if not user.is_super:
servers=[server for server in user.servers if server.id==int(server_id)]
assert len(servers)==1
bridge.open(client_data.data,servers[0])
else:
server=db.query(Server).get(int(server_id))
bridge.open(client_data.data,server)
gen_log.info('connection established from: %s' % self._id())
else:
gen_log.warning('user is not valid')
self.remove_client()
else:
self.remove_client()
gen_log.warning('init param invalid: %s' % client_data.data)
else:
if bridge:
self.command_history.append(client_data.data)
if self.command_history[-1]=='\r':
commmand=''.join(self.command_history)
db=self.application.db
server=db.query(Server).get(int(client_data.target))#get_server(client_data.target)
gen_log.info('server {} run command {}'.format(server.host,commmand))
self.command_history.clear()
bridge.trans_forward(client_data.data)
def on_close(self):
self.remove_client()
gen_log.info('client close the connection: %s' % self._id())
| 35.086093 | 120 | 0.570781 |
0ee1c5a39901c5ba0d8a45d2524be4075d2be9b5 | 543 | tsx | TypeScript | src/app/App.tsx | alicepetzinger/bergfest-music | 781b4906d0b4ef61a67651e37ae73b2169a708f2 | [
"MIT"
] | null | null | null | src/app/App.tsx | alicepetzinger/bergfest-music | 781b4906d0b4ef61a67651e37ae73b2169a708f2 | [
"MIT"
] | null | null | null | src/app/App.tsx | alicepetzinger/bergfest-music | 781b4906d0b4ef61a67651e37ae73b2169a708f2 | [
"MIT"
] | null | null | null | import React from 'react';
import styles from './App.module.css';
import Title from './Components/Title/Title';
function App(): JSX.Element {
return (
<main className={styles.container}>
<Title />
<h2 className={styles.h2}>Entry</h2>
<div className={styles.card} />
<form className={styles.form}>
<input type="text" placeholder="Hi, call me..." />
<input type="text" placeholder="...and my lastname" />
<input type="submit" />
</div>
</form>
</main>
);
}
export default App;
| 24.681818 | 62 | 0.598527 |
c8148a64323a597db9b0a0d10c8787bfb2067027 | 7,810 | rs | Rust | rust/server/auth/src/lib.rs | tlowerison/tilings | 0bd477da4e811aa7de39b58f90a3e86323bd6b23 | [
"MIT"
] | 1 | 2021-09-10T04:47:17.000Z | 2021-09-10T04:47:17.000Z | rust/server/auth/src/lib.rs | tlowerison/tilings | 0bd477da4e811aa7de39b58f90a3e86323bd6b23 | [
"MIT"
] | null | null | null | rust/server/auth/src/lib.rs | tlowerison/tilings | 0bd477da4e811aa7de39b58f90a3e86323bd6b23 | [
"MIT"
] | null | null | null | pub const COOKIE_KEY: &'static str = "tilings_account_id";
#[cfg(not(target_arch = "wasm32"))]
mod auth {
use super::*;
use argon2;
use base64;
use db_conn::DbConn;
use diesel::{self, PgConnection, prelude::*};
use lazy_static::lazy_static;
use models::*;
use r2d2_redis::{r2d2::Pool, redis, RedisConnectionManager};
use result::{APIKeyError, Error, Result};
use rocket::{
http::Status,
request::{Outcome, Request, FromRequest},
State,
};
use schema::accountrole;
use serde::{Deserialize, Serialize};
use std::{
collections::hash_set::HashSet,
ops::DerefMut,
};
pub const AUTHORIZATION_HEADER_KEY: &'static str = "Authorization";
pub const SECRET: &'static str = "JWT_TOKEN";
pub const TOKEN_DURATION_IN_SECONDS: i64 = 10 * 365 * 24 * 60 * 60;
lazy_static! {
static ref AUTHORIZATION_HEADER_VALUE_PREFIX_END_INDEX: usize = "Bearer ".len();
}
pub struct AuthAccount {
pub id: i32,
account: Option<Account>,
roles: Option<HashSet<RoleEnum>>,
}
impl<'a> AuthAccount {
pub fn new(id: i32) -> AuthAccount {
AuthAccount { id, account: None, roles: None }
}
pub fn allowed(&mut self, allowed_roles: &HashSet<RoleEnum>, conn: &PgConnection) -> Result<bool> {
self.pull_roles(conn)?;
if AuthAccount::has_intersection(self.roles.as_ref().unwrap(), allowed_roles) && self.verified(conn)? {
Ok(true)
} else {
Err(Error::Unauthorized)
}
}
pub fn can_edit(&mut self, owned: Owned, id: i32, conn: &PgConnection) -> Result<bool> {
if let Ok(_) = self.allowed(&ALLOWED_ADMIN_ROLES, conn) {
return Ok(true)
}
self.allowed(&ALLOWED_EDITOR_ROLES, conn).or(Err(Error::Unauthorized))?;
let owner_id = owned.get_owner_id(id, conn)?;
if let Some(owner_id) = owner_id {
if owner_id == self.id {
return Ok(true)
}
}
Err(Error::Unauthorized)
}
pub fn get_account(&'a mut self, conn: &PgConnection) -> Result<&'a Account> {
if let None = self.account {
self.account = Some(Account::find(self.id, conn)?);
}
Ok(self.account.as_ref().unwrap())
}
fn has_intersection(roles: &HashSet<RoleEnum>, allowed_roles: &HashSet<RoleEnum>) -> bool {
for role in roles.iter() {
if allowed_roles.contains(role) {
return true
}
}
return false
}
fn pull_roles(&mut self, conn: &PgConnection) -> Result<()> {
if let Some(_) = &self.roles {
return Ok(())
}
let account_roles = accountrole::table.filter(accountrole::account_id.eq(&self.id))
.load(conn)?;
self.roles = Some(account_roles
.into_iter()
.filter_map(AccountRole::as_role_enum)
.collect()
);
Ok(())
}
fn verified(&mut self, conn: &PgConnection) -> Result<bool> {
self.get_account(conn)?;
if self.account.as_ref().unwrap().verified {
Ok(true)
} else {
Err(Error::Unauthorized)
}
}
}
#[derive(Debug, Deserialize, Serialize)]
pub struct APIKeyClaims {
pub email: String,
pub api_key: String,
}
impl APIKeyClaims {
fn decode(encoded: &str) -> Result<APIKeyClaims> {
let decoded = base64::decode(encoded).or(Err(Error::APIKey(APIKeyError::Invalid)))?;
let decoded = std::str::from_utf8(decoded.as_slice()).or(Err(Error::APIKey(APIKeyError::Invalid)))?;
serde_json::from_str::<APIKeyClaims>(decoded)
.or(Err(Error::APIKey(APIKeyError::Invalid)))
}
pub fn encode(self) -> Result<String> {
let serialized = serde_json::to_string(&self).or(Err(Error::Default))?;
Ok(base64::encode(String::from(serialized)))
}
}
#[rocket::async_trait]
impl<'r> FromRequest<'r> for AuthAccount {
type Error = Error;
async fn from_request(req: &'r Request<'_>) -> Outcome<Self, Self::Error> {
match req.headers().get_one(AUTHORIZATION_HEADER_KEY) {
None => match req.cookies().get_private(COOKIE_KEY) {
Some(cookie) => {
let redis_pool = match req.guard::<&State<Pool<RedisConnectionManager>>>().await {
Outcome::Success(redis_pool) => redis_pool,
_ => return Outcome::Failure((Status::InternalServerError, Error::Default)),
};
let mut redis_conn = match redis_pool.get() {
Ok(redis_conn) => redis_conn,
Err(err) => return Outcome::Failure((Status::InternalServerError, Error::from(err))),
};
let cookie_key = cookie.value();
let cookie_value = match redis::cmd("GET")
.arg(cookie_key)
.query::<String>(redis_conn.deref_mut())
{
Ok(cookie_value) => cookie_value,
Err(err) => return Outcome::Failure((Status::InternalServerError, Error::from(err))),
};
let account_id = cookie_value.parse::<i32>();
return match account_id {
Ok(account_id) => Outcome::Success(AuthAccount::new(account_id)),
_ => APIKeyError::Invalid.outcome(),
}
},
None => APIKeyError::Missing.outcome(),
},
Some(key) => {
let token = key
.chars()
.into_iter()
.skip(*AUTHORIZATION_HEADER_VALUE_PREFIX_END_INDEX)
.collect::<String>();
let api_key_claims = match APIKeyClaims::decode(&token) {
Ok(api_key_claims) => api_key_claims,
Err(_) => return APIKeyError::Invalid.outcome(),
};
let email = api_key_claims.email;
let api_key_content = api_key_claims.api_key;
let db = match req.guard::<DbConn>().await {
Outcome::Success(db) => db,
_ => return Outcome::Failure((Status::InternalServerError, Error::Default)),
};
let api_key = match db.run(move |conn| APIKey::find_by_email(email, conn)).await {
Ok(api_key) => api_key,
_ => return APIKeyError::Invalid.outcome(),
};
let is_match = match argon2::verify_encoded(&api_key.content, api_key_content.as_bytes()) {
Ok(is_match) => is_match,
_ => return Outcome::Failure((Status::InternalServerError, Error::Default)),
};
if is_match {
Outcome::Success(AuthAccount::new(api_key.account_id))
} else {
APIKeyError::Invalid.outcome()
}
},
}
}
}
}
#[cfg(not(target_arch = "wasm32"))]
pub use self::auth::*;
| 37.729469 | 115 | 0.498976 |
571363e613aa13457f2955bb5f578222ad441927 | 1,093 | h | C | HandScapeSDK.framework/Versions/A/Headers/UITouch+Synthesize.h | HandScapeIncDrive/HSFLballPod | be027ae76f455ae6c2b0d0ed4d9b0128224de3eb | [
"MIT"
] | null | null | null | HandScapeSDK.framework/Versions/A/Headers/UITouch+Synthesize.h | HandScapeIncDrive/HSFLballPod | be027ae76f455ae6c2b0d0ed4d9b0128224de3eb | [
"MIT"
] | null | null | null | HandScapeSDK.framework/Versions/A/Headers/UITouch+Synthesize.h | HandScapeIncDrive/HSFLballPod | be027ae76f455ae6c2b0d0ed4d9b0128224de3eb | [
"MIT"
] | null | null | null | //
// TouchSynthesis.h
// SelfTesting
//
// Created by Matt Gallagher on 23/11/08.
// Copyright 2008 Matt Gallagher. All rights reserved.
//
// Permission is given to use this source code file, free of charge, in any
// project, commercial or otherwise, entirely at your risk, with the condition
// that any redistribution (in part or whole) of source code must retain
// this copyright and permission notice. Attribution in compiled projects is
// appreciated but not required.
//
#import <UIKit/UIKit.h>
@class HSCTouch;
// UITouch (Synthesize)
//
// Category to allow creation and modification of UITouch objects.
//
@interface UITouch (Synthesize)
#ifndef DEBUG_NO_UITOUCH_HSSWD_44
- (id) initWithHSCTouch: (HSCTouch*) hscTouch;
- (id) initInView: (UIView*) view;
- (void) setPhase: (UITouchPhase) phase;
- (void) setLocationInWindow: (CGPoint) location;
#endif
@end
// UIEvent (Synthesize)
//
// A category to allow creation of a touch event.
//
@interface UIEvent (Synthesize)
#ifndef DEBUG_NO_UITOUCH_HSSWD_44
- (id)initWithTouch: (UITouch*) touch;
#endif
@end
| 20.622642 | 79 | 0.729186 |
293a9e78383bfca880dd15e10e4a4690c4c7484a | 1,608 | kt | Kotlin | workflows/src/main/kotlin/com/sorda/flows/tokens/IssueSordaTokens.kt | fowlerrr/Sorda | b9fa92e8f15217c680ed44f490c2a0399e060f5f | [
"Apache-2.0"
] | null | null | null | workflows/src/main/kotlin/com/sorda/flows/tokens/IssueSordaTokens.kt | fowlerrr/Sorda | b9fa92e8f15217c680ed44f490c2a0399e060f5f | [
"Apache-2.0"
] | 7 | 2021-03-10T06:34:29.000Z | 2022-03-02T07:14:02.000Z | workflows/src/main/kotlin/com/sorda/flows/tokens/IssueSordaTokens.kt | fowlerrr/Sorda | b9fa92e8f15217c680ed44f490c2a0399e060f5f | [
"Apache-2.0"
] | 1 | 2020-08-17T08:56:19.000Z | 2020-08-17T08:56:19.000Z | package com.sorda.flows.tokens
import co.paralleluniverse.fibers.Suspendable
import com.r3.corda.lib.tokens.contracts.utilities.heldBy
import com.r3.corda.lib.tokens.contracts.utilities.issuedBy
import com.r3.corda.lib.tokens.contracts.utilities.of
import com.r3.corda.lib.tokens.workflows.flows.rpc.IssueTokens
import net.corda.core.flows.FinalityFlow
import net.corda.core.flows.FlowLogic
import net.corda.core.flows.InitiatingFlow
import net.corda.core.flows.StartableByRPC
import net.corda.core.identity.Party
import net.corda.core.transactions.SignedTransaction
import net.corda.core.utilities.ProgressTracker
import utils.SordaTokenType
/**
* This flow leverages Tokens-SDK in order to issue SORDA Tokens to an existing account.
*
* @property accountName the Name of the account
* @property quantity the quantity of the SORDA Tokens to be issued
*
*/
@StartableByRPC
@InitiatingFlow
class IssueSordaTokens (
private val quantity: Double
) : FlowLogic<SignedTransaction>() {
override val progressTracker: ProgressTracker = IssueSordaTokens.tracker()
companion object {
object ISSUE : ProgressTracker.Step("Issue Token")
object COMPLETE : ProgressTracker.Step("Complete Token Issuance") {
override fun childProgressTracker() = FinalityFlow.tracker()
}
fun tracker() = ProgressTracker(ISSUE, COMPLETE)
}
@Suspendable
override fun call() : SignedTransaction {
val tokens = quantity of SordaTokenType issuedBy ourIdentity heldBy ourIdentity
return subFlow(IssueTokens(listOf(tokens), emptyList()))
}
} | 33.5 | 88 | 0.764303 |
3fb948761677fc2b752126fb1195528781ad1cca | 22,619 | sql | SQL | citys.sql | yhif/Provinces_citys_areas | b76eb2f9e8cf8f1125a8cdae349b9d74364074b1 | [
"Apache-2.0"
] | 16 | 2018-11-01T07:48:48.000Z | 2021-03-15T15:48:21.000Z | citys.sql | yhif/Provinces_citys_areas | b76eb2f9e8cf8f1125a8cdae349b9d74364074b1 | [
"Apache-2.0"
] | null | null | null | citys.sql | yhif/Provinces_citys_areas | b76eb2f9e8cf8f1125a8cdae349b9d74364074b1 | [
"Apache-2.0"
] | 8 | 2019-02-02T06:35:23.000Z | 2021-03-15T15:48:11.000Z | # ************************************************************
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# Database: dhweb
# Generation Time: 2016-05-25 07:26:38 +0000
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# Dump of table citys
# ------------------------------------------------------------
DROP TABLE IF EXISTS `citys`;
CREATE TABLE `citys` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`city_name` varchar(64) NOT NULL DEFAULT '' COMMENT '城市名称',
`alias` varchar(64) NOT NULL DEFAULT '',
`province_id` int(11) NOT NULL COMMENT '省份ID',
`pinyin` varchar(64) NOT NULL DEFAULT '',
`abbr` varchar(32) NOT NULL DEFAULT '',
`zip` int(11) NOT NULL DEFAULT '0',
PRIMARY KEY (`id`)
) ENGINE=MyISAM DEFAULT CHARSET=utf8;
LOCK TABLES `citys` WRITE;
/*!40000 ALTER TABLE `citys` DISABLE KEYS */;
INSERT INTO `citys` (`id`, `city_name`, `alias`, `province_id`, `pinyin`, `abbr`, `zip`)
VALUES
(110100,'北京市','北京',110000,'BeiJing','BJ',100000),
(120100,'天津市','天津',120000,'TianJin','TJ',300000),
(310100,'上海市','上海',310000,'ShangHai','SH',200000),
(500100,'重庆市','重庆',500000,'ZhongQing','ZQ',400000),
(820100,'澳门','澳门',820000,'Macau','Mac',0),
(130100,'石家庄市','石家庄',130000,'ShiJiaZhuang','SJZ',50000),
(130200,'唐山市','唐山',130000,'TangShan','TS',63000),
(130300,'秦皇岛市','秦皇岛',130000,'QinHuangDao','QHD',66000),
(130400,'邯郸市','邯郸',130000,'HanDan','HD',56000),
(130500,'邢台市','邢台',130000,'XingTai','XT',54000),
(130600,'保定市','保定',130000,'BaoDing','BD',71000),
(130700,'张家口市','张家口',130000,'ZhangJiaKou','ZJK',75000),
(130800,'承德市','承德',130000,'ChengDe','CD',67000),
(130900,'沧州市','沧州',130000,'CangZhou','CZ',61000),
(131000,'廊坊市','廊坊',130000,'LangFang','LF',65000),
(131100,'衡水市','衡水',130000,'HengShui','HS',53000),
(140100,'太原市','太原',140000,'TaiYuan','TY',30000),
(140200,'大同市','大同',140000,'DaTong','DT',37000),
(140300,'阳泉市','阳泉',140000,'YangQuan','YQ',45000),
(140400,'长治市','长治',140000,'ZhangZhi','ZZ',46000),
(140500,'晋城市','晋城',140000,'JinCheng','JC',48000),
(140600,'朔州市','朔州',140000,'ShuoZhou','SZ',36000),
(140700,'晋中市','晋中',140000,'JinZhong','JZ',30600),
(140800,'运城市','运城',140000,'YunCheng','YC',44000),
(140900,'忻州市','忻州',140000,'XinZhou','XZ',34000),
(141000,'临汾市','临汾',140000,'LinFen','LF',41000),
(141100,'吕梁市','吕梁',140000,'LvLiang','LL',33000),
(150100,'呼和浩特市','呼和浩特',150000,'HuHeHaoTe','HHHT',10000),
(150200,'包头市','包头',150000,'BaoTou','BT',14000),
(150300,'乌海市','乌海',150000,'WuHai','WH',16000),
(150400,'赤峰市','赤峰',150000,'ChiFeng','CF',24000),
(150500,'通辽市','通辽',150000,'TongLiao','TL',28000),
(150600,'鄂尔多斯市','鄂尔多斯',150000,'EErDuoSi','EEDS',17004),
(150700,'呼伦贝尔市','呼伦贝尔',150000,'HuLunBeiEr','HLBE',21008),
(150800,'巴彦淖尔市','巴彦淖尔',150000,'BaYanNaoEr','BYNE',15001),
(150900,'乌兰察布市','乌兰察布',150000,'WuLanChaBu','WLCB',12000),
(152200,'兴安盟','兴安盟',150000,'XingAnMeng','XAM',137401),
(152500,'锡林郭勒盟','锡林郭勒盟',150000,'XiLinGuoLeiMeng','XLGLM',26021),
(152900,'阿拉善盟','阿拉善盟',150000,'ALaShanMeng','ALSM',750306),
(210100,'沈阳市','沈阳',210000,'ChenYang','CY',110000),
(210200,'大连市','大连',210000,'DaLian','DL',116000),
(210300,'鞍山市','鞍山',210000,'AnShan','AS',114000),
(210400,'抚顺市','抚顺',210000,'FuShun','FS',113000),
(210500,'本溪市','本溪',210000,'BenXi','BX',117000),
(210600,'丹东市','丹东',210000,'DanDong','DD',118000),
(210700,'锦州市','锦州',210000,'JinZhou','JZ',121000),
(210800,'营口市','营口',210000,'YingKou','YK',115000),
(210900,'阜新市','阜新',210000,'FuXin','FX',123000),
(211000,'辽阳市','辽阳',210000,'LiaoYang','LY',111000),
(211100,'盘锦市','盘锦',210000,'PanJin','PJ',124000),
(211200,'铁岭市','铁岭',210000,'TieLing','TL',112000),
(211300,'朝阳市','朝阳',210000,'ChaoYang','CY',122000),
(211400,'葫芦岛市','葫芦岛',210000,'HuLuDao','HLD',125000),
(220100,'长春市','长春',220000,'ZhangChun','ZC',130000),
(220200,'吉林市','吉林',220000,'JiLin','JL',132000),
(220300,'四平市','四平',220000,'SiPing','SP',136000),
(220400,'辽源市','辽源',220000,'LiaoYuan','LY',136200),
(220500,'通化市','通化',220000,'TongHua','TH',134000),
(220600,'白山市','白山',220000,'BaiShan','BS',134300),
(220700,'松原市','松原',220000,'SongYuan','SY',138000),
(220800,'白城市','白城',220000,'BaiCheng','BC',137000),
(222400,'延边朝鲜族自治州','延边朝鲜族',220000,'YanBianChaoXianZu','YBCXZ',133000),
(230100,'哈尔滨市','哈尔滨',230000,'HaErBin','HEB',150000),
(230200,'齐齐哈尔市','齐齐哈尔',230000,'QiQiHaEr','QQHE',161000),
(230300,'鸡西市','鸡西',230000,'JiXi','JX',158100),
(230400,'鹤岗市','鹤岗',230000,'HeGang','HG',154100),
(230500,'双鸭山市','双鸭山',230000,'ShuangYaShan','SYS',155100),
(230600,'大庆市','大庆',230000,'DaQing','DQ',163000),
(230700,'伊春市','伊春',230000,'YiChun','YC',153000),
(230800,'佳木斯市','佳木斯',230000,'JiaMuSi','JMS',154000),
(230900,'七台河市','七台河',230000,'QiTaiHe','QTH',154600),
(231081,'绥芬河市','绥芬河',230000,'SuiFenHe','SFH',157300),
(231100,'黑河市','黑河',230000,'HeiHe','HH',164300),
(231200,'绥化市','绥化',230000,'SuiHua','SH',152000),
(232700,'大兴安岭地区','大兴安岭地区',230000,'DaXingAnLingDeQu','DXALDQ',165000),
(320100,'南京市','南京',320000,'NanJing','NJ',210000),
(320200,'无锡市','无锡',320000,'WuXi','WX',214000),
(320300,'徐州市','徐州',320000,'XuZhou','XZ',221000),
(320400,'常州市','常州',320000,'ChangZhou','CZ',213000),
(320500,'苏州市','苏州',320000,'SuZhou','SZ',215000),
(320600,'南通市','南通',320000,'NanTong','NT',226000),
(320700,'连云港市','连云港',320000,'LianYunGang','LYG',222000),
(320800,'淮安市','淮安',320000,'HuaiAn','HA',223200),
(320900,'盐城市','盐城',320000,'YanCheng','YC',224000),
(321000,'扬州市','扬州',320000,'YangZhou','YZ',225000),
(321100,'镇江市','镇江',320000,'ZhenJiang','ZJ',212000),
(321300,'宿迁市','宿迁',320000,'SuQian','SQ',223800),
(330100,'杭州市','杭州',330000,'HangZhou','HZ',310000),
(330200,'宁波市','宁波',330000,'NingBo','NB',315000),
(330300,'温州市','温州',330000,'WenZhou','WZ',325000),
(330400,'嘉兴市','嘉兴',330000,'JiaXing','JX',314000),
(330500,'湖州市','湖州',330000,'HuZhou','HZ',313000),
(330600,'绍兴市','绍兴',330000,'ShaoXing','SX',312000),
(330700,'金华市','金华',330000,'JinHua','JH',321000),
(330800,'衢州市','衢州',330000,'QuZhou','QZ',324000),
(330900,'舟山市','舟山',330000,'ZhouShan','ZS',316000),
(331000,'台州市','台州',330000,'TaiZhou','TZ',318000),
(331100,'丽水市','丽水',330000,'LiShui','LS',323000),
(340100,'合肥市','合肥',340000,'HeFei','HF',230000),
(340200,'芜湖市','芜湖',340000,'WuHu','WH',241000),
(340300,'蚌埠市','蚌埠',340000,'BangBu','BB',233000),
(340400,'淮南市','淮南',340000,'HuaiNan','HN',232000),
(340500,'马鞍山市','马鞍山',340000,'MaAnShan','MAS',243000),
(340600,'淮北市','淮北',340000,'HuaiBei','HB',235000),
(340700,'铜陵市','铜陵',340000,'TongLing','TL',244000),
(340800,'安庆市','安庆',340000,'AnQing','AQ',246000),
(341000,'黄山市','黄山',340000,'HuangShan','HS',245000),
(341100,'滁州市','滁州',340000,'ChuZhou','CZ',239000),
(341200,'阜阳市','阜阳',340000,'FuYang','FY',236000),
(341500,'六安市','六安',340000,'LiuAn','LA',237000),
(341600,'亳州市','亳州',340000,'BoZhou','BZ',236800),
(341700,'池州市','池州',340000,'ChiZhou','CZ',247000),
(341800,'宣城市','宣城',340000,'XuanCheng','XC',242000),
(350100,'福州市','福州',350000,'FuZhou','FZ',350000),
(350200,'厦门市','厦门',350000,'ShaMen','SM',361000),
(350300,'莆田市','莆田',350000,'PuTian','PT',351100),
(350400,'三明市','三明',350000,'SanMing','SM',365000),
(350500,'泉州市','泉州',350000,'QuanZhou','QZ',362000),
(350600,'漳州市','漳州',350000,'ZhangZhou','ZZ',363000),
(350700,'南平市','南平',350000,'NanPing','NP',353000),
(350800,'龙岩市','龙岩',350000,'LongYan','LY',364000),
(350900,'宁德市','宁德',350000,'NingDe','ND',352100),
(360100,'南昌市','南昌',360000,'NanChang','NC',330000),
(360200,'景德镇市','景德镇',360000,'JingDeZhen','JDZ',333000),
(360300,'萍乡市','萍乡',360000,'PingXiang','PX',337000),
(360400,'九江市','九江',360000,'JiuJiang','JJ',332000),
(360500,'新余市','新余',360000,'XinYu','XY',338000),
(360700,'赣州市','赣州',360000,'GanZhou','GZ',341000),
(360800,'吉安市','吉安',360000,'JiAn','JA',343000),
(360900,'宜春市','宜春',360000,'YiChun','YC',336000),
(361000,'抚州市','抚州',360000,'FuZhou','FZ',344000),
(361100,'上饶市','上饶',360000,'ShangRao','SR',334000),
(370100,'济南市','济南',370000,'JiNan','JN',250000),
(370200,'青岛市','青岛',370000,'QingDao','QD',266000),
(370300,'淄博市','淄博',370000,'ZiBo','ZB',255000),
(370400,'枣庄市','枣庄',370000,'ZaoZhuang','ZZ',277100),
(370500,'东营市','东营',370000,'DongYing','DY',257000),
(370600,'烟台市','烟台',370000,'YanTai','YT',264000),
(370700,'潍坊市','潍坊',370000,'WeiFang','WF',261000),
(370800,'济宁市','济宁',370000,'JiNing','JN',272100),
(370900,'泰安市','泰安',370000,'TaiAn','TA',271000),
(371000,'威海市','威海',370000,'WeiHai','WH',264200),
(371100,'日照市','日照',370000,'RiZhao','RZ',276800),
(371200,'莱芜市','莱芜',370000,'LaiWu','LW',271100),
(371300,'临沂市','临沂',370000,'LinYi','LY',276000),
(371400,'德州市','德州',370000,'DeZhou','DZ',253000),
(371500,'聊城市','聊城',370000,'LiaoCheng','LC',252000),
(371600,'滨州市','滨州',370000,'BinZhou','BZ',256600),
(371700,'菏泽市','菏泽',370000,'HeZe','HZ',274000),
(410100,'郑州市','郑州',410000,'ZhengZhou','ZZ',450000),
(410200,'开封市','开封',410000,'KaiFeng','KF',475000),
(410300,'洛阳市','洛阳',410000,'LuoYang','LY',471000),
(410400,'平顶山市','平顶山',410000,'PingDingShan','PDS',467000),
(410500,'安阳市','安阳',410000,'AnYang','AY',455000),
(410600,'鹤壁市','鹤壁',410000,'HeBi','HB',458000),
(410700,'新乡市','新乡',410000,'XinXiang','XX',453000),
(410800,'焦作市','焦作',410000,'JiaoZuo','JZ',454000),
(410900,'濮阳市','濮阳',410000,'PuYang','PY',457000),
(411000,'许昌市','许昌',410000,'XuChang','XC',461000),
(411200,'三门峡市','三门峡',410000,'SanMenXia','SMX',472000),
(411300,'南阳市','南阳',410000,'NanYang','NY',473000),
(411400,'商丘市','商丘',410000,'ShangQiu','SQ',476000),
(411500,'信阳市','信阳',410000,'XinYang','XY',464000),
(411600,'周口市','周口',410000,'ZhouKou','ZK',466000),
(411700,'驻马店市','驻马店',410000,'ZhuMaDian','ZMD',463000),
(419001,'济源市','济源',410000,'JiYuan','JY',454650),
(420100,'武汉市','武汉',420000,'WuHan','WH',430000),
(420200,'黄石市','黄石',420000,'HuangShi','HS',435000),
(420300,'十堰市','十堰',420000,'ShiYan','SY',442000),
(420500,'宜昌市','宜昌',420000,'YiChang','YC',443000),
(420600,'襄阳市','襄阳',420000,'XiangYang','XY',441000),
(420700,'鄂州市','鄂州',420000,'EZhou','EZ',436000),
(420800,'荆门市','荆门',420000,'JingMen','JM',448000),
(420900,'孝感市','孝感',420000,'XiaoGan','XG',432000),
(421000,'荆州市','荆州',420000,'JingZhou','JZ',434000),
(421100,'黄冈市','黄冈',420000,'HuangGang','HG',438000),
(421200,'咸宁市','咸宁',420000,'XianNing','XN',437100),
(421300,'随州市','随州',420000,'SuiZhou','SZ',441300),
(422800,'恩施土家族苗族自治州','恩施土家族苗族',420000,'EnShiTuJiaZuMiaoZu','ESTJZMZ',445400),
(429004,'仙桃市','仙桃',420000,'XianTao','XT',433000),
(429005,'潜江市','潜江',420000,'QianJiang','QJ',433100),
(429006,'天门市','天门',420000,'TianMen','TM',431700),
(429021,'神农架林区','神农架林区',420000,'ShenNongJiaLinQu','SNJLQ',442400),
(430100,'长沙市','长沙',430000,'ZhangSha','ZS',410000),
(430200,'株洲市','株洲',430000,'ZhuZhou','ZZ',412000),
(430300,'湘潭市','湘潭',430000,'XiangTan','XT',411100),
(430400,'衡阳市','衡阳',430000,'HengYang','HY',421000),
(430500,'邵阳市','邵阳',430000,'ShaoYang','SY',422000),
(430600,'岳阳市','岳阳',430000,'YueYang','YY',414000),
(430700,'常德市','常德',430000,'ChangDe','CD',415000),
(430800,'张家界市','张家界',430000,'ZhangJiaJie','ZJJ',427000),
(430900,'益阳市','益阳',430000,'YiYang','YY',413000),
(431000,'郴州市','郴州',430000,'ChenZhou','CZ',423000),
(431100,'永州市','永州',430000,'YongZhou','YZ',425000),
(431200,'怀化市','怀化',430000,'HuaiHua','HH',418000),
(431300,'娄底市','娄底',430000,'LouDi','LD',417000),
(433100,'湘西土家族苗族自治州','湘西土家族苗族',430000,'XiangXiTuJiaZuMiaoZu','XXTJZMZ',416000),
(440100,'广州市','广州',440000,'GuangZhou','GZ',510000),
(440200,'韶关市','韶关',440000,'ShaoGuan','SG',512000),
(440300,'深圳市','深圳',440000,'ShenZhen','SZ',518000),
(440400,'珠海市','珠海',440000,'ZhuHai','ZH',519000),
(440500,'汕头市','汕头',440000,'ShanTou','ST',515000),
(440600,'佛山市','佛山',440000,'FuShan','FS',528000),
(440700,'江门市','江门',440000,'JiangMen','JM',529000),
(440800,'湛江市','湛江',440000,'ZhanJiang','ZJ',524000),
(440900,'茂名市','茂名',440000,'MaoMing','MM',525000),
(441200,'肇庆市','肇庆',440000,'ZhaoQing','ZQ',526000),
(441300,'惠州市','惠州',440000,'HuiZhou','HZ',516000),
(441400,'梅州市','梅州',440000,'MeiZhou','MZ',514000),
(441500,'汕尾市','汕尾',440000,'ShanWei','SW',516600),
(441600,'河源市','河源',440000,'HeYuan','HY',517000),
(441602,'源城区','源城区',440000,'YuanChengQu','YCQ',517000),
(441700,'阳江市','阳江',440000,'YangJiang','YJ',529500),
(441800,'清远市','清远',440000,'QingYuan','QY',511500),
(441900,'东莞市','东莞',440000,'DongGuan','DG',523000),
(442000,'中山市','中山',440000,'ZhongShan','ZS',528400),
(445100,'潮州市','潮州',440000,'ChaoZhou','CZ',521000),
(445200,'揭阳市','揭阳',440000,'JieYang','JY',522000),
(445300,'云浮市','云浮',440000,'YunFu','YF',527300),
(450100,'南宁市','南宁',450000,'NanNing','NN',530000),
(450200,'柳州市','柳州',450000,'LiuZhou','LZ',545000),
(450300,'桂林市','桂林',450000,'GuiLin','GL',541000),
(450400,'梧州市','梧州',450000,'WuZhou','WZ',543000),
(450500,'北海市','北海',450000,'BeiHai','BH',536000),
(450600,'防城港市','防城港',450000,'FangChengGang','FCG',538000),
(450700,'钦州市','钦州',450000,'QinZhou','QZ',535000),
(450800,'贵港市','贵港',450000,'GuiGang','GG',537100),
(450900,'玉林市','玉林',450000,'YuLin','YL',537000),
(451000,'百色市','百色',450000,'BaiSe','BS',533000),
(451100,'贺州市','贺州',450000,'HeZhou','HZ',542800),
(451200,'河池市','河池',450000,'HeChi','HC',547000),
(451300,'来宾市','来宾',450000,'LaiBin','LB',546100),
(451400,'崇左市','崇左',450000,'ChongZuo','CZ',532200),
(460100,'海口市','海口',460000,'HaiKou','HK',570000),
(460200,'三亚市','三亚',460000,'SanYa','SY',572000),
(460300,'三沙市','三沙',460000,'SanSha','SS',0),
(469001,'五指山市','五指山',460000,'WuZhiShan','WZS',572200),
(469002,'琼海市','琼海',460000,'QiongHai','QH',571400),
(469003,'儋州市','儋州',460000,'DanZhou','DZ',571700),
(469005,'文昌市','文昌',460000,'WenChang','WC',571300),
(469006,'万宁市','万宁',460000,'WanNing','WN',571500),
(469007,'东方市','东方',460000,'DongFang','DF',572600),
(469021,'定安县','定安',460000,'DingAn','DA',0),
(469022,'屯昌县','屯昌',460000,'TunChang','TC',0),
(469023,'澄迈县','澄迈',460000,'ChengMai','CM',0),
(469024,'临高县','临高',460000,'LinGao','LG',0),
(469025,'白沙黎族自治县','白沙黎族',460000,'BaiShaLiZu','BSLZ',571200),
(469026,'昌江黎族自治县','昌江黎族',460000,'ChangJiangLiZu','CJLZ',571600),
(469027,'乐东黎族自治县','乐东黎族',460000,'LeDongLiZu','LDLZ',571900),
(469028,'陵水黎族自治县','陵水黎族',460000,'LingShuiLiZu','LSLZ',571800),
(469029,'保亭黎族苗族自治县','保亭黎族苗族',460000,'BaoTingLiZuMiaoZu','BTLZMZ',0),
(469030,'琼中黎族苗族自治县','琼中黎族苗族',460000,'QiongZhongLiZuMiaoZu','QZLZMZ',572800),
(510100,'成都市','成都',510000,'ChengDou','CD',610000),
(510300,'自贡市','自贡',510000,'ZiGong','ZG',643000),
(510400,'攀枝花市','攀枝花',510000,'PanZhiHua','PZH',617000),
(510500,'泸州市','泸州',510000,'LuZhou','LZ',646000),
(510600,'德阳市','德阳',510000,'DeYang','DY',618000),
(510700,'绵阳市','绵阳',510000,'MianYang','MY',621000),
(510800,'广元市','广元',510000,'GuangYuan','GY',628000),
(510900,'遂宁市','遂宁',510000,'SuiNing','SN',629000),
(511000,'内江市','内江',510000,'NeiJiang','NJ',641000),
(511100,'乐山市','乐山',510000,'LeShan','LS',614000),
(511300,'南充市','南充',510000,'NanChong','NC',637000),
(511400,'眉山市','眉山',510000,'MeiShan','MS',620000),
(511500,'宜宾市','宜宾',510000,'YiBin','YB',644000),
(511600,'广安市','广安',510000,'GuangAn','GA',638000),
(511700,'达州市','达州',510000,'DaZhou','DZ',635000),
(511800,'雅安市','雅安',510000,'YaAn','YA',625000),
(511900,'巴中市','巴中',510000,'BaZhong','BZ',636000),
(511902,'巴州区','巴州区',510000,'BaZhouQu','BZQ',636601),
(512000,'资阳市','资阳',510000,'ZiYang','ZY',641300),
(513200,'阿坝藏族羌族自治州','阿坝藏族羌族',510000,'ABaCangZuQiangZu','ABCZQZ',623000),
(513300,'甘孜藏族自治州','甘孜藏族',510000,'GanZiCangZu','GZCZ',626000),
(513400,'凉山彝族自治州','凉山彝族',510000,'LiangShanYiZu','LSYZ',615000),
(520100,'贵阳市','贵阳',520000,'GuiYang','GY',550000),
(520200,'六盘水市','六盘水',520000,'LiuPanShui','LPS',553000),
(520300,'遵义市','遵义',520000,'ZunYi','ZY',563000),
(520400,'安顺市','安顺',520000,'AnShun','AS',561000),
(520500,'毕节市','毕节',520000,'BiJie','BJ',0),
(520600,'铜仁市','铜仁',520000,'TongRen','TR',0),
(522300,'黔西南布依族苗族自治州','黔西南布依族苗族',520000,'QianXiNanBuYiZuMiaoZu','QXNBYZMZ',562400),
(522600,'黔东南苗族侗族自治州','黔东南苗族侗族',520000,'QianDongNanMiaoZuDongZu','QDNMZDZ',556000),
(522700,'黔南布依族苗族自治州','黔南布依族苗族',520000,'QianNanBuYiZuMiaoZu','QNBYZMZ',558200),
(530100,'昆明市','昆明',530000,'KunMing','KM',650000),
(530300,'曲靖市','曲靖',530000,'QuJing','QJ',655000),
(530400,'玉溪市','玉溪',530000,'YuXi','YX',653100),
(530500,'保山市','保山',530000,'BaoShan','BS',678200),
(530600,'昭通市','昭通',530000,'ZhaoTong','ZT',657000),
(530700,'丽江市','丽江',530000,'LiJiang','LJ',674100),
(530800,'普洱市','普洱',530000,'PuEr','PE',665000),
(530900,'临沧市','临沧',530000,'LinCang','LC',677000),
(532300,'楚雄彝族自治州','楚雄彝族',530000,'ChuXiongYiZu','CXYZ',675000),
(532500,'红河哈尼族彝族自治州','红河哈尼族彝族',530000,'HongHeHaNiZuYiZu','HHHNZYZ',662200),
(532600,'文山壮族苗族自治州','文山壮族苗族',530000,'WenShanZhuangZuMiaoZu','WSZZMZ',663200),
(532800,'西双版纳傣族自治州','西双版纳傣族',530000,'XiShuangBanNaDaiZu','XSBNDZ',666100),
(532900,'大理白族自治州','大理白族',530000,'DaLiBaiZu','DLBZ',671000),
(533100,'德宏傣族景颇族自治州','德宏傣族景颇族',530000,'DeHongDaiZuJingPoZu','DHDZJPZ',678600),
(533300,'怒江傈僳族自治州','怒江傈僳族',530000,'NuJiangLiSuZu','NJLSZ',673200),
(533400,'迪庆藏族自治州','迪庆藏族',530000,'DiQingCangZu','DQCZ',674400),
(540100,'拉萨市','拉萨',540000,'LaSa','LS',850000),
(542100,'昌都地区','昌都地区',540000,'ChangDouDeQu','CDDQ',854000),
(542200,'山南地区','山南地区',540000,'ShanNanDeQu','SNDQ',856100),
(542300,'日喀则地区','日喀则地区',540000,'RiKaZeDeQu','RKZDQ',857400),
(542400,'那曲地区','那曲地区',540000,'NaQuDeQu','NQDQ',852000),
(542500,'阿里地区','阿里地区',540000,'ALiDeQu','ALDQ',859400),
(542600,'林芝地区','林芝地区',540000,'LinZhiDeQu','LZDQ',860100),
(610100,'西安市','西安',610000,'XiAn','XA',710000),
(610200,'铜川市','铜川',610000,'TongChuan','TC',727000),
(610300,'宝鸡市','宝鸡',610000,'BaoJi','BJ',721000),
(610400,'咸阳市','咸阳',610000,'XianYang','XY',712000),
(610500,'渭南市','渭南',610000,'WeiNan','WN',714000),
(610600,'延安市','延安',610000,'YanAn','YA',716000),
(610700,'汉中市','汉中',610000,'HanZhong','HZ',723000),
(610800,'榆林市','榆林',610000,'YuLin','YL',719000),
(610900,'安康市','安康',610000,'AnKang','AK',725000),
(611000,'商洛市','商洛',610000,'ShangLuo','SL',726000),
(620100,'兰州市','兰州',620000,'LanZhou','LZ',730000),
(620200,'嘉峪关市','嘉峪关',620000,'JiaYuGuan','JYG',735100),
(620300,'金昌市','金昌',620000,'JinChang','JC',737100),
(620400,'白银市','白银',620000,'BaiYin','BY',730900),
(620500,'天水市','天水',620000,'TianShui','TS',741000),
(620600,'武威市','武威',620000,'WuWei','WW',733000),
(620700,'张掖市','张掖',620000,'ZhangYe','ZY',734000),
(620800,'平凉市','平凉',620000,'PingLiang','PL',744000),
(620900,'酒泉市','酒泉',620000,'JiuQuan','JQ',735000),
(621000,'庆阳市','庆阳',620000,'QingYang','QY',745000),
(621100,'定西市','定西',620000,'DingXi','DX',743000),
(621200,'陇南市','陇南',620000,'LongNan','LN',746000),
(622900,'临夏回族自治州','临夏回族',620000,'LinXiaHuiZu','LXHZ',731100),
(623000,'甘南藏族自治州','甘南藏族',620000,'GanNanCangZu','GNCZ',747000),
(630100,'西宁市','西宁',630000,'XiNing','XN',810000),
(630200,'海东市','海东市',630000,'HaiDongShi','HDS',810600),
(632200,'海北藏族自治州','海北藏族',630000,'HaiBeiCangZu','HBCZ',810300),
(632300,'黄南藏族自治州','黄南藏族',630000,'HuangNanCangZu','HNCZ',811300),
(632500,'海南藏族自治州','海南藏族',630000,'HaiNanCangZu','HNCZ',813000),
(632600,'果洛藏族自治州','果洛藏族',630000,'GuoLuoCangZu','GLCZ',814000),
(632700,'玉树藏族自治州','玉树藏族',630000,'YuShuCangZu','YSCZ',815000),
(632800,'海西蒙古族藏族自治州','海西蒙古族藏族',630000,'HaiXiMengGuZuCangZu','HXMGZCZ',816000),
(640100,'银川市','银川',640000,'YinChuan','YC',750000),
(640200,'石嘴山市','石嘴山',640000,'ShiZuiShan','SZS',753000),
(640300,'吴忠市','吴忠',640000,'WuZhong','WZ',751100),
(640400,'固原市','固原',640000,'GuYuan','GY',756000),
(640500,'中卫市','中卫',640000,'ZhongWei','ZW',755000),
(650100,'乌鲁木齐市','乌鲁木齐',650000,'WuLuMuQi','WLMQ',830000),
(650200,'克拉玛依市','克拉玛依',650000,'KeLaMaYi','KLMY',834000),
(652100,'吐鲁番地区','吐鲁番地区',650000,'TuLuFanDeQu','TLFDQ',838000),
(652200,'哈密地区','哈密地区',650000,'HaMiDeQu','HMDQ',839000),
(652300,'昌吉回族自治州','昌吉回族',650000,'ChangJiHuiZu','CJHZ',831100),
(652700,'博尔塔拉蒙古自治州','博尔塔拉蒙古',650000,'BoErTaLaMengGu','BETLMG',833400),
(652800,'巴音郭楞蒙古自治州','巴音郭楞蒙古',650000,'BaYinGuoLengMengGu','BYGLMG',841000),
(652900,'阿克苏地区','阿克苏地区',650000,'AKeSuDeQu','AKSDQ',843000),
(653000,'克孜勒苏柯尔克孜自治州','克孜勒苏柯尔克孜',650000,'KeZiLeiSuKeErKeZi','KZLSKEKZ',845350),
(653100,'喀什地区','喀什地区',650000,'KaShenDeQu','KSDQ',844000),
(653200,'和田地区','和田地区',650000,'HeTianDeQu','HTDQ',848000),
(654000,'伊犁哈萨克自治州','伊犁哈萨克',650000,'YiLiHaSaKe','YLHSK',835000),
(654200,'塔城地区','塔城地区',650000,'TaChengDeQu','TCDQ',834700),
(654300,'阿勒泰地区','阿勒泰地区',650000,'ALeiTaiDeQu','ALTDQ',836500),
(659001,'石河子市','石河子',650000,'ShiHeZi','SHZ',832000),
(659002,'阿拉尔市','阿拉尔',650000,'ALaEr','ALE',843300),
(659003,'图木舒克市','图木舒克',650000,'TuMuShuKe','TMSK',843806),
(659004,'五家渠市','五家渠',650000,'WuJiaQu','WJQ',831300),
(710100,'台北市','台北',710000,'TaiBei','TB',0),
(710200,'高雄市','高雄',710000,'GaoXiong','GX',0),
(710300,'台南市','台南',710000,'TaiNan','TN',0),
(710400,'台中市','台中',710000,'TaiZhong','TZ',0),
(710500,'金门县','金门',710000,'JinMen','JM',0),
(710600,'南投县','南投',710000,'NanTou','NT',0),
(710700,'基隆市','基隆',710000,'JiLong','JL',0),
(710800,'新竹市','新竹',710000,'XinZhu','XZ',0),
(710900,'嘉义市','嘉义',710000,'JiaYi','JY',0),
(711100,'新北市','新北',710000,'XinBei','XB',0),
(711200,'宜兰县','宜兰',710000,'YiLan','YL',0),
(711300,'新竹县','新竹',710000,'XinZhu','XZ',0),
(711400,'桃园县','桃园',710000,'TaoYuan','TY',0),
(711500,'苗栗县','苗栗',710000,'MiaoLi','ML',0),
(711700,'彰化县','彰化',710000,'ZhangHua','ZH',0),
(711900,'嘉义县','嘉义',710000,'JiaYi','JY',0),
(712100,'云林县','云林',710000,'YunLin','YL',0),
(712400,'屏东县','屏东',710000,'PingDong','PD',0),
(712500,'台东县','台东',710000,'TaiDong','TD',0),
(712600,'花莲县','花莲',710000,'HuaLian','HL',0),
(712700,'澎湖县','澎湖',710000,'PengHu','PH',0),
(810100,'香港岛','香港岛',810000,'XiangGangDao','XGD',0),
(810200,'九龙','九龙',810000,'JiuLong','JL',0),
(810300,'新界','新界',810000,'XinJie','XJ',0),
(231000,'牡丹江市','牡丹江',230000,'MuDanJiang','MDJ',157000),
(321200,'泰州市','泰州',320000,'TaiZhou','TZ',225300),
(360600,'鹰潭市','鹰潭',360000,'YingTan','YT',335000),
(341300,'宿州市','宿州',340000,'SuZhou','SZ',234000),
(411100,'漯河市','漯河',410000,'LuoHe','LH',462000);
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| 50.715247 | 88 | 0.643088 |
505d2cef1b5e31bbd0e1faae1dc2786873099062 | 5,926 | go | Go | api/model_telegraf_all_of_links.gen.go | TwentyFiveSoftware/influx-cli | 239a8cc4e9c1964993c51f59a910e83a02b1c9e6 | [
"MIT"
] | 23 | 2021-05-11T21:40:19.000Z | 2022-03-17T15:46:36.000Z | api/model_telegraf_all_of_links.gen.go | TwentyFiveSoftware/influx-cli | 239a8cc4e9c1964993c51f59a910e83a02b1c9e6 | [
"MIT"
] | 208 | 2021-04-12T16:02:16.000Z | 2022-03-24T13:52:08.000Z | api/model_telegraf_all_of_links.gen.go | TwentyFiveSoftware/influx-cli | 239a8cc4e9c1964993c51f59a910e83a02b1c9e6 | [
"MIT"
] | 5 | 2021-09-10T20:19:12.000Z | 2022-03-25T10:04:43.000Z | /*
* Subset of Influx API covered by Influx CLI
*
* No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)
*
* API version: 2.0.0
*/
// Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
package api
import (
"encoding/json"
)
// TelegrafAllOfLinks struct for TelegrafAllOfLinks
type TelegrafAllOfLinks struct {
// URI of resource.
Self *string `json:"self,omitempty" yaml:"self,omitempty"`
// URI of resource.
Labels *string `json:"labels,omitempty" yaml:"labels,omitempty"`
// URI of resource.
Members *string `json:"members,omitempty" yaml:"members,omitempty"`
// URI of resource.
Owners *string `json:"owners,omitempty" yaml:"owners,omitempty"`
}
// NewTelegrafAllOfLinks instantiates a new TelegrafAllOfLinks object
// This constructor will assign default values to properties that have it defined,
// and makes sure properties required by API are set, but the set of arguments
// will change when the set of required properties is changed
func NewTelegrafAllOfLinks() *TelegrafAllOfLinks {
this := TelegrafAllOfLinks{}
return &this
}
// NewTelegrafAllOfLinksWithDefaults instantiates a new TelegrafAllOfLinks object
// This constructor will only assign default values to properties that have it defined,
// but it doesn't guarantee that properties required by API are set
func NewTelegrafAllOfLinksWithDefaults() *TelegrafAllOfLinks {
this := TelegrafAllOfLinks{}
return &this
}
// GetSelf returns the Self field value if set, zero value otherwise.
func (o *TelegrafAllOfLinks) GetSelf() string {
if o == nil || o.Self == nil {
var ret string
return ret
}
return *o.Self
}
// GetSelfOk returns a tuple with the Self field value if set, nil otherwise
// and a boolean to check if the value has been set.
func (o *TelegrafAllOfLinks) GetSelfOk() (*string, bool) {
if o == nil || o.Self == nil {
return nil, false
}
return o.Self, true
}
// HasSelf returns a boolean if a field has been set.
func (o *TelegrafAllOfLinks) HasSelf() bool {
if o != nil && o.Self != nil {
return true
}
return false
}
// SetSelf gets a reference to the given string and assigns it to the Self field.
func (o *TelegrafAllOfLinks) SetSelf(v string) {
o.Self = &v
}
// GetLabels returns the Labels field value if set, zero value otherwise.
func (o *TelegrafAllOfLinks) GetLabels() string {
if o == nil || o.Labels == nil {
var ret string
return ret
}
return *o.Labels
}
// GetLabelsOk returns a tuple with the Labels field value if set, nil otherwise
// and a boolean to check if the value has been set.
func (o *TelegrafAllOfLinks) GetLabelsOk() (*string, bool) {
if o == nil || o.Labels == nil {
return nil, false
}
return o.Labels, true
}
// HasLabels returns a boolean if a field has been set.
func (o *TelegrafAllOfLinks) HasLabels() bool {
if o != nil && o.Labels != nil {
return true
}
return false
}
// SetLabels gets a reference to the given string and assigns it to the Labels field.
func (o *TelegrafAllOfLinks) SetLabels(v string) {
o.Labels = &v
}
// GetMembers returns the Members field value if set, zero value otherwise.
func (o *TelegrafAllOfLinks) GetMembers() string {
if o == nil || o.Members == nil {
var ret string
return ret
}
return *o.Members
}
// GetMembersOk returns a tuple with the Members field value if set, nil otherwise
// and a boolean to check if the value has been set.
func (o *TelegrafAllOfLinks) GetMembersOk() (*string, bool) {
if o == nil || o.Members == nil {
return nil, false
}
return o.Members, true
}
// HasMembers returns a boolean if a field has been set.
func (o *TelegrafAllOfLinks) HasMembers() bool {
if o != nil && o.Members != nil {
return true
}
return false
}
// SetMembers gets a reference to the given string and assigns it to the Members field.
func (o *TelegrafAllOfLinks) SetMembers(v string) {
o.Members = &v
}
// GetOwners returns the Owners field value if set, zero value otherwise.
func (o *TelegrafAllOfLinks) GetOwners() string {
if o == nil || o.Owners == nil {
var ret string
return ret
}
return *o.Owners
}
// GetOwnersOk returns a tuple with the Owners field value if set, nil otherwise
// and a boolean to check if the value has been set.
func (o *TelegrafAllOfLinks) GetOwnersOk() (*string, bool) {
if o == nil || o.Owners == nil {
return nil, false
}
return o.Owners, true
}
// HasOwners returns a boolean if a field has been set.
func (o *TelegrafAllOfLinks) HasOwners() bool {
if o != nil && o.Owners != nil {
return true
}
return false
}
// SetOwners gets a reference to the given string and assigns it to the Owners field.
func (o *TelegrafAllOfLinks) SetOwners(v string) {
o.Owners = &v
}
func (o TelegrafAllOfLinks) MarshalJSON() ([]byte, error) {
toSerialize := map[string]interface{}{}
if o.Self != nil {
toSerialize["self"] = o.Self
}
if o.Labels != nil {
toSerialize["labels"] = o.Labels
}
if o.Members != nil {
toSerialize["members"] = o.Members
}
if o.Owners != nil {
toSerialize["owners"] = o.Owners
}
return json.Marshal(toSerialize)
}
type NullableTelegrafAllOfLinks struct {
value *TelegrafAllOfLinks
isSet bool
}
func (v NullableTelegrafAllOfLinks) Get() *TelegrafAllOfLinks {
return v.value
}
func (v *NullableTelegrafAllOfLinks) Set(val *TelegrafAllOfLinks) {
v.value = val
v.isSet = true
}
func (v NullableTelegrafAllOfLinks) IsSet() bool {
return v.isSet
}
func (v *NullableTelegrafAllOfLinks) Unset() {
v.value = nil
v.isSet = false
}
func NewNullableTelegrafAllOfLinks(val *TelegrafAllOfLinks) *NullableTelegrafAllOfLinks {
return &NullableTelegrafAllOfLinks{value: val, isSet: true}
}
func (v NullableTelegrafAllOfLinks) MarshalJSON() ([]byte, error) {
return json.Marshal(v.value)
}
func (v *NullableTelegrafAllOfLinks) UnmarshalJSON(src []byte) error {
v.isSet = true
return json.Unmarshal(src, &v.value)
}
| 26.221239 | 109 | 0.71836 |
fcad758bf1b9b45e2b85af8718d37012e1af9626 | 366 | css | CSS | resources/style.css | gabzon/info-handicap-1 | 6c65037123248e09ef5f41a1a5210cd0ceab6614 | [
"MIT"
] | null | null | null | resources/style.css | gabzon/info-handicap-1 | 6c65037123248e09ef5f41a1a5210cd0ceab6614 | [
"MIT"
] | null | null | null | resources/style.css | gabzon/info-handicap-1 | 6c65037123248e09ef5f41a1a5210cd0ceab6614 | [
"MIT"
] | null | null | null | /*
Theme Name: Info Handicap 1
Theme URI: https://info-handicap-ge.ch
Description: Thème pour le site info handicap geneve
Version: 0.0.1
Author: Gabriel Zambrano
Author URI: https://zambrano.ch
Text Domain: sage
License: MIT License
License URI: http://opensource.org/licenses/MIT
*/
| 28.153846 | 59 | 0.598361 |
a61da836fcbccbb71930f7c8a9876ad1af1cbaef | 1,450 | kt | Kotlin | src/main/kotlin/game/GameOfLife.kt | mr90/kotlin-game-of-life | fe41df28bb068e31825e6f6fd9084a573d28aef4 | [
"Apache-2.0"
] | null | null | null | src/main/kotlin/game/GameOfLife.kt | mr90/kotlin-game-of-life | fe41df28bb068e31825e6f6fd9084a573d28aef4 | [
"Apache-2.0"
] | null | null | null | src/main/kotlin/game/GameOfLife.kt | mr90/kotlin-game-of-life | fe41df28bb068e31825e6f6fd9084a573d28aef4 | [
"Apache-2.0"
] | null | null | null | package game
import java.awt.Color
import javax.swing.JFrame
import javax.swing.WindowConstants
fun main() {
with(JFrame()) {
title = "Game Of Life"
defaultCloseOperation = WindowConstants.EXIT_ON_CLOSE
val boardWidth = 20
val boardHeight = 8
val settings = GameOfLifeSettings()
add(GameOfLifeView(RandomGameOfLifeInitializer(boardWidth, boardHeight), settings))
val panelWidth = boardWidth*settings.cellSize + (boardWidth+1)*settings.cellMargin
val panelHeight = boardHeight*settings.cellSize + (boardHeight+1)*settings.cellMargin + FRAME_HEADER_HEIGHT
setSize(panelWidth, panelHeight)
isResizable = false
setLocationRelativeTo(null)
isVisible = true
}
}
private val FRAME_HEADER_HEIGHT = 36
private val FONT_NAME = "Arial"
private val FONT_SIZE = 20
private val CELL_SIZE = 64
private val CELL_MARGIN = 16
private val BACKGROUND_COLOR = Color(0xDDDDDD)
private val LIVE_COLOR = Color(0x000000)
private val DEAD_COLOR = Color(0xFFFFFF)
data class GameOfLifeSettings(
val fontName : String = FONT_NAME,
val fontSize : Int = FONT_SIZE,
val cellSize : Int = CELL_SIZE,
val cellMargin : Int = CELL_MARGIN,
val backgroundColor : Color = BACKGROUND_COLOR,
val liveColor : Color = LIVE_COLOR,
val deadColor : Color = DEAD_COLOR)
| 30.208333 | 115 | 0.675172 |
98c4bd44d7e72b2f4a728ab63468c3ae26247d62 | 339 | html | HTML | userpypi/templates/djangopypi/package_list.html | coordt/djangopypi | 1f955a339feadcf177b61d4da147bf63b4fcac61 | [
"BSD-3-Clause"
] | 1 | 2015-11-04T16:25:03.000Z | 2015-11-04T16:25:03.000Z | userpypi/templates/djangopypi/package_list.html | coordt/djangopypi | 1f955a339feadcf177b61d4da147bf63b4fcac61 | [
"BSD-3-Clause"
] | null | null | null | userpypi/templates/djangopypi/package_list.html | coordt/djangopypi | 1f955a339feadcf177b61d4da147bf63b4fcac61 | [
"BSD-3-Clause"
] | null | null | null | <html>
<head>
<title>Package Index</title>
</head>
<body>
<h1>Package Index</h1>
<ul>
{% for package in package_list %}
<li><a href="{{ package.get_absolute_url }}">{{ package.name }}</a>{% if package.latest and package.latest.summary %}: {{ package.latest.summary }}{% endif %}</li>
{% endfor %}
</ul>
</body>
</html> | 26.076923 | 166 | 0.59882 |
2a5d2f0928b16a949c590d11035eefb2b4c8e602 | 3,448 | java | Java | x-pack/plugin/eql/src/main/java/org/elasticsearch/xpack/eql/expression/function/scalar/string/StringContainsFunctionPipe.java | SaiKai/elasticsearch | 783d14d179e958fc54356191fa02532a893fb39e | [
"Apache-2.0"
] | 1 | 2021-01-24T16:47:06.000Z | 2021-01-24T16:47:06.000Z | x-pack/plugin/eql/src/main/java/org/elasticsearch/xpack/eql/expression/function/scalar/string/StringContainsFunctionPipe.java | SaiKai/elasticsearch | 783d14d179e958fc54356191fa02532a893fb39e | [
"Apache-2.0"
] | null | null | null | x-pack/plugin/eql/src/main/java/org/elasticsearch/xpack/eql/expression/function/scalar/string/StringContainsFunctionPipe.java | SaiKai/elasticsearch | 783d14d179e958fc54356191fa02532a893fb39e | [
"Apache-2.0"
] | 1 | 2021-06-12T11:34:52.000Z | 2021-06-12T11:34:52.000Z | /*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the Elastic License;
* you may not use this file except in compliance with the Elastic License.
*/
package org.elasticsearch.xpack.eql.expression.function.scalar.string;
import org.elasticsearch.xpack.ql.execution.search.QlSourceBuilder;
import org.elasticsearch.xpack.ql.expression.Expression;
import org.elasticsearch.xpack.ql.expression.gen.pipeline.Pipe;
import org.elasticsearch.xpack.ql.tree.NodeInfo;
import org.elasticsearch.xpack.ql.tree.Source;
import java.util.Arrays;
import java.util.List;
import java.util.Objects;
public class StringContainsFunctionPipe extends Pipe {
private final Pipe string, substring;
private final boolean isCaseSensitive;
public StringContainsFunctionPipe(Source source, Expression expression, Pipe string, Pipe substring, boolean isCaseSensitive) {
super(source, expression, Arrays.asList(string, substring));
this.string = string;
this.substring = substring;
this.isCaseSensitive = isCaseSensitive;
}
@Override
public final Pipe replaceChildren(List<Pipe> newChildren) {
return replaceChildren(newChildren.get(0), newChildren.get(1));
}
@Override
public final Pipe resolveAttributes(AttributeResolver resolver) {
Pipe newString = string.resolveAttributes(resolver);
Pipe newSubstring = substring.resolveAttributes(resolver);
if (newString == string && newSubstring == substring) {
return this;
}
return replaceChildren(newString, newSubstring);
}
@Override
public boolean supportedByAggsOnlyQuery() {
return string.supportedByAggsOnlyQuery() && substring.supportedByAggsOnlyQuery();
}
@Override
public boolean resolved() {
return string.resolved() && substring.resolved();
}
protected StringContainsFunctionPipe replaceChildren(Pipe string, Pipe substring) {
return new StringContainsFunctionPipe(source(), expression(), string, substring, isCaseSensitive);
}
@Override
public final void collectFields(QlSourceBuilder sourceBuilder) {
string.collectFields(sourceBuilder);
substring.collectFields(sourceBuilder);
}
@Override
protected NodeInfo<StringContainsFunctionPipe> info() {
return NodeInfo.create(this, StringContainsFunctionPipe::new, expression(), string, substring, isCaseSensitive);
}
@Override
public StringContainsFunctionProcessor asProcessor() {
return new StringContainsFunctionProcessor(string.asProcessor(), substring.asProcessor(), isCaseSensitive);
}
public Pipe string() {
return string;
}
public Pipe substring() {
return substring;
}
protected boolean isCaseSensitive() {
return isCaseSensitive;
}
@Override
public int hashCode() {
return Objects.hash(string(), substring());
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (obj == null || getClass() != obj.getClass()) {
return false;
}
StringContainsFunctionPipe other = (StringContainsFunctionPipe) obj;
return Objects.equals(string(), other.string())
&& Objects.equals(substring(), other.substring());
}
}
| 31.925926 | 131 | 0.697216 |
3e2aaa5fd8e7e8606bc5ad6b456ed7a81a8b9ea3 | 6,437 | h | C | dependancies/include/gtkmm/pangomm/cairofontmap.h | Illation/synthesizer | da77d55c1c69829bbad76d0c14b9c56a5261b642 | [
"MIT"
] | 2 | 2020-03-24T09:46:35.000Z | 2020-06-16T01:42:46.000Z | dependancies/include/gtkmm/pangomm/cairofontmap.h | Illation/synthesizer | da77d55c1c69829bbad76d0c14b9c56a5261b642 | [
"MIT"
] | null | null | null | dependancies/include/gtkmm/pangomm/cairofontmap.h | Illation/synthesizer | da77d55c1c69829bbad76d0c14b9c56a5261b642 | [
"MIT"
] | null | null | null | // Generated by gmmproc 2.49.5 -- DO NOT MODIFY!
#ifndef _PANGOMM_CAIROFONTMAP_H
#define _PANGOMM_CAIROFONTMAP_H
#include <glibmm/ustring.h>
#include <sigc++/sigc++.h>
/* $Id: cairofontmap.hg,v 1.1 2006/05/30 17:14:21 murrayc Exp $ */
/* fontmap.h
*
* Copyright 2001 The gtkmm Development Team
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free
* Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
#include <glibmm/interface.h>
#include <pangomm/context.h>
#include <pango/pangocairo.h>
#ifndef DOXYGEN_SHOULD_SKIP_THIS
typedef struct _PangoCairoFontMapIface PangoCairoFontMapIface;
#endif
#ifndef DOXYGEN_SHOULD_SKIP_THIS
using PangoCairoFontMap = struct _PangoCairoFontMap;
using PangoCairoFontMapClass = struct _PangoCairoFontMapClass;
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
#ifndef DOXYGEN_SHOULD_SKIP_THIS
namespace Pango
{ class CairoFontMap_Class; } // namespace Pango
#endif // DOXYGEN_SHOULD_SKIP_THIS
namespace Pango
{
class Context;
/** A Pango::CairoFontMap represents the set of fonts available for a particular rendering system.
*/
class CairoFontMap : public Glib::Interface
{
#ifndef DOXYGEN_SHOULD_SKIP_THIS
public:
using CppObjectType = CairoFontMap;
using CppClassType = CairoFontMap_Class;
using BaseObjectType = PangoCairoFontMap;
using BaseClassType = PangoCairoFontMapIface;
// noncopyable
CairoFontMap(const CairoFontMap&) = delete;
CairoFontMap& operator=(const CairoFontMap&) = delete;
private:
friend class CairoFontMap_Class;
static CppClassType cairofontmap_class_;
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
protected:
/**
* You should derive from this class to use it.
*/
CairoFontMap();
#ifndef DOXYGEN_SHOULD_SKIP_THIS
/** Called by constructors of derived classes. Provide the result of
* the Class init() function to ensure that it is properly
* initialized.
*
* @param interface_class The Class object for the derived type.
*/
explicit CairoFontMap(const Glib::Interface_Class& interface_class);
public:
// This is public so that C++ wrapper instances can be
// created for C instances of unwrapped types.
// For instance, if an unexpected C type implements the C interface.
explicit CairoFontMap(PangoCairoFontMap* castitem);
protected:
#endif /* DOXYGEN_SHOULD_SKIP_THIS */
public:
CairoFontMap(CairoFontMap&& src) noexcept;
CairoFontMap& operator=(CairoFontMap&& src) noexcept;
~CairoFontMap() noexcept override;
static void add_interface(GType gtype_implementer);
/** Get the GType for this class, for use with the underlying GObject type system.
*/
static GType get_type() G_GNUC_CONST;
#ifndef DOXYGEN_SHOULD_SKIP_THIS
static GType get_base_type() G_GNUC_CONST;
#endif
///Provides access to the underlying C GObject.
PangoCairoFontMap* gobj() { return reinterpret_cast<PangoCairoFontMap*>(gobject_); }
///Provides access to the underlying C GObject.
const PangoCairoFontMap* gobj() const { return reinterpret_cast<PangoCairoFontMap*>(gobject_); }
private:
public:
//_WRAP_METHOD(static Glib::RefPtr<PangoFontMap> get_default(), pango_cairo_font_map_get_default) //TODO: ref this?
/** Sets a default Pango::CairoFontMap to use with Cairo.
*
* This can be used to change the Cairo font backend that the
* default fontmap uses for example. The old default font map
* is unreffed and the new font map referenced.
*
* Note that since Pango 1.32.6, the default fontmap is per-thread.
* This function only changes the default fontmap for
* the current thread. Default fontmaps of exisiting threads
* are not changed. Default fontmaps of any new threads will
* still be created using new().
*
* A value of <tt>nullptr</tt> for @a fontmap will cause the current default
* font map to be released and a new default font
* map to be created on demand, using new().
*
* @newin{1,22}
*/
void set_default();
/** Gets the type of Cairo font backend that @a fontmap uses.
*
* @newin{1,18}
*
* @return The #cairo_font_type_t cairo font backend type.
*/
Cairo::FontType get_font_type() const;
/** Sets the resolution for the fontmap. This is a scale factor between
* points specified in a Pango::FontDescription and Cairo units. The
* default value is 96, meaning that a 10 point font will be 13
* units high. (10 * 96. / 72. = 13.3).
*
* @newin{1,10}
*
* @param dpi The resolution in "dots per inch". (Physical inches aren't actually
* involved; the terminology is conventional.).
*/
void set_resolution(double dpi);
/** Gets the resolution for the fontmap. See set_resolution()
*
* @newin{1,10}
*
* @return The resolution in "dots per inch".
*/
double get_resolution() const;
// TODO: Remove this when we can break ABI.
/** Create a Pango::Context for the given fontmap.
*
* @newin{1,10}
*
* Deprecated: 1.22: Use Pango::FontMap::create_context() instead.
*
* @return The newly created context; free with Glib::object_unref().
*/
Glib::RefPtr<Context> create_context();
public:
public:
//C++ methods used to invoke GTK+ virtual functions:
protected:
//GTK+ Virtual Functions (override these to change behaviour):
//Default Signal Handlers::
};
} // namespace Pango
namespace Glib
{
/** A Glib::wrap() method for this object.
*
* @param object The C instance.
* @param take_copy False if the result should take ownership of the C instance. True if it should take a new copy or ref.
* @result A C++ instance that wraps this C instance.
*
* @relates Pango::CairoFontMap
*/
Glib::RefPtr<Pango::CairoFontMap> wrap(PangoCairoFontMap* object, bool take_copy = false);
} // namespace Glib
#endif /* _PANGOMM_CAIROFONTMAP_H */
| 28.232456 | 124 | 0.720367 |
3f5b35f73b2b7de228a0d09bdee452db28660845 | 437 | sql | SQL | 02-Joins/5-Composite_primary_key.sql | ramapinnimty/SQL-Vault | ebe08e5cd24efa71b231c6b4a29d11d6481c28bf | [
"MIT"
] | null | null | null | 02-Joins/5-Composite_primary_key.sql | ramapinnimty/SQL-Vault | ebe08e5cd24efa71b231c6b4a29d11d6481c28bf | [
"MIT"
] | null | null | null | 02-Joins/5-Composite_primary_key.sql | ramapinnimty/SQL-Vault | ebe08e5cd24efa71b231c6b4a29d11d6481c28bf | [
"MIT"
] | null | null | null | -- There will be times when we cannot uniquely identify records in a given table (i.e, where there is no primary key).
-- Thus, in such scenarios, we use a combination of these columns to uniquely identify records.
-- This is called a "Composite Primary Key" that contains more than one column.
USE sql_store;
SELECT *
FROM order_items oi
JOIN order_item_notes oin
ON oi.order_id = oin.order_id
AND oi.product_id = oin.product_id; | 39.727273 | 118 | 0.76659 |
8d0ad64076043165e891d27ab42589724f3a2a91 | 424 | lua | Lua | Un-identified/3/Programs/arun.lua | RamiLego4Game/LIKO-12-Projects | 846ccc1d95f98d1b5ec791003438ea309300a116 | [
"MIT"
] | 3 | 2017-08-02T10:11:03.000Z | 2017-11-06T20:52:45.000Z | Appdata/Programs/arun.lua | Rami-Sabbagh/BatteryMan | 3dc86c294c3f12077ad72d83744becbaf8961041 | [
"MIT"
] | null | null | null | Appdata/Programs/arun.lua | Rami-Sabbagh/BatteryMan | 3dc86c294c3f12077ad72d83744becbaf8961041 | [
"MIT"
] | null | null | null | local term = require("terminal")
term.execute("save") flip()
term.execute("tileset") flip()
clear(0)
printCursor(0,0,0)
while true do
cam()
pal()
palt()
printCursor(false,false,0)
color(7)
print("Press any key to run")
for event, key in pullEvent do
if event == "keypressed" then
if key == "escape" then return end
break
end
end
term.execute("tiled")
flip()
term.execute("run")
end | 18.434783 | 40 | 0.641509 |
bedc8c7c8435174ff581ad69ffcd04e681f7025d | 102 | kt | Kotlin | flank-scripts/src/main/kotlin/flank/scripts/utils/FileUtils.kt | jan-gogo/flank | 9d1ba8db43e76807ffd1578122005eef60490a08 | [
"Apache-2.0"
] | null | null | null | flank-scripts/src/main/kotlin/flank/scripts/utils/FileUtils.kt | jan-gogo/flank | 9d1ba8db43e76807ffd1578122005eef60490a08 | [
"Apache-2.0"
] | 1 | 2020-12-11T14:04:24.000Z | 2020-12-11T14:04:50.000Z | flank-scripts/src/main/kotlin/flank/scripts/utils/FileUtils.kt | jan-gogo/flank | 9d1ba8db43e76807ffd1578122005eef60490a08 | [
"Apache-2.0"
] | null | null | null | package flank.scripts.utils
internal fun String.withNewLineAtTheEnd() = plus(System.lineSeparator())
| 25.5 | 72 | 0.813725 |
75e456b76dbcade9447d541d54c53e98f98f364c | 1,312 | php | PHP | resources/views/front/partials/emoje/about.blade.php | NohaElMandoh/genenaMall | 6fba23011ef3323d077090c7210db3e93dd92dbd | [
"MIT"
] | null | null | null | resources/views/front/partials/emoje/about.blade.php | NohaElMandoh/genenaMall | 6fba23011ef3323d077090c7210db3e93dd92dbd | [
"MIT"
] | null | null | null | resources/views/front/partials/emoje/about.blade.php | NohaElMandoh/genenaMall | 6fba23011ef3323d077090c7210db3e93dd92dbd | [
"MIT"
] | null | null | null | <div class="emojabout">
<div class="container">
<div class="emojaboutcontent">
<div class="row">
<div class="col-lg-7 col-md-7 col-sm-6 col-xs-12">
<div class="emojaboutdetparg">
{{-- <p>Let your children have an unforgettable spring break with our endless entertainment offerings, fascinating quests, educational and physical activities.</p>
<p>Say goodbye to the boredom of the school classroom. At Emoji, every day will start with a workout, breakfast at the Foodmama restaurant, and games with camp leaders.</p>
<p>Then the children have a rich program: active games, ice skating, children's yoga, flying in a wind tunnel and much more useful and exciting. In the afternoon, the guys are fed a delicious lunch, afternoon snack and
dinner.</p> --}}
<p> {!!$emoje->desc!!} </p>
</div>
</div>
<div class="col-lg-5 col-md-5 col-sm-6 col-xs-12">
<div class="images">
<img src="{{asset('front/images/slider/logotype.svg')}}">
</div>
</div>
</div>
</div>
</div>
</div> | 59.636364 | 242 | 0.528201 |
177a15b293d206ee9b00073a2623875d0e632ecf | 283 | kt | Kotlin | src/commonMain/kotlin/com/bkahlert/kommons/io/InMemoryFile.kt | bkahlert/koodies | 35e2ac1c4246decdf7e7a1160bfdd5c9e28fd066 | [
"MIT"
] | 7 | 2020-12-20T10:47:06.000Z | 2021-08-03T14:21:57.000Z | src/commonMain/kotlin/com/bkahlert/kommons/io/InMemoryFile.kt | bkahlert/koodies | 35e2ac1c4246decdf7e7a1160bfdd5c9e28fd066 | [
"MIT"
] | 42 | 2021-08-25T16:22:09.000Z | 2022-03-21T16:22:37.000Z | src/commonMain/kotlin/com/bkahlert/kommons/io/InMemoryFile.kt | bkahlert/koodies | 35e2ac1c4246decdf7e7a1160bfdd5c9e28fd066 | [
"MIT"
] | null | null | null | package com.bkahlert.kommons.io
/**
* A file stored purely in-memory.
*/
public interface InMemoryFile {
/**
* Name of this in-memory file.
*/
public val name: String
/**
* Data this in-memory file consists of.
*/
public val data: ByteArray
}
| 16.647059 | 44 | 0.607774 |
8a0f3e046ba90c73b3a136ed380bf6f69d51ed23 | 885 | rs | Rust | dsf_precompile/src/structs.rs | saveriomiroddi/dwarf_seeks_fortune-dev | 651b7970da38cabeac223b775dd293ed265e680d | [
"BlueOak-1.0.0"
] | null | null | null | dsf_precompile/src/structs.rs | saveriomiroddi/dwarf_seeks_fortune-dev | 651b7970da38cabeac223b775dd293ed265e680d | [
"BlueOak-1.0.0"
] | null | null | null | dsf_precompile/src/structs.rs | saveriomiroddi/dwarf_seeks_fortune-dev | 651b7970da38cabeac223b775dd293ed265e680d | [
"BlueOak-1.0.0"
] | null | null | null | use amethyst::{
animation::AnimationSetPrefab,
assets::{PrefabData, ProgressCounter},
derive::PrefabData,
ecs::{
prelude::{Component, Entity},
DenseVecStorage,
},
error::Error,
renderer::sprite::{prefab::SpriteScenePrefab, SpriteRender},
};
use serde::{Deserialize, Serialize};
/// Animation ids used in a AnimationSet
#[derive(Eq, PartialOrd, PartialEq, Hash, Debug, Copy, Clone, Deserialize, Serialize)]
pub enum AnimationId {
Fly,
}
/// Loading data for one entity
#[derive(Debug, Clone, Deserialize, PrefabData)]
pub struct MyPrefabData {
/// Information for rendering a scene with sprites
sprite_scene: SpriteScenePrefab,
/// Аll animations that can be run on the entity
animation_set: AnimationSetPrefab<AnimationId, SpriteRender>,
}
impl Component for MyPrefabData {
type Storage = DenseVecStorage<Self>;
}
| 27.65625 | 86 | 0.708475 |
7ff7033d4d2e6edb87d7aa49bb130ff4316c2e04 | 731 | go | Go | routes/routes.go | tjtaill/gin-gonic-spike | 3ff7b70b952ce10928cdf369b355001c9a3c3902 | [
"MIT"
] | null | null | null | routes/routes.go | tjtaill/gin-gonic-spike | 3ff7b70b952ce10928cdf369b355001c9a3c3902 | [
"MIT"
] | null | null | null | routes/routes.go | tjtaill/gin-gonic-spike | 3ff7b70b952ce10928cdf369b355001c9a3c3902 | [
"MIT"
] | null | null | null | package routes
import (
_ "github.com/ElementAI/gin-gonic-spike/docs"
"github.com/ElementAI/gin-gonic-spike/middleware"
"github.com/gin-gonic/gin"
"github.com/jinzhu/gorm"
ginSwagger "github.com/swaggo/gin-swagger"
"github.com/swaggo/gin-swagger/swaggerFiles"
)
func Register(router *gin.Engine, db *gorm.DB) error {
authMiddleware, rbacMiddleware, err := middleware.Create(db)
if err != nil {
return err
}
url := ginSwagger.URL("http://localhost:8080/docs/doc.json")
router.GET("/docs/*any", ginSwagger.WrapHandler(swaggerFiles.Handler, url))
loginRoutes(router, authMiddleware)
api := router.Group("/api/v1")
api.Use(authMiddleware.MiddlewareFunc())
api.Use(*rbacMiddleware)
userRoutes(api, db)
return nil
}
| 28.115385 | 76 | 0.744186 |
8260583008770db666cf01cc04a49da723c4b3b2 | 229 | sql | SQL | app/lib/db_query/sql/profilesMostFollowers.sql | MichaelCurrin/twitterverse | 9629f848377e4346be833db70f11c593cc0d7b6c | [
"MIT"
] | 10 | 2019-03-22T07:07:41.000Z | 2022-01-26T00:57:45.000Z | app/lib/db_query/sql/profilesMostFollowers.sql | MichaelCurrin/twitterverse | 9629f848377e4346be833db70f11c593cc0d7b6c | [
"MIT"
] | 70 | 2017-07-12T19:49:38.000Z | 2020-09-02T10:03:28.000Z | app/lib/db_query/sql/profilesMostFollowers.sql | MichaelCurrin/twitterverse | 9629f848377e4346be833db70f11c593cc0d7b6c | [
"MIT"
] | 2 | 2017-06-30T07:13:39.000Z | 2020-12-04T00:39:12.000Z | /**
* Twitter Profiles, ordered by highest followers first.
*/
SELECT
screen_name,
name,
verified,
followers_count,
statuses_count,
location,
description
FROM Profile
ORDER BY followers_count DESC;
| 15.266667 | 56 | 0.694323 |
21c0f4fcee6c878e01201d4660ade4e1b49402bc | 637 | kt | Kotlin | app/src/main/java/com/example/admin/bigkt/TestWhen.kt | DNF229298806/BigKtDemo | 761ed216197c2391e54deaeb60f4b77559237c0e | [
"Apache-2.0"
] | null | null | null | app/src/main/java/com/example/admin/bigkt/TestWhen.kt | DNF229298806/BigKtDemo | 761ed216197c2391e54deaeb60f4b77559237c0e | [
"Apache-2.0"
] | null | null | null | app/src/main/java/com/example/admin/bigkt/TestWhen.kt | DNF229298806/BigKtDemo | 761ed216197c2391e54deaeb60f4b77559237c0e | [
"Apache-2.0"
] | null | null | null | package com.example.admin.bigkt
/**
* @author Richard_Y_Wang
* @des 2018/12/13 21:36
*/
var opCount = 0
val UNIX_LINE_SEPARAOR = "\n"
const val UNIX_LINE_SEPARAOR1 = "???"
fun main(args: Array<String>) {
for (i in 1..10) {
when {
i % 2 == 0 || i % 3 == 0 -> println("i是偶数")
i % 10 == 0 -> println("i被10整除")
}
}
val list = listOf("1", "2", "3", "4")
println(joinToString(list))
val testString = "Kotlin"
//Java
TestWhen.lastChar(testString)
//Kotlin
testString.lastChar()
println(testString.lastChar())
}
fun String.lastChar(): Char = get(length - 1)
| 21.233333 | 55 | 0.565149 |
261510acf3e735c92b410f552f23c06c5911055b | 8,683 | java | Java | java/testng.ui/src/org/netbeans/modules/testng/ui/wizards/NewTestSuiteWizardIterator.java | timfel/netbeans | fa4b0f70def0573f9675fc06108e13b8b6c49c0e | [
"Apache-2.0"
] | 1,056 | 2019-04-25T20:00:35.000Z | 2022-03-30T04:46:14.000Z | java/testng.ui/src/org/netbeans/modules/testng/ui/wizards/NewTestSuiteWizardIterator.java | timfel/netbeans | fa4b0f70def0573f9675fc06108e13b8b6c49c0e | [
"Apache-2.0"
] | 1,846 | 2019-04-25T20:50:05.000Z | 2022-03-31T23:40:41.000Z | java/testng.ui/src/org/netbeans/modules/testng/ui/wizards/NewTestSuiteWizardIterator.java | timfel/netbeans | fa4b0f70def0573f9675fc06108e13b8b6c49c0e | [
"Apache-2.0"
] | 550 | 2019-04-25T20:04:33.000Z | 2022-03-25T17:43:01.000Z | /*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/
package org.netbeans.modules.testng.ui.wizards;
import java.awt.Component;
import java.io.IOException;
import java.util.*;
import javax.swing.JComponent;
import javax.swing.event.ChangeListener;
import org.netbeans.api.java.project.JavaProjectConstants;
import org.netbeans.api.project.*;
import org.netbeans.api.templates.TemplateRegistration;
import org.netbeans.modules.testng.api.TestNGSupport;
import org.netbeans.spi.java.project.support.ui.templates.JavaTemplates;
import org.netbeans.spi.project.ui.templates.support.Templates;
import org.openide.WizardDescriptor;
import org.openide.filesystems.FileObject;
import org.openide.filesystems.FileUtil;
import org.openide.loaders.DataFolder;
import org.openide.loaders.DataObject;
import org.openide.util.NbBundle;
@TemplateRegistration(folder = "UnitTests", position = 1200,
content = "../resources/TestNGSuite.xml.template",
scriptEngine = "freemarker",
displayName = "#TestNGSuite_displayName",
description = "/org/netbeans/modules/testng/ui/resources/newTestSuite.html",
iconBase = "org/netbeans/modules/testng/ui/resources/testng.gif",
category="junit")
@NbBundle.Messages("TestNGSuite_displayName=TestNG Test Suite")
public final class NewTestSuiteWizardIterator implements WizardDescriptor.InstantiatingIterator<WizardDescriptor> {
private transient int index;
private transient WizardDescriptor.Panel[] panels;
private transient WizardDescriptor wiz;
public NewTestSuiteWizardIterator() {
}
private WizardDescriptor.Panel[] createPanels(final WizardDescriptor wizardDescriptor) {
// Ask for Java folders
Project project = Templates.getProject(wizardDescriptor);
Sources sources = ProjectUtils.getSources(project);
SourceGroup[] groups = getTestRoots(sources);
if (groups.length == 0) {
if (SourceGroupModifier.createSourceGroup(project, JavaProjectConstants.SOURCES_TYPE_JAVA, JavaProjectConstants.SOURCES_HINT_TEST) != null) {
groups = getTestRoots(sources);
}
}
if (groups.length == 0) {
groups = sources.getSourceGroups(Sources.TYPE_GENERIC);
}
return new WizardDescriptor.Panel[]{
JavaTemplates.createPackageChooser(project, groups)
};
}
private String[] createSteps(String[] before, WizardDescriptor.Panel[] panels) {
assert panels != null;
// hack to use the steps set before this panel processed
int diff = 0;
if (before == null) {
before = new String[0];
} else if (before.length > 0) {
diff = ("...".equals(before[before.length - 1])) ? 1 : 0; // NOI18N
}
String[] res = new String[(before.length - diff) + panels.length];
for (int i = 0; i < res.length; i++) {
if (i < (before.length - diff)) {
res[i] = before[i];
} else {
res[i] = panels[i - before.length + diff].getComponent().getName();
}
}
return res;
}
public Set<DataObject> instantiate() throws IOException {
FileObject targetFolder = Templates.getTargetFolder(wiz);
TestNGSupport.findTestNGSupport(FileOwnerQuery.getOwner(targetFolder)).configureProject(targetFolder);
String targetName = Templates.getTargetName(wiz);
DataFolder df = DataFolder.findFolder(targetFolder);
FileObject template = Templates.getTemplate(wiz);
DataObject dTemplate = DataObject.find(template);
String pkgName = getSelectedPackageName(targetFolder);
String suiteName = pkgName + " suite";
String projectName = ProjectUtils.getInformation(FileOwnerQuery.getOwner(targetFolder)).getName();
if (pkgName == null || pkgName.trim().length() < 1) {
pkgName = ".*"; //NOI18N
suiteName = "All tests for " + projectName;
}
Map<String, String> props = new HashMap<String, String>();
props.put("suiteName", projectName);
props.put("testName", suiteName);
props.put("pkg", pkgName);
DataObject dobj = dTemplate.createFromTemplate(df, targetName, props);
return Collections.singleton(dobj);
}
public void initialize(WizardDescriptor wiz) {
this.wiz = wiz;
index = 0;
panels = createPanels(wiz);
// Make sure list of steps is accurate.
String[] beforeSteps = null;
Object prop = wiz.getProperty("WizardPanel_contentData"); // NOI18N
if (prop != null && prop instanceof String[]) {
beforeSteps = (String[]) prop;
}
String[] steps = createSteps(beforeSteps, panels);
for (int i = 0; i < panels.length; i++) {
Component c = panels[i].getComponent();
if (steps[i] == null) {
// Default step name to component name of panel.
// Mainly useful for getting the name of the target
// chooser to appear in the list of steps.
steps[i] = c.getName();
}
if (c instanceof JComponent) { // assume Swing components
JComponent jc = (JComponent) c;
// Step #.
jc.putClientProperty("WizardPanel_contentSelectedIndex", Integer.valueOf(i)); // NOI18N
// Step name (actually the whole list for reference).
jc.putClientProperty("WizardPanel_contentData", steps); // NOI18N
}
}
}
public void uninitialize(WizardDescriptor wiz) {
this.wiz = null;
panels = null;
}
public String name() {
return ""; // NOI18N
}
public boolean hasNext() {
return index < panels.length - 1;
}
public boolean hasPrevious() {
return index > 0;
}
public void nextPanel() {
if (!hasNext()) {
throw new NoSuchElementException();
}
index++;
}
public void previousPanel() {
if (!hasPrevious()) {
throw new NoSuchElementException();
}
index--;
}
public WizardDescriptor.Panel current() {
return panels[index];
}
public final void addChangeListener(ChangeListener l) {
}
public final void removeChangeListener(ChangeListener l) {
}
private static String getSelectedPackageName(FileObject targetFolder) {
Project project = FileOwnerQuery.getOwner(targetFolder);
Sources sources = ProjectUtils.getSources(project);
SourceGroup[] groups = sources.getSourceGroups(JavaProjectConstants.SOURCES_TYPE_JAVA);
String packageName = null;
for (int i = 0; i < groups.length && packageName == null; i++) {
packageName = FileUtil.getRelativePath(groups[i].getRootFolder(), targetFolder);
}
if (packageName != null) {
packageName = packageName.replaceAll("/", "."); // NOI18N
}
return packageName;
}
private SourceGroup[] getTestRoots(Sources srcs) {
SourceGroup[] groups = srcs.getSourceGroups(JavaProjectConstants.SOURCES_TYPE_JAVA);
assert groups != null : "Cannot return null from Sources.getSourceGroups: " + srcs;
//XXX - have to filter out regular source roots, there should
//be better way to do this... (Hint: use UnitTestForSourceQuery)
//${test - Ant based projects
//2TestSourceRoot - Maven projects
List<SourceGroup> result = new ArrayList<SourceGroup>(2);
for (SourceGroup sg : groups) {
if (sg.getName().startsWith("${test") || "2TestSourceRoot".equals(sg.getName())) { //NOI18N
result.add(sg);
}
}
return result.toArray(new SourceGroup[result.size()]);
}
}
| 38.93722 | 153 | 0.640908 |
6239db5b2fbb86f00b1ae95e8fd80dc2ffb3d863 | 5,970 | rs | Rust | src/signature/mod.rs | joseluis/bsv-wasm | 67ce940c7c16479257898fa67e414f8a455677a3 | [
"MIT"
] | null | null | null | src/signature/mod.rs | joseluis/bsv-wasm | 67ce940c7c16479257898fa67e414f8a455677a3 | [
"MIT"
] | null | null | null | src/signature/mod.rs | joseluis/bsv-wasm | 67ce940c7c16479257898fa67e414f8a455677a3 | [
"MIT"
] | null | null | null | use crate::{get_hash_digest, BSVErrors, PublicKey, Sha256r, SigningHash, ECDSA};
use digest::Digest;
use ecdsa::signature::{DigestVerifier, Signature as SigTrait};
use elliptic_curve::sec1::*;
use k256::{
ecdsa::Signature as SecpSignature,
ecdsa::{recoverable, signature::Verifier, VerifyingKey},
EncodedPoint, FieldBytes, Scalar,
};
use wasm_bindgen::{convert::OptionIntoWasmAbi, prelude::*, throw_str};
#[wasm_bindgen]
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct Signature {
pub(crate) sig: k256::ecdsa::Signature,
pub(crate) recovery_i: u8,
}
/**
* Implementation Methods
*/
impl Signature {
pub(crate) fn from_der_impl(bytes: &[u8], is_recoverable: bool) -> Result<Signature, BSVErrors> {
let sig = SecpSignature::from_der(bytes)?;
Ok(Signature {
sig,
recovery_i: is_recoverable as u8,
})
}
pub(crate) fn from_hex_der_impl(hex: &str, is_recoverable: bool) -> Result<Signature, BSVErrors> {
let bytes = hex::decode(hex)?;
let sig = SecpSignature::from_der(&bytes)?;
Ok(Signature {
sig,
recovery_i: is_recoverable as u8,
})
}
pub(crate) fn get_public_key(&self, message: &[u8], hash_algo: SigningHash) -> Result<PublicKey, BSVErrors> {
let recovery_id_main = match recoverable::Id::new(self.recovery_i) {
Ok(v) => v,
Err(e) => {
return Err(BSVErrors::PublicKeyRecoveryError(
format!("Recovery I ({}) is too large, must be 0 or 1 for this library. {}", self.recovery_i, e),
None,
))
}
};
let recoverable_sig = recoverable::Signature::new(&self.sig, recovery_id_main)?;
let message_digest = get_hash_digest(hash_algo, message);
let verify_key = match recoverable_sig.recover_verify_key_from_digest(message_digest) {
Ok(v) => v,
Err(e) => {
return Err(BSVErrors::PublicKeyRecoveryError(format!("Signature Hex: {} Id: {}", self.to_hex(), self.recovery_i), Some(e)));
}
};
let pub_key = PublicKey::from_bytes_impl(&verify_key.to_bytes().to_vec())?;
Ok(pub_key)
}
pub(crate) fn from_compact_impl(compact_bytes: &[u8]) -> Result<Signature, BSVErrors> {
// 27-30: P2PKH uncompressed
// 31-34: P2PKH compressed
let i = match compact_bytes[0] - 27 {
x if x > 4 => x - 4,
x => x,
};
let r = Scalar::from_bytes_reduced(FieldBytes::from_slice(&compact_bytes[1..33]));
let s = Scalar::from_bytes_reduced(FieldBytes::from_slice(&compact_bytes[33..65]));
let sig = SecpSignature::from_scalars(r, s)?;
Ok(Signature { sig, recovery_i: i })
}
}
#[wasm_bindgen]
impl Signature {
#[wasm_bindgen(js_name = toHex)]
pub fn to_hex(&self) -> String {
let bytes = self.sig.to_der();
hex::encode(bytes)
}
#[wasm_bindgen(js_name = toDER)]
pub fn to_der_bytes(&self) -> Vec<u8> {
let bytes = self.sig.to_der();
bytes.as_bytes().to_vec()
}
#[wasm_bindgen(js_name = toCompactBytes)]
pub fn to_compact_bytes(&self) -> Vec<u8> {
// Need to handle compression?
let mut compact_buf = vec![self.recovery_i + 27 + 4];
let r_bytes = &*self.sig.r().to_bytes();
compact_buf.extend_from_slice(r_bytes);
let s_bytes = &*self.sig.s().to_bytes();
compact_buf.extend_from_slice(s_bytes);
compact_buf
}
#[wasm_bindgen(js_name = verifyMessage)]
pub fn verify_message(&self, message: &[u8], pub_key: &PublicKey) -> bool {
ECDSA::verify_digest_impl(message, pub_key, self, SigningHash::Sha256).is_ok()
}
}
/**
* WASM Exported Methods
*/
#[cfg_attr(target_arch = "wasm32", wasm_bindgen)]
#[cfg(target_arch = "wasm32")]
impl Signature {
#[cfg_attr(target_arch = "wasm32", wasm_bindgen(js_name = fromDER))]
pub fn from_der(bytes: &[u8], is_recoverable: bool) -> Result<Signature, JsValue> {
Signature::from_der_impl(bytes, is_recoverable).map_err(|e| throw_str(&e.to_string()))
}
#[cfg_attr(target_arch = "wasm32", wasm_bindgen(js_name = fromHexDER))]
pub fn from_hex_der(hex: &str, is_recoverable: bool) -> Result<Signature, JsValue> {
match Signature::from_hex_der_impl(hex, is_recoverable) {
Ok(v) => Ok(v),
Err(e) => throw_str(&e.to_string()),
}
}
#[wasm_bindgen(js_name = fromCompactBytes)]
pub fn from_compact_bytes(compact_bytes: &[u8]) -> Result<Signature, JsValue> {
match Signature::from_compact_impl(compact_bytes) {
Ok(v) => Ok(v),
Err(e) => throw_str(&e.to_string()),
}
}
#[wasm_bindgen(js_name = recoverPublicKey)]
pub fn recover_public_key(&self, message: &[u8], hash_algo: SigningHash) -> Result<PublicKey, JsValue> {
match Signature::get_public_key(&self, &message, hash_algo) {
Ok(v) => Ok(v),
Err(e) => throw_str(&e.to_string()),
}
}
}
/**
* Native Exported Methods
*/
#[cfg(not(target_arch = "wasm32"))]
impl Signature {
#[cfg(not(target_arch = "wasm32"))]
pub fn from_der(bytes: &[u8], is_recoverable: bool) -> Result<Signature, BSVErrors> {
Signature::from_der_impl(bytes, is_recoverable)
}
#[cfg(not(target_arch = "wasm32"))]
pub fn from_hex_der(hex: &str, is_recoverable: bool) -> Result<Signature, BSVErrors> {
Signature::from_hex_der_impl(hex, is_recoverable)
}
pub fn from_compact_bytes(compact_bytes: &[u8]) -> Result<Signature, BSVErrors> {
Signature::from_compact_impl(compact_bytes)
}
#[cfg(not(target_arch = "wasm32"))]
pub fn recover_public_key(&self, message: &[u8], hash_algo: SigningHash) -> Result<PublicKey, BSVErrors> {
Signature::get_public_key(self, message, hash_algo)
}
}
| 33.166667 | 140 | 0.616248 |
f187bccdccead7b69c1c784e654ec309458e1557 | 130 | rb | Ruby | db/migrate/20141018175154_remove_hidden_note_from_gifts.rb | aligature/wishlist | 66b6acc4f13c53e56ef984f151d1a40fb769f7f8 | [
"Apache-2.0"
] | null | null | null | db/migrate/20141018175154_remove_hidden_note_from_gifts.rb | aligature/wishlist | 66b6acc4f13c53e56ef984f151d1a40fb769f7f8 | [
"Apache-2.0"
] | null | null | null | db/migrate/20141018175154_remove_hidden_note_from_gifts.rb | aligature/wishlist | 66b6acc4f13c53e56ef984f151d1a40fb769f7f8 | [
"Apache-2.0"
] | 1 | 2018-07-28T00:49:59.000Z | 2018-07-28T00:49:59.000Z | class RemoveHiddenNoteFromGifts < ActiveRecord::Migration
def change
remove_column :gifts, :hidden_note, :string
end
end
| 21.666667 | 57 | 0.776923 |
969366fdb0c58035e879b7911e81e7405072bd20 | 1,482 | html | HTML | src/app/shared/layout/header/notification-modal/notification-modal.component.html | IT-Academy-Social-Projects-KRV/Mentor4you_Angular | 2147629691bbd14352c1d8e4ff3fa320dca42702 | [
"MIT"
] | null | null | null | src/app/shared/layout/header/notification-modal/notification-modal.component.html | IT-Academy-Social-Projects-KRV/Mentor4you_Angular | 2147629691bbd14352c1d8e4ff3fa320dca42702 | [
"MIT"
] | 1 | 2021-08-24T10:07:38.000Z | 2021-08-24T10:07:38.000Z | src/app/shared/layout/header/notification-modal/notification-modal.component.html | IT-Academy-Social-Projects-KRV/Mentor4you_Angular | 2147629691bbd14352c1d8e4ff3fa320dca42702 | [
"MIT"
] | null | null | null | <ng-container *ngIf="display$ | async as display">
<div class="container">
<section [class.open]="display === 'open'" (click)="close()">
<div (click)="$event.stopPropagation()">
<button class="close" type="button" (click)="close()"><i class="fas fa-times"></i></button>
<h1 class="mentorship-request__title">Requesting mentorship</h1>
<hr>
<ng-container *ngIf="modalService.isNewNotification$ | async; else NoNotification">
<div class="mentorship-request__users">
<ng-container *ngIf="auth.isMentor">
<app-mentorship-request
*ngFor="let mentee of mentees$ | async"
[mentee]="mentee"
(approveIgnoreRequest)="approveIgnoreRequest($event)"
>
</app-mentorship-request>
</ng-container>
<ng-container *ngIf="auth.isMentee">
<app-mentorship-approve
*ngFor="let mentor of mentors$ | async"
[mentorCooperation]="mentor"
(moderatorDetails)="moderatorNavigate($event)"
(deleteNotificationRequest)="deleteNotificationRequest($event)"
>
</app-mentorship-approve>
</ng-container>
</div>
</ng-container>
<ng-template #NoNotification>
<h2 class="no-request">No new notifications</h2>
</ng-template>
</div>
</section>
</div>
</ng-container>
| 41.166667 | 99 | 0.556005 |
2a4740db2d03b8956d397623306afc662654e50b | 404 | java | Java | sample/app/src/main/java/demo/jaop/sample/Zoo.java | ltshddx/jaop | 190385523cd48fc90b32d5f1f2bae9f9e3c5ba84 | [
"Apache-2.0"
] | 125 | 2015-12-31T13:26:58.000Z | 2021-12-09T03:44:43.000Z | sample/app/src/main/java/demo/jaop/sample/Zoo.java | 2017398956/jaop | 190385523cd48fc90b32d5f1f2bae9f9e3c5ba84 | [
"Apache-2.0"
] | 5 | 2016-09-23T10:01:02.000Z | 2018-02-24T09:58:02.000Z | sample/app/src/main/java/demo/jaop/sample/Zoo.java | 2017398956/jaop | 190385523cd48fc90b32d5f1f2bae9f9e3c5ba84 | [
"Apache-2.0"
] | 31 | 2015-12-31T11:06:45.000Z | 2020-06-10T11:58:15.000Z | package demo.jaop.sample;
import android.util.Log;
/**
* Created by liting06 on 16/1/20.
*/
public class Zoo extends Foo {
public Zoo() {
Log.e("init", "Zoo");
}
public Zoo(String s) {
super(s);
Log.e("init", "Zoo whit param");
}
static {
Log.e("init", "static");
}
@Override
public void say() {
Log.e("Zoo", "Zoo");
}
}
| 14.962963 | 40 | 0.5 |
cb7054c01f3d04f33b5a0ec78b1110ce0ba60d2a | 10,370 | html | HTML | niushop/template/platform/Activity/activityDetail.html | caorui33707/ruikun | 3169f4d4c5eea45d93e47a85452b5c92b691ab7f | [
"Apache-2.0"
] | null | null | null | niushop/template/platform/Activity/activityDetail.html | caorui33707/ruikun | 3169f4d4c5eea45d93e47a85452b5c92b691ab7f | [
"Apache-2.0"
] | null | null | null | niushop/template/platform/Activity/activityDetail.html | caorui33707/ruikun | 3169f4d4c5eea45d93e47a85452b5c92b691ab7f | [
"Apache-2.0"
] | null | null | null | {extend name="platform/base" /}
{block name="resources"/}
<style type="text/css">
</style>
{/block}
{block name="main"}
<div class="row padder-v">
<div class="col-sm-4">
<button class="btn btn-sm btn-default" type="button" onclick="deleteCount()">批量删除</button>
</div>
<div class="col-sm-2">
<input type="text" id="item_name" class="input-sm form-control" placeholder="请输入商品名称">
</div>
<div class="col-sm-2">
<input type="text" id="shop_name" class="input-sm form-control" placeholder="请输入店铺名称">
</div>
<div class="col-sm-2">
<div style="width:25%;float:left;line-height: 30px;">审核状态</div>
<select class="input-sm form-control" id="activity_detail_state" style="width:75%;float:left;">
<option value="">全部</option>
<option value="0">待审核</option>
<option value="1">通过</option>
<option value="2">未通过</option>
</select>
</div>
<div class="col-sm-1">
<button class="btn btn-sm btn-default" type="button" onclick="LoadingInfo(1)">搜索</button>
</div>
</div>
<section class="panel panel-default">
<div class="table-responsive">
<table class="table table-striped b-t b-light text-sm">
<thead>
<tr>
<th width="20"><input type="checkbox"></th>
<th class="center">排序</th>
<th class="center">商品名称</th>
<th class="center">所属店铺</th>
<th class="center">状态</th>
<th class="center">操作</th>
</tr>
</thead>
<tbody id="list">
<tr></tr>
</tbody>
</table>
</div>
{include file="platform/page" /}
</section>
{/block}
{block name="script"}
<script type="text/javascript">
var activity_id = "{$activity_id}";
$(function(){
LoadingInfo(1);
});
function LoadingInfo(page_index) {
var item_name = $("#item_name").val();
var shop_name = $("#shop_name").val();
var activity_detail_state = $("#activity_detail_state").val();
$.ajax({
type : "post",
url : "{:__URL('PLATFORM_MAIN/activity/activityDetail')}",
async : true,
data : {
"page_index" : page_index,
"item_name" : item_name,
"shop_name" : shop_name,
"activity_id" : activity_id,
"activity_detail_state" : activity_detail_state
},
success : function(data) {
// alert(JSON.stringify(data));
var html = '';
$("#total_count_num").text(data["total_count"]);
$("#page_count_num").text(data["page_count"]);
$("#page_count").val(data["page_count"]);
$("#pageNumber a").remove();
if (data["data"].length > 0) {
for (var i = 0; i < data["data"].length; i++) {
html += '<tr align="center">';
html += '<td><div class="cell"><label ><input name="sub" type="checkbox" value="'+ data['data'][i]["activity_detail_id"]+'" ></label></div></td>';
html += '<td class="center"><input style="width:50px;" type="text" id="sort" value="' + data['data'][i]["activity_detail_sort"] + '" onchange="setSort('+data["data"][i]["activity_detail_id"]+',this);"/></td>';
html += '<td >'+ data['data'][i]["item_name"]+'</td>';
//
html += '<td >'+ data['data'][i]["shop_name"]+'</td>';
if(data['data'][i]["activity_detail_state"] == 0){
html += '<td>待审核</td>';
}else if(data['data'][i]["activity_detail_state"] == 1){
html += '<td>通过</td>';
}else if(data['data'][i]["activity_detail_state"] == 2){
html += '<td>未通过</td>';
}else if(data['data'][i]["activity_detail_state"] == 3){
html += '<td>已开始</td>';
}else if(data['data'][i]["activity_detail_state"] == 4){
html += '<td>已结束</td>';
}
if(data['data'][i]["activity_detail_state"] == 0){
html += '<td><a href="javascript:passHandle('+data['data'][i]['activity_detail_id']+');">通过 |</a> <a href="javascript:refuseHandle('+data['data'][i]['activity_detail_id']+');">未通过 |</a> <a href="javascript:deleteHandle('+data['data'][i]['activity_detail_id']+');">删除 |</a></td>';
}else if(data['data'][i]["activity_detail_state"] == 1){
html += '<td><a href="javascript:refuseHandle('+data['data'][i]['activity_detail_id']+');">未通过</a></td>';
}else if(data['data'][i]["activity_detail_state"] == 2){
html += '<td><a href="javascript:passHandle('+data['data'][i]['activity_detail_id']+');">通过 |</a> <a href="javascript:deleteHandle('+data['data'][i]['activity_detail_id']+');">删除 |</a></td>';
}else if(data['data'][i]["activity_detail_state"] == 3 || data['data'][i]["activity_detail_state"] == 4){
html += '<td></td>';
}else{
html += '<td></td>';
}
html += '</tr>';
}
} else {
html += '<tr align="center"><th colspan="5">暂无符合条件的数据记录</th></tr>';
}
$("#list").html(html);
var totalpage = $("#page_count").val();
if (totalpage == 1) {
changeClass("all");
}
var $html = pagenumShow(jumpNumber,totalpage,{$pageshow})
$("#pageNumber").append($html);
}
});
}
//全选
function CheckAll(event){
var checked = event.checked;
$(".style0list tbody input[type = 'checkbox']").prop("checked",checked);
}
//通过审核
function passHandle(activity_detail_id){
$.ajax({
type : "post",
url : "{:__URL('PLATFORM_MAIN/activity/passHandle')}",
data : {
'activity_detail_id' : activity_detail_id
},
async : true,
success : function(data) {
if (data['code'] > 0) {
showMessage('success', '审核成功');
location.href=__URL("PLATFORM_MAIN/activity/activityDetail?activity_id="+activity_id);
} else {
showMessage('error', '审核失败');
}
}
});
}
//拒绝通过
function refuseHandle(activity_detail_id){
$.ajax({
type : "post",
url : "{:__URL('PLATFORM_MAIN/activity/refuseHandle')}",
data : {
'activity_detail_id' : activity_detail_id
},
success : function(data) {
if (data['code'] > 0) {
showMessage('success', '拒绝成功');
location.href=__URL("PLATFORM_MAIN/activity/activityDetail?activity_id="+activity_id);
} else {
showMessage('error', '拒绝失败');
}
}
});
}
//删除
function deleteHandle(activity_detail_id){
$.ajax({
type : "post",
url : "PLATFORM_MAIN/activity/deleteActivityDetail",
data : {
'activity_detail_id' : activity_detail_id
},
success : function(data) {
if (data['code'] > 0) {
showMessage('success', '删除成功');
location.href="PLATFORM_MAIN/activity/activityDetail?activity_id="+activity_id;
} else {
showMessage('error', '删除失败');
}
}
});
}
// //批量删除
// function aaa(){
// var activity_detail_ids = new array();
// $("#list input[type='checkbox']:checked").each(function()
// {
// if (!isNaN($(this).val())) {
// activity_detail_ids.push($(this).val());
// alert(JSON.stringify(activity_detail_ids));
// return false;
// var state = $(this).attr("state");
// if(state != -1){
// $( "#dialog" ).dialog({
// buttons: {
// "确定,#e57373": function() {
// $(this).dialog('close');
// }
// },
// contentText:"记录中包含非待审核状态的商品",
// title:"消息提醒",
// });
// return false;
// }
// goods_ids = $(this).val() + "," + goods_ids;
// }
// });
// goods_ids = goods_ids.substring(0, goods_ids.length - 1);
//
// if(goods_ids == ""){
// $( "#dialog" ).dialog({
// buttons: {
// "确定,#e57373": function() {
// $(this).dialog('close');
// }
// },
// contentText:"请选择需要操作的记录",
// title:"消息提醒",
// });
// return false;
// }
// modifyGoodsOnline(goods_ids,status);
// }
function setSort(id,e){
var sortVal = $(e).val();
$.ajax({
type:"post",
url:"PLATFORM_MAIN/activity/setActivityDetailSort",
data:{
'activity_detail_id' : id,
'activity_detail_sort' : sortVal
},
async:true,
success: function (data) {
if(data['code'] > 0){
showMessage('success', '修改成功');
location.href="PLATFORM_MAIN/activity/activityDetail?activity_id="+activity_id;
}else{
showMessage('error', '修改失败');
location.href="PLATFORM_MAIN/activity/activityDetail?activity_id="+activity_id;
}
}
});
}
</script>
{/block} | 39.132075 | 322 | 0.448312 |
c09e0a330412418e1b854c6f05180ae936743661 | 1,006 | sql | SQL | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/uao/uaocs_mvcc/readcommit/readcommit_concurr_vacuum_vacuum/sql/2.sql | sridhargoudrangu/gpdb | 0783892116708662d7fe7ef4f307197de40ecc04 | [
"PostgreSQL",
"Apache-2.0"
] | null | null | null | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/uao/uaocs_mvcc/readcommit/readcommit_concurr_vacuum_vacuum/sql/2.sql | sridhargoudrangu/gpdb | 0783892116708662d7fe7ef4f307197de40ecc04 | [
"PostgreSQL",
"Apache-2.0"
] | null | null | null | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/uao/uaocs_mvcc/readcommit/readcommit_concurr_vacuum_vacuum/sql/2.sql | sridhargoudrangu/gpdb | 0783892116708662d7fe7ef4f307197de40ecc04 | [
"PostgreSQL",
"Apache-2.0"
] | null | null | null | -- @Description UAOCS MVCC readcommit and vacuum + vacuum
-- Transaction 2 of 2 (vacuum)
--
select pg_sleep(3);
insert into sto_uaocs_mvcc_status (workload, script) values('readcommit_concurr_vacuum_vacuum', 't2_insert_tuples');
select count(*) as only_visi_rows from sto_uaocs_mvcc_vacuum2;
set gp_select_invisible = true;
select count(*) as visi_and_invisi_rows from sto_uaocs_mvcc_vacuum2;
set gp_select_invisible = false;
update sto_uaocs_mvcc_status set updover = CURRENT_TIMESTAMP
where workload='readcommit_concurr_vacuum_vacuum'
AND script='t2_insert_tuples';
set transaction isolation level READ COMMITTED;
vacuum full sto_uaocs_mvcc_vacuum2 ;
update sto_uaocs_mvcc_status set endtime = CURRENT_TIMESTAMP
where workload='readcommit_concurr_vacuum_vacuum'
AND script='t2_insert_tuples';
select count(*) as only_visi_rows from sto_uaocs_mvcc_vacuum2;
set gp_select_invisible = true;
select count(*) as visi_and_invisi_rows from sto_uaocs_mvcc_vacuum2;
set gp_select_invisible = false;
| 34.689655 | 116 | 0.826044 |
40df310583a9dc504ab5d4ae5d40a203fef20b5e | 27,380 | py | Python | tests/test_make_phyla_plots_AGP.py | JWDebelius/American-Gut | ed2479a5951946ed977a5916444b51c34c48e60c | [
"BSD-3-Clause-Clear"
] | 92 | 2015-02-25T18:33:56.000Z | 2021-12-10T07:47:40.000Z | tests/test_make_phyla_plots_AGP.py | JWDebelius/American-Gut | ed2479a5951946ed977a5916444b51c34c48e60c | [
"BSD-3-Clause-Clear"
] | 96 | 2015-02-24T17:21:58.000Z | 2018-12-10T19:40:53.000Z | tests/test_make_phyla_plots_AGP.py | JWDebelius/American-Gut | ed2479a5951946ed977a5916444b51c34c48e60c | [
"BSD-3-Clause-Clear"
] | 67 | 2015-02-25T20:53:16.000Z | 2022-03-11T14:02:16.000Z | #!/usr/bin/env python
from __future__ import division
from unittest import TestCase, main
from StringIO import StringIO
from numpy import array
from numpy.testing import assert_almost_equal
from biom import Table
from matplotlib.transforms import Bbox
from americangut.make_phyla_plots import (map_to_2D_dict,
identify_most_common_categories,
summarize_common_categories,
calculate_dimensions_rectangle,
calculate_dimensions_bar,
translate_colors)
__author__ = "Justine Debelius"
__copyright__ = "Copyright 2013, The American Gut Project"
__credits__ = ["Justine Debelius", "Adam Robbins-Pianka"]
__license__ = "BSD"
__version__ = "unversioned"
__maintainer__ = "Justine Debelius"
__email__ = "Justine.Debelius@colorado.edu"
class MakePhylaPlotsAGPTest(TestCase):
def setUp(self):
# Creates an otu table for testing which corresponds with the
sample_ids = ['00010', '00100', '00200', '00111', '00112', '00211']
observation_ids = ['1001', '2001', '2002', '2003', '3001', '3003',
'4001', '5001', '6001', '7001', '8001', '9001',
'9002', '9003']
observation_md = [{'taxonomy': (u'k__Bacteria', u'p__Bacteroidetes',
u'c__Bacteroidia')},
{'taxonomy': (u'k__Bacteria', u'p__Firmicutes',
u'c__Clostridia')},
{'taxonomy': (u'k__Bacteria', u'p__Firmicutes',
u'c__Erysipelotrichi')},
{'taxonomy': (u'k__Bacteria', u'p__Firmicutes',
u'c__Bacilli')},
{'taxonomy': (u'k__Bacteria', u'p__Proteobacteria',
u'c__Alphaproteobacteria')},
{'taxonomy': (u'k__Bacteria', u'p__Proteobacteria',
u'c__Gammaproteobacteria')},
{'taxonomy': (u'k__Bacteria', u'p__Tenericutes',
u'c__Mollicutes')},
{'taxonomy': (u'k__Bacteria', u'p__Actinobacteria',
u'c__Coriobacteriia')},
{'taxonomy': (u'k__Bacteria', u'p__Verrucomicrobia',
u'c__Verrucomicrobiae')},
{'taxonomy': (u'k__Bacteria', u'p__Cyanobacteria',
u'c__4C0d-2')},
{'taxonomy': (u'k__Bacteria', u'p__Fusobacteria',
u'c__Fusobacteriia')},
{'taxonomy': (u'k__Bacteria', u'p__TM7',
u'c__TM7-2')},
{'taxonomy': (u'k__Bacteria', u'p__Acidobacteria',
u'c__Chloracidobacteria')},
{'taxonomy': (u'k__Bacteria', u'p__', u'c__')}]
data = array([[ 1691, 3004, 18606, 6914, 1314, 22843],
[ 2019, 1091, 8163, 1112, 738, 2362],
[ 67, 4, 2835, 310, 85, 161],
[ 731, 407, 18240, 1924, 492, 522],
[ 8, 1, 0, 53, 8, 275],
[ 105, 179, 0, 504, 79, 2771],
[ 451, 0, 0, 33, 0, 0],
[ 282, 60, 106, 11, 0, 0],
[ 113, 481, 2658, 22, 146, 0],
[ 6, 120, 0, 0, 0, 0],
[ 45, 0, 106, 0, 0, 1523],
[ 39, 341, 1761, 139, 18, 0],
[ 21, 268, 153, 8, 15, 0],
[ 59, 51, 531, 120, 25, 0]])
self.otu_table = Table(data,
observation_ids,
sample_ids,
observation_metadata=observation_md)
self.common_cats = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria')]
def test_map_to_2D_dict(self):
"""Checks map_to_2D_dict is sane"""
# Creates a pseudo-opening function
test_map = StringIO(
'#SampleID\tBIRTH_YEAR\tDEATH_YEAR\tSEX\tPROFESSION\tHOME_STATE\n'
'00010\t1954\t2006\tmale\tMechanic\tKansas\n'
'00100\t1954\t1983\tfemale\tHunter\tKansas\n'
'00200\tNA\t2009\tfemale\tNurse\tMinnesota\n'
'00111\t1979\t2007\tmale\tHunter\tImpala\n'
'00112\t1983\t2006\tmale\tHunter\tImpala\n'
'00211\t1990\t2009\tmale\tStudent\tMinnesota\n')
# Sets up the known dictionary
known_dict = {'00010': {'#SampleID': '00010', 'BIRTH_YEAR': '1954',
'DEATH_YEAR': '2006', 'SEX': 'male',
'PROFESSION': 'Mechanic', 'HOME_STATE':
'Kansas'},
'00100': {'#SampleID': '00100', 'BIRTH_YEAR': '1954',
'DEATH_YEAR': '1983', 'SEX': 'female',
'PROFESSION': 'Hunter', 'HOME_STATE':
'Kansas'},
'00200': {'#SampleID': '00200', 'BIRTH_YEAR': 'NA',
'DEATH_YEAR': '2009', 'SEX': 'female',
'PROFESSION': 'Nurse',
'HOME_STATE': 'Minnesota'},
'00111': {'#SampleID': '00111', 'BIRTH_YEAR': '1979',
'DEATH_YEAR': '2007', 'SEX': 'male',
'PROFESSION': 'Hunter', 'HOME_STATE':
'Impala'},
'00112': {'#SampleID': '00112', 'BIRTH_YEAR': '1983',
'DEATH_YEAR': '2006', 'SEX': 'male',
'PROFESSION': 'Hunter', 'HOME_STATE':
'Impala'},
'00211': {'#SampleID': '00211', 'BIRTH_YEAR': '1990',
'DEATH_YEAR': '2009', 'SEX': 'male',
'PROFESSION': 'Student', 'HOME_STATE':
'Minnesota'}}
# Checks the test dictionary is loaded properly and equals the known
test_dict = map_to_2D_dict(test_map)
self.assertEqual(test_dict, known_dict)
def test_identify_most_common_categories(self):
"""Tests that indentify_most_common_categories is sane"""
# Sets up known values
known_cats_comp = [(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__TM7'),
(u'k__Bacteria', u'p__Acidobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__'),
(u'k__Bacteria', u'p__Fusobacteria')]
known_cats_aver = [(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Firmicutes')]
known_cat_count = [(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__TM7'),
(u'k__Bacteria', u'p__Acidobacteria'),
(u'k__Bacteria', u'p__'),
(u'k__Bacteria', u'p__Actinobacteria')]
known_cats_none = [(u'k__Bacteria', u'p__'),
(u'k__Bacteria', u'p__Acidobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Fusobacteria'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__TM7'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Verrucomicrobia')]
known_scores_comp = [[(u'k__Bacteria', u'p__Bacteroidetes'), 0.4950,
1.0000, 4950.00],
[(u'k__Bacteria', u'p__Firmicutes'), 0.3584,
1.0000, 3584.00],
[(u'k__Bacteria', u'p__Proteobacteria'), 0.0383,
0.8333, 319.15],
[(u'k__Bacteria', u'p__Verrucomicrobia'), 0.0337,
0.8333, 280.82],
[(u'k__Bacteria', u'p__TM7'), 0.0192,
0.8333, 159.99],
[(u'k__Bacteria', u'p__Acidobacteria'), 0.0095,
0.8333, 79.16],
[(u'k__Bacteria', u'p__Actinobacteria'), 0.0105,
0.6667, 70.00],
[(u'k__Bacteria', u'p__'), 0.0080,
0.8333, 66.66],
[(u'k__Bacteria', u'p__Fusobacteria'), 0.0100,
0.5000, 50.00],
[(u'k__Bacteria', u'p__Tenericutes'), 0.0138,
0.3333, 46.00],
[(u'k__Bacteria', u'p__Cyanobacteria'), 0.0035,
0.3333, 11.67]]
known_scores_aver = [[(u'k__Bacteria', u'p__Bacteroidetes'), 0.4950,
1.0000, 4950.00],
[(u'k__Bacteria', u'p__Firmicutes'), 0.3584,
1.0000, 3584.00],
[(u'k__Bacteria', u'p__Proteobacteria'), 0.0383,
0.8333, 319.15],
[(u'k__Bacteria', u'p__Verrucomicrobia'), 0.0337,
0.8333, 280.82],
[(u'k__Bacteria', u'p__TM7'), 0.0192,
0.8333, 159.99],
[(u'k__Bacteria', u'p__Tenericutes'), 0.0138,
0.3333, 46.00],
[(u'k__Bacteria', u'p__Actinobacteria'), 0.0105,
0.6667, 70.00],
[(u'k__Bacteria', u'p__Fusobacteria'), 0.0100,
0.5000, 50.00],
[(u'k__Bacteria', u'p__Acidobacteria'), 0.0095,
0.8333, 79.16],
[(u'k__Bacteria', u'p__'), 0.0080,
0.8333, 66.66],
[(u'k__Bacteria', u'p__Cyanobacteria'), 0.0035,
0.3333, 11.67]]
known_score_count = [[(u'k__Bacteria', u'p__Bacteroidetes'), 0.4950,
1.0000, 4950.00],
[(u'k__Bacteria', u'p__Firmicutes'), 0.3584,
1.0000, 3584.00],
[(u'k__Bacteria', u'p__Proteobacteria'), 0.0383,
0.8333, 319.15],
[(u'k__Bacteria', u'p__Verrucomicrobia'), 0.0337,
0.8333, 280.82],
[(u'k__Bacteria', u'p__TM7'), 0.0192,
0.8333, 159.99],
[(u'k__Bacteria', u'p__Acidobacteria'), 0.0095,
0.8333, 79.16],
[(u'k__Bacteria', u'p__'), 0.0080,
0.8333, 66.66],
[(u'k__Bacteria', u'p__Actinobacteria'), 0.0105,
0.6667, 70.00],
[(u'k__Bacteria', u'p__Fusobacteria'), 0.0100,
0.5000, 50.00],
[(u'k__Bacteria', u'p__Tenericutes'), 0.0138,
0.3333, 46.00],
[(u'k__Bacteria', u'p__Cyanobacteria'), 0.0035,
0.3333, 11.67]]
known_scores_none = [[(u'k__Bacteria', u'p__'), 0.0080,
0.8333, 66.66],
[(u'k__Bacteria', u'p__Acidobacteria'), 0.0095,
0.8333, 79.16],
[(u'k__Bacteria', u'p__Actinobacteria'), 0.0105,
0.6667, 70.00],
[(u'k__Bacteria', u'p__Bacteroidetes'), 0.4950,
1.0000, 4950.00],
[(u'k__Bacteria', u'p__Cyanobacteria'), 0.0035,
0.3333, 11.67],
[(u'k__Bacteria', u'p__Firmicutes'), 0.3584,
1.0000, 3584.00],
[(u'k__Bacteria', u'p__Fusobacteria'), 0.0100,
0.5000, 50.00],
[(u'k__Bacteria', u'p__Proteobacteria'), 0.0383,
0.8333, 319.15],
[(u'k__Bacteria', u'p__TM7'), 0.0192,
0.8333, 159.99],
[(u'k__Bacteria', u'p__Tenericutes'), 0.0138,
0.3333, 46.00],
[(u'k__Bacteria', u'p__Verrucomicrobia'), 0.0337,
0.8333, 280.82]]
# Tests code
[test_cats_none, test_scores_none] = \
identify_most_common_categories(biom_table=self.otu_table,
level=2,
metadata_category='taxonomy',
limit_mode='NONE')
[test_cats_comp, test_scores_comp] = \
identify_most_common_categories(biom_table=self.otu_table,
level=2,
metadata_category='taxonomy',
limit_mode='COMPOSITE',
limit=49)
[test_cats_aver, test_scores_aver] = \
identify_most_common_categories(biom_table=self.otu_table,
level=2,
metadata_category='taxonomy',
limit_mode='AVERAGE',
limit=0.1)
[test_cat_count, test_score_count] = \
identify_most_common_categories(biom_table=self.otu_table,
level=2,
metadata_category='taxonomy',
limit_mode='COUNTS',
limit=0.5)
# Checks that appropriate errors are called
with self.assertRaises(ValueError):
identify_most_common_categories(biom_table=self.otu_table,
level=2,
limit_mode='This is a test')
with self.assertRaises(ValueError):
identify_most_common_categories(biom_table=self.otu_table,
level=2,
limit=100000)
# Checks that output values are correct
self.assertEqual(test_cats_none, known_cats_none)
self.assertEqual(test_scores_none, known_scores_none)
self.assertEqual(test_cats_comp, known_cats_comp)
self.assertEqual(test_scores_comp, known_scores_comp)
self.assertEqual(test_cats_aver, known_cats_aver)
self.assertEqual(test_scores_aver, known_scores_aver)
self.assertEqual(test_cat_count, known_cat_count)
self.assertEqual(test_score_count, known_score_count)
def test_summarize_common_categories(self):
"""Checks that summarize_common_categories is sane"""
# Defines the known values
known_ids = ('00010', '00100', '00200', '00111', '00112', '00211')
table_known = array([[ 0.49973390, 0.25004162, 0.55001035,
0.30008969, 0.45034247, 0.09997702],
[ 0.29998226, 0.50008324, 0.35000658,
0.62008969, 0.45000000, 0.75000821],
[ 0.02004612, 0.02996504, 0.00000000,
0.04995516, 0.02979452, 0.10000985],
[ 0.05002661, 0.00998835, 0.00199402,
0.00098655, 0.00000000, 0.00000000],
[ 0.02004612, 0.08007325, 0.05000094,
0.00197309, 0.05000000, 0.00000000],
[ 0.08000710, 0.00000000, 0.00000000,
0.00295964, 0.00000000, 0.00000000],
[ 0.00106440, 0.01997669, 0.00000000,
0.00000000, 0.00000000, 0.00000000],
[ 0.00798297, 0.00000000, 0.00199402,
0.00000000, 0.00000000, 0.05000492],
[ 0.02111052, 0.10987182, 0.04599409,
0.02394619, 0.01986301, 0.00000000]])
known_common_cats = [(u'k__Bacteria', u'p__Firmicutes'),
(u'k__Bacteria', u'p__Bacteroidetes'),
(u'k__Bacteria', u'p__Proteobacteria'),
(u'k__Bacteria', u'p__Actinobacteria'),
(u'k__Bacteria', u'p__Verrucomicrobia'),
(u'k__Bacteria', u'p__Tenericutes'),
(u'k__Bacteria', u'p__Cyanobacteria'),
(u'k__Bacteria', u'p__Fusobacteria'),
(u'k__Bacteria', u'p__Other')]
# Checks that appropriate errors are raised when the wrong type of
# argument is passed.
with self.assertRaises(ValueError):
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats,
metadata_category='Billy_Joel_Song')
# Calculates the test values
[test_ids, test_table, test_common_cats] = \
summarize_common_categories(biom_table=self.otu_table,
level=2,
common_categories=self.common_cats)
# Checks that all the outputs are correct
self.assertEqual(tuple(test_ids), known_ids)
assert_almost_equal(test_table, table_known, decimal=4)
self.assertEqual(test_common_cats, known_common_cats)
def test_calculate_dimensions_rectangle(self):
"""Checcks calculate_dimensions_rectangle is sane"""
# Sets up known values
known_figure_dimensions_def = (5.2, 4.45)
known_axis_dimensions_def = Bbox(array([[0.01923077, 0.02247191],
[0.78846154, 0.92134831]]))
known_figure_dims_in = (4.2, 3.45)
known_axis_dims_in = Bbox(array([[0.26190476, 0.31884058],
[0.73809524, 0.89855072]]))
known_figure_dims_cm = (1.6535433, 1.3582677)
known_axis_dims_cm = Bbox(array([[0.26190476, 0.31884058],
[0.73809524, 0.89855072]]))
# Sets up test values
test_axis_side = 2
test_border = 0.1
test_xlab = 1
test_ylab = 1
# Tests that an error is raised if the units are not sane
with self.assertRaises(ValueError):
calculate_dimensions_rectangle(unit='Demons')
# Calculates the test values
(test_axis_df, test_fig_df) = calculate_dimensions_rectangle()
(test_axis_in, test_fig_in) = \
calculate_dimensions_rectangle(axis_width=test_axis_side,
axis_height=test_axis_side,
border=test_border,
xlab=test_xlab,
ylab=test_ylab)
(test_axis_cm, test_fig_cm) = \
calculate_dimensions_rectangle(axis_width=test_axis_side,
axis_height=test_axis_side,
border=test_border,
xlab=test_xlab,
ylab=test_ylab,
unit='cm')
assert_almost_equal(test_fig_df, known_figure_dimensions_def,
decimal=5)
assert_almost_equal(test_axis_df, known_axis_dimensions_def,
decimal=5)
assert_almost_equal(test_fig_in, known_figure_dims_in, decimal=5)
assert_almost_equal(test_axis_in, known_axis_dims_in, decimal=5)
assert_almost_equal(test_fig_cm, known_figure_dims_cm, decimal=5)
assert_almost_equal(test_axis_cm, known_axis_dims_cm, decimal=5)
def test_calculate_dimensions_bar(self):
"""Checks that calculate_dimensions_bar is sane"""
# Sets up known values
known_axis_df = array([[0.01388889, 0.02380952],
[0.70833333, 0.73809524]])
known_axis_in = array([[0.06179775, 0.21153846],
[0.43258427, 0.98076923]])
known_axis_cm = array([[0.06179775, 0.21153846],
[0.43258427, 0.98076923]])
known_fig_df = (7.2, 4.2)
known_fig_in = (8.9, 2.6)
known_fig_cm = (8.9/2.54, 2.6/2.54)
# Sets up test values
test_num_bars = 10
test_bar_width = 0.33
test_axis_height = 2
test_border = 0.05
test_title = 0
test_legend = 5
test_xlab = 0.5
test_ylab = 0.5
# Checks that errors are raised when improper arguments are passed. (A
# number of bars that is not an integer less than one, or a unit other
# than 'in' or 'cm'.)
with self.assertRaises(ValueError):
calculate_dimensions_bar(num_bars=-3)
with self.assertRaises(ValueError):
calculate_dimensions_bar(num_bars=3.1435)
with self.assertRaises(ValueError):
calculate_dimensions_bar(num_bars=3, unit='Angel')
# Calculates the test value
(test_axis_df, test_fig_df) = calculate_dimensions_bar(test_num_bars)
(test_axis_in, test_fig_in) = \
calculate_dimensions_bar(num_bars=test_num_bars,
bar_width=test_bar_width,
border=test_border,
axis_height=test_axis_height,
xlab=test_xlab,
ylab=test_ylab,
title=test_title,
legend=test_legend)
(test_axis_cm, test_fig_cm) = \
calculate_dimensions_bar(num_bars=test_num_bars,
bar_width=test_bar_width,
border=test_border,
axis_height=test_axis_height,
xlab=test_xlab,
ylab=test_ylab,
title=test_title,
legend=test_legend,
unit='cm')
# Checks that values are appropriate
self.assertEqual(known_fig_df, test_fig_df)
self.assertEqual(known_fig_in, test_fig_in)
self.assertEqual(known_fig_cm, test_fig_cm)
assert_almost_equal(known_axis_df, test_axis_df, decimal=5)
assert_almost_equal(known_axis_in, test_axis_in, decimal=5)
assert_almost_equal(known_axis_cm, test_axis_cm, decimal=5)
def test_translate_colors(self):
"""Checks translate_colors is sane"""
# Sets up knowns values
known_def_8 = array([[0.83529412, 0.24313725, 0.30980392],
[0.95686275, 0.42745098, 0.26274510],
[0.99215686, 0.68235294, 0.38039216],
[0.99607843, 0.87843137, 0.54509804],
[0.90196078, 0.96078431, 0.59607843],
[0.67058824, 0.86666667, 0.64313725],
[0.40000000, 0.76078431, 0.64705882],
[0.19607843, 0.53333333, 0.74117647]])
known_PuRd_9 = array([[0.96862745, 0.95686275, 0.97647059],
[0.90588235, 0.88235294, 0.93725490],
[0.83137255, 0.72549020, 0.85490196],
[0.78823529, 0.58039216, 0.78039216],
[0.87450980, 0.39607843, 0.69019608],
[0.90588235, 0.16078431, 0.54117647],
[0.80784314, 0.07058824, 0.33725490],
[0.59607843, 0.00000000, 0.26274510],
[0.40392157, 0.00000000, 0.12156863]])
# Test the calls
with self.assertRaises(ValueError):
translate_colors(5, 'Winchester')
with self.assertRaises(ValueError):
translate_colors(13)
def_map = translate_colors(8)
PuRd_map = translate_colors(9, 'PuRd')
# Checks the outputs are sane
assert_almost_equal(known_def_8, def_map)
assert_almost_equal(known_PuRd_9, PuRd_map)
if __name__ == '__main__':
main()
| 51.17757 | 78 | 0.449525 |
6e396141f20d75b1197f8303cd5a1758ff77c72e | 1,336 | swift | Swift | iOS.Conf Extension/VenueInterfaceController.swift | stelarelas/ios-conference | c6166c97c66235d77c0070c6e4601c3f8171628a | [
"MIT"
] | 15 | 2017-03-07T23:13:36.000Z | 2020-02-26T15:34:21.000Z | iOS.Conf Extension/VenueInterfaceController.swift | stelarelas/ios-conference | c6166c97c66235d77c0070c6e4601c3f8171628a | [
"MIT"
] | 1 | 2017-05-03T09:24:05.000Z | 2017-05-03T09:24:05.000Z | iOS.Conf Extension/VenueInterfaceController.swift | stelarelas/ios-conference | c6166c97c66235d77c0070c6e4601c3f8171628a | [
"MIT"
] | 3 | 2017-03-08T15:10:11.000Z | 2019-09-18T12:54:45.000Z | //
// VenueInterfaceController.swift
// iOSConf
//
// Created by Nikos Maounis on 08/02/2017.
// Copyright © 2017 Taxibeat Ltd. All rights reserved.
//
import WatchKit
import Foundation
class VenueInterfaceController: WKInterfaceController {
@IBOutlet var venueMap: WKInterfaceMap!
@IBOutlet var venueLabel: WKInterfaceLabel!
let coordinate = CLLocationCoordinate2DMake(37.9787925, 23.7123368)
override func awake(withContext context: Any?) {
super.awake(withContext: context)
venueLabel.setText("Voutadon 34, Athens, Greece")
setupMap()
}
func setupMap() {
venueMap.setVisibleMapRect(MKMapRect(origin: MKMapPointForCoordinate(coordinate), size: MKMapSize(width: 0.5, height: 0.5)))
let span = MKCoordinateSpanMake(0.003, 0.003)
venueMap.setRegion(MKCoordinateRegion(center: coordinate, span: span))
venueMap.addAnnotation(coordinate, withImageNamed: "venueIcon", centerOffset: CGPoint(x: 0.0, y: -9.0))
}
override func willActivate() {
// This method is called when watch view controller is about to be visible to user
super.willActivate()
}
override func didDeactivate() {
// This method is called when watch view controller is no longer visible
super.didDeactivate()
}
}
| 30.363636 | 132 | 0.689371 |
16e2f905cc81fcc7b3ca9f88ab2e71b49e138721 | 127 | kt | Kotlin | idea/testData/quickfix/checkArguments/addNameToArgument/mixedNamedAndPositionalArgumentsMultiple.kt | qussarah/declare | c83b764c7394efa3364915d973ae79c4ebed2437 | [
"Apache-2.0"
] | 152 | 2016-02-03T20:19:47.000Z | 2021-05-28T07:08:12.000Z | idea/testData/quickfix/checkArguments/addNameToArgument/mixedNamedAndPositionalArgumentsMultiple.kt | qussarah/declare | c83b764c7394efa3364915d973ae79c4ebed2437 | [
"Apache-2.0"
] | 1 | 2020-09-03T16:13:29.000Z | 2020-09-03T16:13:29.000Z | idea/testData/quickfix/checkArguments/addNameToArgument/mixedNamedAndPositionalArgumentsMultiple.kt | qussarah/declare | c83b764c7394efa3364915d973ae79c4ebed2437 | [
"Apache-2.0"
] | 54 | 2016-02-29T16:27:38.000Z | 2020-12-26T15:02:23.000Z | // "Add name to argument..." "true"
fun f(a: Int, b: String = "b", c: String = "c") {}
fun g() {
f(a = 10, <caret>"FOO")
} | 21.166667 | 50 | 0.480315 |
7b8cd84603dc7b669a38ff36240d81d884aa5146 | 908 | css | CSS | Spring_02_ReadBook/src/main/webapp/static/write.css | paldonenemttin/2021_11_IDEA | 1fa440f09dcca14cb745df11d1a09e880aea03e4 | [
"MIT"
] | null | null | null | Spring_02_ReadBook/src/main/webapp/static/write.css | paldonenemttin/2021_11_IDEA | 1fa440f09dcca14cb745df11d1a09e880aea03e4 | [
"MIT"
] | null | null | null | Spring_02_ReadBook/src/main/webapp/static/write.css | paldonenemttin/2021_11_IDEA | 1fa440f09dcca14cb745df11d1a09e880aea03e4 | [
"MIT"
] | null | null | null | .content-box{
display: flex;
flex-direction: column;
width: 90vw;
margin-left: 5%;
}
.one{
display: flex;
margin-top: 20px;
justify-content: center;
}
.one input{
width:50vw;
height: 5vh;
}
.two{
display: flex;
margin-top: 20px;
justify-content: center;
}
.three{
display: flex;
margin-top: 20px;
justify-content: center;
flex-direction: column;
margin-bottom: 20px;
}
.button_box{
display: flex;
justify-content: right;
margin-bottom: 20px;
width: 90vw;
margin-left: 5%;
}
#save{
background-color: cadetblue;
color: white;
margin-right: 10px;
}
#reset{
background-color: tomato;
color: white;
margin-right: 10px;
}
#list{
background-color: yellow;
color: black;
}
.btn_boys{
padding: 3px;
border-radius: 2px;
box-shadow: 1px 1px 1px gray;
width: 10%;
height: 30px;
} | 16.214286 | 33 | 0.606828 |
1873994a38612ad6dcbaf591297d6d534e3132ae | 1,749 | rb | Ruby | app/models/saved_search.rb | unepwcmc/ProtectedPlanet | 0d1e0b643b5926551b3dda7fbba19f88a6862872 | [
"BSD-3-Clause"
] | 12 | 2015-09-21T08:39:07.000Z | 2021-06-24T13:22:27.000Z | app/models/saved_search.rb | unepwcmc/ProtectedPlanet | 0d1e0b643b5926551b3dda7fbba19f88a6862872 | [
"BSD-3-Clause"
] | 220 | 2015-01-08T12:07:15.000Z | 2022-03-21T17:08:02.000Z | app/models/saved_search.rb | unepwcmc/ProtectedPlanet | 0d1e0b643b5926551b3dda7fbba19f88a6862872 | [
"BSD-3-Clause"
] | 2 | 2017-09-21T11:53:14.000Z | 2018-02-21T07:24:53.000Z | # Only used for the areas search download - to cache search results and reduce
# performance hit on the system
class SavedSearch < ApplicationRecord
# Elasticsearch has a maximum page size of 10000
MAX_SIZE = 9999
def name
search_term
end
def parsed_filters
JSON.parse(filters) if filters.present?
end
def all_wdpa_ids
search_results.flatten
end
private
def search_results
# Perform initial search to store the first set of results
@results = []
initial_set = extract_wdpa_ids(download_search.results)
@results << initial_set
# Return early if number of hits is less than 10000 to avoid unnecessary searches
return @results if @results.last.length < MAX_SIZE
# Keep looping until there are no more results
loop do
last_wdpa_id = @results.last.last
next_batch = extract_wdpa_ids(download_search(last_wdpa_id).results)
break if next_batch.empty?
@results << next_batch
end
@results
end
def extract_wdpa_ids(results)
results.pluck('wdpa_id')
end
def search_query_options
{
offset: 0, # Have to set this to 0 for Elastic's search_after API
filters: parsed_filters || {},
without_aggregations: true,
sort: [{ 'wdpa_id': 'asc' }],
size: MAX_SIZE
}
end
# Make use of Elasticsearch search_after API to search after the WDPA ID passed
# to the search.
def download_search(last_wdpaid_of_results = nil)
if last_wdpaid_of_results
merged_query_options = search_query_options.merge({ last_wdpa_id: last_wdpaid_of_results })
else
merged_query_options = search_query_options
end
Search.search(search_term, merged_query_options, Search::PA_INDEX)
end
end
| 24.291667 | 97 | 0.71012 |
c87a40e9857c5fba5e45d3e5428cddb4e970df97 | 1,803 | rs | Rust | src/lib.rs | saghm/dynlist | b859e87d0cdfcf4f3a3a69e08e9027e61dcc3810 | [
"Apache-2.0"
] | null | null | null | src/lib.rs | saghm/dynlist | b859e87d0cdfcf4f3a3a69e08e9027e61dcc3810 | [
"Apache-2.0"
] | null | null | null | src/lib.rs | saghm/dynlist | b859e87d0cdfcf4f3a3a69e08e9027e61dcc3810 | [
"Apache-2.0"
] | null | null | null | #[macro_use]
mod elem;
use std::fmt;
use std::marker::PhantomData;
pub use self::elem::DynElem;
#[derive(Debug)]
pub struct DynList<'a, T: 'a> {
inner: Vec<DynElem<'a, T>>,
phantom: PhantomData<&'a T>,
}
impl<'a, T> DynList<'a, T> {
pub fn new<I>(i: I) -> Self
where
I: IntoIterator<Item = DynElem<'a, T>>,
{
DynList {
inner: i.into_iter().collect(),
phantom: PhantomData,
}
}
pub fn inner_ref(&self) -> DynList<&T> {
DynList {
inner: self.inner.iter().map(DynElem::inner_ref).collect(),
phantom: PhantomData,
}
}
pub fn iter(&self) -> DynListIntoIter<&T> {
self.inner_ref().into_iter()
}
}
impl<'a, T> fmt::Display for DynList<'a, T>
where
T: fmt::Display,
{
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
write!(f, "[")?;
for (i, elem) in self.inner.iter().enumerate() {
if i != 0 {
write!(f, ",")?;
}
write!(f, "{}", elem)?;
}
write!(f, "]")?;
Ok(())
}
}
impl<'a, T: 'a> IntoIterator for DynList<'a, T> {
type Item = T;
type IntoIter = DynListIntoIter<'a, T>;
fn into_iter(self) -> Self::IntoIter {
DynListIntoIter {
inner: Box::new(self.inner.into_iter().flat_map(IntoIterator::into_iter)),
}
}
}
#[macro_export]
macro_rules! dyn_list {
($($elem:tt),*) => {{
use dynlist::DynList;
DynList::new(vec![$(dyn_elem!($elem)),*])
}};
}
pub struct DynListIntoIter<'a, T: 'a> {
inner: Box<Iterator<Item = T> + 'a>,
}
impl<'a, T: 'a> Iterator for DynListIntoIter<'a, T> {
type Item = T;
fn next(&mut self) -> Option<Self::Item> {
self.inner.next()
}
}
| 20.258427 | 86 | 0.507488 |
0496792faf894bc146da3978dd94780a4561c9fc | 5,220 | kt | Kotlin | sdk/src/main/java/me/uport/sdk/AccountStorage.kt | uport-project/uport-android-sdk | 6b53dc1dcc992b3761496c1f4b13838b1bf914d5 | [
"Apache-2.0"
] | 32 | 2018-05-25T10:43:07.000Z | 2020-09-16T10:31:35.000Z | sdk/src/main/java/me/uport/sdk/AccountStorage.kt | uport-project/uport-android-sdk | 6b53dc1dcc992b3761496c1f4b13838b1bf914d5 | [
"Apache-2.0"
] | 56 | 2018-06-22T08:20:24.000Z | 2019-10-10T10:11:03.000Z | sdk/src/main/java/me/uport/sdk/AccountStorage.kt | uport-project/uport-android-sdk | 6b53dc1dcc992b3761496c1f4b13838b1bf914d5 | [
"Apache-2.0"
] | 8 | 2018-06-26T02:29:08.000Z | 2022-03-29T10:05:25.000Z | package me.uport.sdk
import android.content.SharedPreferences
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json
import me.uport.sdk.identity.Account
import me.uport.sdk.identity.AccountType
import me.uport.sdk.identity.HDAccount
import me.uport.sdk.identity.MetaIdentityAccount
interface AccountStorage {
fun upsert(newAcc: Account)
fun get(handle: String): Account?
fun delete(handle: String)
fun all(): List<Account>
fun upsertAll(list: Collection<Account>)
/**
* sets the default account using its handle
*/
fun setAsDefault(accountHandle: String)
/**
* fetches the default account
*/
fun getDefaultAccount(): Account?
}
/**
* An account storage mechanism that relies on [SharedPreferences] for persistence to disk
*
* Accounts are serialized then wrapped in an AccountHolder then along with the AccountType and isDefault
*
* Accounts are loaded during construction and then relayed from memory
*/
class SharedPrefsAccountStorage(
private val prefs: SharedPreferences
) : AccountStorage {
private val accounts = mapOf<String, AccountHolder>().toMutableMap()
init {
prefs.getStringSet(KEY_ACCOUNTS, emptySet())
.orEmpty()
.forEach { serialized ->
val accountHolder = try {
AccountHolder.fromJson(serialized)
} catch (ex: Exception) {
null
}
accountHolder.let {
val account = fetchAccountFromHolder(accountHolder)
if (account != null) {
upsert(account)
}
}
}
}
override fun upsert(newAcc: Account) {
accounts[newAcc.handle] = buildAccountHolder(newAcc)
persist()
}
override fun upsertAll(list: Collection<Account>) {
list.forEach {
accounts[it.handle] = buildAccountHolder(it)
}
persist()
}
override fun get(handle: String): Account? {
val holder: AccountHolder? = accounts[handle]
return fetchAccountFromHolder(holder)
}
override fun delete(handle: String) {
accounts.remove(handle)
if (getDefaultAccount()?.handle.equals(handle)) {
persistDefault("")
}
persist()
}
override fun all(): List<Account> = fetchAllAccounts()
override fun setAsDefault(accountHandle: String) {
persistDefault(accountHandle)
}
override fun getDefaultAccount(): Account? {
val accountHandle = prefs.getString(KEY_DEFAULT_ACCOUNT, "") ?: ""
val defaultAccountHolder = accounts[accountHandle]
if (defaultAccountHolder != null && defaultAccountHolder != AccountHolder.blank) {
return fetchAccountFromHolder(defaultAccountHolder)
}
else {
return null
}
}
private fun persist() {
prefs.edit()
.putStringSet(KEY_ACCOUNTS, accounts.values.map { it.toJson() }.toSet())
.apply()
}
private fun persistDefault(serializedAccountHolder: String) {
prefs.edit()
.putString(KEY_DEFAULT_ACCOUNT, serializedAccountHolder)
.apply()
}
companion object {
private const val KEY_ACCOUNTS = "accounts"
private const val KEY_DEFAULT_ACCOUNT = "default_account"
}
@Suppress("UnsafeCast")
private fun buildAccountHolder(account: Account): AccountHolder {
val acc = when (account.type) {
AccountType.HDKeyPair -> (account as HDAccount).toJson()
AccountType.MetaIdentityManager -> (account as MetaIdentityAccount).toJson()
else -> throw IllegalArgumentException("Storage not supported AccountType ${account.type}")
}
return AccountHolder(acc, account.type.toString())
}
private fun fetchAccountFromHolder(holder: AccountHolder?): Account? {
return when (holder?.type) {
AccountType.HDKeyPair.toString() -> HDAccount.fromJson(holder.account)
AccountType.MetaIdentityManager.toString() -> MetaIdentityAccount.fromJson(holder.account)
else -> null
}
}
private fun fetchAllAccounts() = accounts
.map { fetchAccountFromHolder(it.value) }
.filterNotNull()
}
/**
* Used to wrap any type of account before it is stored
*/
@Serializable
data class AccountHolder(
val account: String,
val type: String
) {
/**
* serializes accountHolder
*/
fun toJson(pretty: Boolean = false): String = if (pretty) Json.indented.stringify(serializer(), this) else Json.stringify(serializer(), this)
companion object {
val blank = AccountHolder("", "")
/**
* de-serializes accountHolder
*/
fun fromJson(serializedAccountHolder: String): AccountHolder {
if (serializedAccountHolder.isEmpty()) {
return blank
}
return Json.parse(serializer(), serializedAccountHolder)
}
}
} | 28.064516 | 145 | 0.615326 |
e9ca0ad74e463c3f20d2d665a2fdf394b36f30de | 527 | rb | Ruby | app/models/association.rb | alilee/shortepic-pmo | 953ea3b35960e8a47f9625f2a8b592aecfb75c4d | [
"MIT"
] | 1 | 2016-05-09T05:05:50.000Z | 2016-05-09T05:05:50.000Z | app/models/association.rb | alilee/shortepic-pmo | 953ea3b35960e8a47f9625f2a8b592aecfb75c4d | [
"MIT"
] | null | null | null | app/models/association.rb | alilee/shortepic-pmo | 953ea3b35960e8a47f9625f2a8b592aecfb75c4d | [
"MIT"
] | null | null | null | # == Schema Information
# Schema version: 16
#
# Table name: associations
#
# id :integer not null, primary key
# item_id_from :integer not null
# item_id_to :integer not null
#
class Association < ActiveRecord::Base
belongs_to :item_from, :class_name => "Item", :foreign_key => "item_id_from"
belongs_to :item_to, :class_name => "Item", :foreign_key => "item_id_to"
validates_presence_of :item_id_from, :item_id_to
validates_uniqueness_of :item_id_to, :scope => 'item_id_from'
end
| 29.277778 | 78 | 0.6926 |
5de444a2fe9e22bfba19997eb7b783018541574a | 578 | h | C | Comprehensive_Project/Comprehensive_Project/Parser/KLT2010-TestVersion-2017/API/kma/header/pomi-def.h | blackberry-pie/Comprehensive_Project | f54e07047fa411d2348422de089e406ffed44ac2 | [
"Apache-2.0"
] | null | null | null | Comprehensive_Project/Comprehensive_Project/Parser/KLT2010-TestVersion-2017/API/kma/header/pomi-def.h | blackberry-pie/Comprehensive_Project | f54e07047fa411d2348422de089e406ffed44ac2 | [
"Apache-2.0"
] | null | null | null | Comprehensive_Project/Comprehensive_Project/Parser/KLT2010-TestVersion-2017/API/kma/header/pomi-def.h | blackberry-pie/Comprehensive_Project | f54e07047fa411d2348422de089e406ffed44ac2 | [
"Apache-2.0"
] | null | null | null | /*
File name: pomi-def.h
Description: Definition of prefinal Eomi field.
Written by: Kang, Seung-Shik 04/11/1997
*/
/*
Definition for 'pomi' field of result structure
1-byte is used for 'Ui/WbV/WfV/AgV'.
Each bit-potistion is as follows.
+-----------------+-----+-----+-----+-----+
| 4 bits(not used)| Ui | WbV | WfV | AgV |
+-----------------+-----+-----+-----+-----+
If Ui-bit is set to 1, 'Ui' is found.
*/
#define POMI_AgV 0x01
#define POMI_WfV 0x02
#define POMI_WbV 0x04
#define POMI_Ui 0x08
/*------------------ end of pomi-def.h -------------------*/
| 24.083333 | 61 | 0.532872 |
56a08d570ed511e28094965b49589dd53f62c060 | 2,016 | swift | Swift | ContentApp/Modules/Advance Search/Views/Table Cells/List Item/ListItemTableViewCell.swift | Ricksoft-OSS/alfresco-mobile-workspace-ios | cd0862a2c7b8e9b76c95a2071307d28a7a337a1f | [
"Apache-2.0"
] | null | null | null | ContentApp/Modules/Advance Search/Views/Table Cells/List Item/ListItemTableViewCell.swift | Ricksoft-OSS/alfresco-mobile-workspace-ios | cd0862a2c7b8e9b76c95a2071307d28a7a337a1f | [
"Apache-2.0"
] | null | null | null | ContentApp/Modules/Advance Search/Views/Table Cells/List Item/ListItemTableViewCell.swift | Ricksoft-OSS/alfresco-mobile-workspace-ios | cd0862a2c7b8e9b76c95a2071307d28a7a337a1f | [
"Apache-2.0"
] | null | null | null | //
// Copyright (C) 2005-2021 Alfresco Software Limited.
//
// This file is part of the Alfresco Content Mobile iOS App.
//
// 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 UIKit
class ListItemTableViewCell: UITableViewCell, CellConfigurable {
@IBOutlet weak var baseView: UIView!
@IBOutlet weak var titleLabel: UILabel!
@IBOutlet weak var checkBoxImageView: UIImageView!
var viewModel: ListItemCellViewModel?
override func awakeFromNib() {
super.awakeFromNib()
}
func setup(viewModel: RowViewModel) {
guard let viewModel = viewModel as? ListItemCellViewModel else { return }
self.viewModel = viewModel
self.titleLabel.text = NSLocalizedString(viewModel.title ?? "", comment: "")
self.checkBoxImageView.image = viewModel.image
titleLabel.accessibilityIdentifier = "\(String(describing: self.titleLabel.text))"
checkBoxImageView.accessibilityIdentifier = "\(String(describing: self.titleLabel.text))"
}
// MARK: - Apply Themes and Localization
func applyTheme(with service: MaterialDesignThemingService?) {
guard let currentTheme = service?.activeTheme else { return }
baseView.backgroundColor = currentTheme.surfaceColor
titleLabel.applyStyleSubtitle1OnSurface(theme: currentTheme)
}
@IBAction func selectTableCellButtonAction(_ sender: Any) {
guard let viewModel = self.viewModel else { return }
viewModel.didSelectListItem?()
}
}
| 38.769231 | 97 | 0.716766 |
2f14abda5a2716e3bc8773de02045b960683f0b7 | 3,698 | php | PHP | resources/views/membership_cards_for_individuals/show.blade.php | mohIbrahim/ghazala | 8cc3d50c3bca98d27719b8726cef0ba89ed1c2dd | [
"MIT"
] | null | null | null | resources/views/membership_cards_for_individuals/show.blade.php | mohIbrahim/ghazala | 8cc3d50c3bca98d27719b8726cef0ba89ed1c2dd | [
"MIT"
] | null | null | null | resources/views/membership_cards_for_individuals/show.blade.php | mohIbrahim/ghazala | 8cc3d50c3bca98d27719b8726cef0ba89ed1c2dd | [
"MIT"
] | null | null | null | @extends('layouts.app')
@section('title')
الكارت {{$membershipCard->serial}}
@endsection
@section('content')
<div class="col-xs-10 col-xs-offset-1 col-sm-10 col-sm-offset-1 col-md-10 col-md-offset-1 col-lg-10 col-lg-offset-1">
<div class="panel panel-primary">
<div class="panel-heading">
<h3 class="panel-title text-center arabic-direction"><strong> الكارت:{{$membershipCard->serial}} </strong></h3>
</div>
<div class="panel-body">
<div class="table-responsive arabic-direction">
<table class="table table-striped table-condensed table-hover">
<tbody class="text-right">
<tr>
<td><strong>الكود الكارت:</strong></td>
<td>{{ $membershipCard->serial }}</td>
</tr>
<tr>
<td><strong>كود الوحدة:</strong></td>
<td>
@if($membershipCard->unit)
<a href="{{ action('UnitsController@show', ['id'=>$membershipCard->unit->id]) }}" target="_blank">
{{ $membershipCard->unit->code }}
</a>
@endif
</td>
</tr>
<tr>
<td><strong>اسم مالك الوحدة:</strong></td>
<td>
@if($membershipCard->owner)
<a href="{{ action('OwnersController@show', ['slug'=>$membershipCard->owner->slug]) }}" target="_blandk">
{{ $membershipCard->owner->name }}
</a>
@endif
</td>
</tr>
<tr>
<td><strong>نوع الكارت:</strong></td>
<td>{{ $membershipCard->type }}</td>
</tr>
<tr>
<td><strong>تاريخ الإصدار:</strong></td>
<td>{{ ($membershipCard->release_date)?$membershipCard->release_date->format('d-m-Y') : "" }}</td>
</tr>
<tr>
<td><strong>حالة الكارت:</strong></td>
<td>{{ ($membershipCard->status)? "فعّال":"غير فعّال" }}</td>
</tr>
<tr>
<td><strong>هل تم تسليم الكارت؟</strong></td>
<td>{{ ($membershipCard->delivered)? "نعم" : "لا" }}</td>
</tr>
<tr>
<td><strong>تاريخ تسليم الكارت:</strong></td>
<td>{{ ($membershipCard->delivered_date)? $membershipCard->delivered_date->format('d-m-Y'): '' }}</td>
</tr>
<tr>
<td><strong>التعليقات:</strong></td>
<td>{{ $membershipCard->comments }}</td>
</tr>
<tr>
<td><strong>إنشاء من قبل المستخدم:</strong></td>
<td>{{ $membershipCard->creator->name }}</td>
</tr>
<tr>
<td><strong>تاريخ و وقت الإنشاء:</strong></td>
<td>{{ $membershipCard->created_at }}</td>
</tr>
<tr>
<td><strong>تاريخ و وقت التعديل:</strong></td>
<td>{{ $membershipCard->updated_at }}</td>
</tr>
@if(in_array('update_membership_cards_for_individuals', $permissions))
<tr>
<td><strong>تعديل:</strong></td>
<td><a href="{{action('MembershipCardsForIndividualsController@edit',['id'=>$membershipCard->id]) }}">تعديل</a></td>
</tr>
@endif
@if(in_array('delete_membership_cards_for_individuals', $permissions))
<tr>
<td><strong>حذف:</strong></td>
<td><button type="button" class="btn btn-danger" data-toggle="modal" data-target="#myModal">حذف الوحدة</button></td>
</tr>
@endif
</tbody>
</table>
</div>
</div>
</div>
</div>
@include('partial.deleteConfirm',['name'=>$membershipCard->serial,
'id'=>$membershipCard->id,
'message'=>'هل انت متأكد تريد حذف الكارت',
'route'=>'MembershipCardsForIndividualsController@destroy'])
@endsection
| 29.822581 | 127 | 0.523797 |
fb1fbde2c443a408162d2b46ec8455e49784c3da | 1,074 | go | Go | pagination_test.go | oussama4/gopify | b4c52e6f2eb135ad7727fcfa014f5baff252b0dd | [
"MIT"
] | 4 | 2021-09-20T08:48:16.000Z | 2022-02-01T15:47:30.000Z | pagination_test.go | oussama4/gopify | b4c52e6f2eb135ad7727fcfa014f5baff252b0dd | [
"MIT"
] | 1 | 2022-02-01T20:49:05.000Z | 2022-02-01T20:49:05.000Z | pagination_test.go | oussama4/gopify | b4c52e6f2eb135ad7727fcfa014f5baff252b0dd | [
"MIT"
] | 1 | 2021-12-19T17:35:00.000Z | 2021-12-19T17:35:00.000Z | package gopify
import (
"errors"
"testing"
)
func TestExtractPagination(t *testing.T) {
cases := []struct {
linkHeader string
expectedPagination *Pagination
expectedError error
}{
{
linkHeader: "invalid header",
expectedPagination: nil,
expectedError: errors.New("invalid header"),
},
{
linkHeader: `<https://resource.url?page_info=next_cursor>; rel="next"`,
expectedPagination: &Pagination{Next: "next_cursor"},
expectedError: nil,
},
{
linkHeader: `<https://resource.url?page_info=next_cursor>; rel="next", <http://resource.url?page_info=previous_cursor>; rel="previous"`,
expectedPagination: &Pagination{Next: "next_cursor", Previous: "previous_cursor"},
expectedError: nil,
},
}
for _, c := range cases {
pagination, err := extractPagination(c.linkHeader)
if pagination != c.expectedPagination && c.expectedError != err {
t.Errorf("expected pagination: %v, and error : %v, but got : %v, %v", c.expectedPagination, c.expectedError, pagination, err)
}
}
}
| 28.263158 | 147 | 0.659218 |
041813b54e10edef5ca62adc2aaecf888b239bc6 | 1,150 | lua | Lua | init_buttons.lua | angeljesmar/corona-GameTemplateWStoryBoard | 98d2c6e9a77fad302b8a7f6bff9df7846629a139 | [
"MIT"
] | 1 | 2015-01-06T08:22:14.000Z | 2015-01-06T08:22:14.000Z | init_buttons.lua | angeljesmar/corona-GameTemplateWStoryBoard | 98d2c6e9a77fad302b8a7f6bff9df7846629a139 | [
"MIT"
] | null | null | null | init_buttons.lua | angeljesmar/corona-GameTemplateWStoryBoard | 98d2c6e9a77fad302b8a7f6bff9df7846629a139 | [
"MIT"
] | null | null | null | _G.buttons = {
about = {
default = "res/btn_about.png",
defaultX = 160,
defaultY = 32,
over = "res/btn_about_over.png",
overX = 160,
overY = 32,
id = "btnAbout",
text = "",
font = "Helvetica",
textColor = { 255, 255, 255, 255 },
emboss = false
},
help = {
default = "res/btn_help.png",
defaultX = 160,
defaultY = 32,
over = "res/btn_help_over.png",
overX = 160,
overY = 32,
id = "btnHelp",
text = "",
font = "Helvetica",
textColor = { 255, 255, 255, 255 },
emboss = false
},
play = {
default = "res/btn_play.png",
defaultX = 160,
defaultY = 32,
over = "res/btn_play_over.png",
overX = 160,
overY = 32,
id = "btnPlay",
text = "",
font = "Helvetica",
textColor = { 255, 255, 255, 255 },
emboss = false
},
settings = {
default = "res/btn_settings.png",
defaultX = 160,
defaultY = 32,
over = "res/btn_settings_over.png",
overX = 160,
overY = 32,
id = "btnSettings",
text = "",
font = "Helvetica",
textColor = { 255, 255, 255, 255 },
emboss = false
}
}
| 20.535714 | 39 | 0.51913 |
5375e94df5b72d2106fc9b0909da20ab00a2b25c | 71 | sql | SQL | hasura/migrations/default/1650979928645_alter_table_public_comment_alter_column_rating_id/up.sql | eoscostarica/rate.eoscostarica.io | 53cdcaa808414eb418d5e37f85391d1a367553f3 | [
"MIT"
] | 5 | 2018-09-11T01:45:02.000Z | 2018-10-01T00:31:04.000Z | hasura/migrations/default/1650979928645_alter_table_public_comment_alter_column_rating_id/up.sql | eoscostarica/rate.eoscostarica.io | 53cdcaa808414eb418d5e37f85391d1a367553f3 | [
"MIT"
] | 46 | 2018-09-11T01:37:01.000Z | 2018-10-22T03:28:44.000Z | hasura/migrations/default/1650979928645_alter_table_public_comment_alter_column_rating_id/up.sql | eoscostarica/rate.eoscostarica.io | 53cdcaa808414eb418d5e37f85391d1a367553f3 | [
"MIT"
] | 2 | 2018-09-19T22:39:21.000Z | 2018-09-25T18:09:42.000Z | alter table "public"."comment" alter column "rating_id" drop not null;
| 35.5 | 70 | 0.760563 |
4f2328e8086cfedd40909e4150d00dcb62dd1d35 | 79,593 | sql | SQL | framework/Targets/wordpress_3_2/plugins/pretty-link_1_5_2/database.sql | UncleWillis/BugBox | 25682f25fc3222db383649a4924bcd65f2ddcb34 | [
"BSD-3-Clause"
] | 1 | 2019-01-25T21:32:42.000Z | 2019-01-25T21:32:42.000Z | framework/Targets/wordpress_3_2/plugins/pretty-link_1_5_2/database.sql | UMD-SEAM/bugbox | 1753477cbca12fe43446d8ded320f77894671dfe | [
"BSD-3-Clause"
] | null | null | null | framework/Targets/wordpress_3_2/plugins/pretty-link_1_5_2/database.sql | UMD-SEAM/bugbox | 1753477cbca12fe43446d8ded320f77894671dfe | [
"BSD-3-Clause"
] | 1 | 2018-04-17T06:04:09.000Z | 2018-04-17T06:04:09.000Z | -- MySQL dump 10.13 Distrib 5.5.28, for debian-linux-gnu (i686)
--
-- Host: localhost Database: wordpress_3_2
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-- Server version 5.5.28-1
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/*!40101 SET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='NO_AUTO_VALUE_ON_ZERO' */;
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`tag_name` varchar(250) DEFAULT NULL,
`updated_date` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
PRIMARY KEY (`id`),
KEY `image_id` (`image_id`),
KEY `category_id` (`category_id`),
KEY `tag_name` (`tag_name`)
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`label` varchar(255) DEFAULT NULL,
`type` int(4) DEFAULT NULL,
`numeric_field` tinyint(1) NOT NULL DEFAULT '0',
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`keyboard_type` tinyint(1) DEFAULT NULL,
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`form_id` int(11) NOT NULL AUTO_INCREMENT,
`form_name` varchar(30) DEFAULT NULL,
`form` text,
`css` text,
`fields_used` text,
`category` tinyint(1) DEFAULT NULL,
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| 71.899729 | 35,505 | 0.668112 |
5f0869d1bb143e039be62686b221b43b037c362f | 856 | tsx | TypeScript | src/pages/academy/_video-banner.tsx | mohammad-rework/deriv-com | 3a13751535bc6b4aa6a903299468251670e64338 | [
"Apache-2.0"
] | 1 | 2021-11-08T10:23:34.000Z | 2021-11-08T10:23:34.000Z | src/pages/academy/_video-banner.tsx | mohammad-rework/deriv-com | 3a13751535bc6b4aa6a903299468251670e64338 | [
"Apache-2.0"
] | null | null | null | src/pages/academy/_video-banner.tsx | mohammad-rework/deriv-com | 3a13751535bc6b4aa6a903299468251670e64338 | [
"Apache-2.0"
] | null | null | null | import React from 'react'
import Dbanner from './components/video-banner/_DBanner'
import { FeaturedVideoListDataType, NonFeaturedVideoListDataType } from './index'
import { Flex } from 'components/containers'
export type VideoBannerProps = {
featured_video_list_data: FeaturedVideoListDataType
non_featured_video_list_data: NonFeaturedVideoListDataType
}
const VideoBanner = ({
featured_video_list_data,
non_featured_video_list_data,
}: VideoBannerProps) => {
return (
<Flex>
{non_featured_video_list_data && non_featured_video_list_data.length && (
<Dbanner
featured_video_list_data={featured_video_list_data}
non_featured_video_list_data={non_featured_video_list_data}
/>
)}
</Flex>
)
}
export default VideoBanner
| 30.571429 | 85 | 0.693925 |
652605d299fd300a1dad2460c429b891eac6cf67 | 3,011 | rs | Rust | src/lib.rs | lights0123/ddelta-rs | 17662629fda4cbb3e2f38aa36626c47ebba6f30b | [
"MIT"
] | 6 | 2020-04-06T16:59:37.000Z | 2021-03-30T15:40:01.000Z | src/lib.rs | lights0123/ddelta-rs | 17662629fda4cbb3e2f38aa36626c47ebba6f30b | [
"MIT"
] | null | null | null | src/lib.rs | lights0123/ddelta-rs | 17662629fda4cbb3e2f38aa36626c47ebba6f30b | [
"MIT"
] | null | null | null | //! A rust port of [ddelta], which is a streaming and more efficient version of [bsdiff]. The
//! output created by this program is sometimes (when using [`generate`]) compatible with the
//! original C tool, [ddelta], but not with [bsdiff]. This library may use up to 5 times the old
//! file size + the new file size, (5 × min(o, 2^31-1) + min(n, 2^31-1)), up to 12GiB. To control
//! this, see the `chunk_sizes` parameter of [`generate_chunked`].
//!
//! **Note**: the patches created by program should be compressed. If not compressed, the output may
//! actually be larger than just including the new file. You might want to feed the patch file
//! directly to an [encoder][XzEncoder], and read via a
//! [decoder implementing a compression algorithm][XzDecoder] to not require much disk space.
//! Additionally, no checksum is performed, so you should strongly consider doing a checksum of at
//! least either the old or new file once written.
//!
//! ## Features
//!
//! This crate optionally supports compiling the c library, divsufsort, which is enabled by default.
//! A Rust port is available; however, it has worse performance than the C version. If you'd like
//! to use the Rust version instead, for example if you don't have a C compiler installed, add
//! `default-features = false` to your Cargo.toml, i.e.
//!
//! ```toml
//! [dependencies]
//! ddelta = { version = "0.1.0", default-features = false }
//! ```
//!
//! [ddelta]: https://github.com/julian-klode/ddelta
//! [bsdiff]: http://www.daemonology.net/bsdiff/
//! [XzEncoder]: https://docs.rs/xz2/*/xz2/write/struct.XzEncoder.html
//! [XzDecoder]: https://docs.rs/xz2/*/xz2/read/struct.XzDecoder.html
use byteorder::BigEndian;
use zerocopy::{AsBytes, FromBytes, Unaligned, I64, U64};
use anyhow::Result;
#[cfg(feature = "diff")]
pub use diff::{generate, generate_chunked};
pub use patch::{apply, apply_chunked};
const DDELTA_MAGIC: &[u8; 8] = b"DDELTA40";
#[cfg(feature = "diff")]
mod diff;
mod patch;
/// The current state of the generator.
///
/// Passed to a callback periodically to give feedback, such as updating a progress bar.
#[derive(Eq, PartialEq, Copy, Clone, Hash, Debug)]
#[cfg(feature = "diff")]
pub enum State {
/// The new or old file is currently being read. This is currently only used in
/// [`generate_chunked`].
Reading,
/// The internal algorithm, divsufsort, is currently being run.
Sorting,
/// The generator is currently working its way through the data. The number represents how much
/// of the new file has been worked through. In other words, if calculating a percentage, divide
/// this number by the size of the new file.
Working(u64),
}
#[derive(Debug, Copy, Clone, FromBytes, AsBytes, Unaligned)]
#[repr(C)]
struct PatchHeader {
magic: [u8; 8],
new_file_size: U64<BigEndian>,
}
#[derive(Debug, Copy, Clone, FromBytes, AsBytes, Unaligned)]
#[repr(C)]
struct EntryHeader {
diff: U64<BigEndian>,
extra: U64<BigEndian>,
seek: I64<BigEndian>,
}
| 39.618421 | 100 | 0.696114 |
feea767009355a0b71c77d474ccbc46a8c95d85e | 103,807 | html | HTML | paper-mining/nips/nips2004/nips-2004-Self-Tuning_Spectral_Clustering.html | makerhacker/makerhacker.github.io | abe4da269cc42f7f9f5f168cbc8dfc60e6b3dec5 | [
"MIT"
] | 21 | 2015-01-18T20:28:43.000Z | 2022-03-15T14:02:16.000Z | paper-mining/nips/nips2004/nips-2004-Self-Tuning_Spectral_Clustering.html | makerhacker/makerhacker.github.io | abe4da269cc42f7f9f5f168cbc8dfc60e6b3dec5 | [
"MIT"
] | null | null | null | paper-mining/nips/nips2004/nips-2004-Self-Tuning_Spectral_Clustering.html | makerhacker/makerhacker.github.io | abe4da269cc42f7f9f5f168cbc8dfc60e6b3dec5 | [
"MIT"
] | 14 | 2015-01-07T16:15:46.000Z | 2022-03-14T03:58:16.000Z | <!DOCTYPE html>
<html>
<head>
<meta charset=utf-8>
<title>161 nips-2004-Self-Tuning Spectral Clustering</title>
</head>
<body>
<p><a title="nips" href="../nips_home.html">nips</a> <a title="nips-2004" href="../home/nips2004_home.html">nips2004</a> <a title="nips-2004-161" href="#">nips2004-161</a> knowledge-graph by maker-knowledge-mining</p><script async src="//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>
<!-- maker adsense -->
<ins class="adsbygoogle"
style="display:inline-block;width:728px;height:90px"
data-ad-client="ca-pub-5027806277543591"
data-ad-slot="4192012269"></ins>
<script>
(adsbygoogle = window.adsbygoogle || []).push({});
</script>
<h1>161 nips-2004-Self-Tuning Spectral Clustering</h1>
<br/><p>Source: <a title="nips-2004-161-pdf" href="http://papers.nips.cc/paper/2619-self-tuning-spectral-clustering.pdf">pdf</a></p><p>Author: Lihi Zelnik-manor, Pietro Perona</p><p>Abstract: We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. We first propose that a ‘local’ scale should be used to compute the affinity between each pair of points. This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. This leads to a new algorithm in which the final randomly initialized k-means stage is eliminated. 1</p><p>Reference: <a title="nips-2004-161-reference" href="../nips2004_reference/nips-2004-Self-Tuning_Spectral_Clustering_reference.html">text</a></p><br/><h2>Summary: the most important sentenses genereted by tfidf model</h2><p>sentIndex sentText sentNum sentScore</p><p>1 html Abstract We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. [sent-9, score-0.393]
</p><p>2 This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. [sent-11, score-0.614]
</p><p>3 We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. [sent-12, score-0.359]
</p><p>4 An alternative clustering approach, which was shown to handle such structured data is spectral clustering. [sent-18, score-0.41]
</p><p>5 It does not require estimating an explicit model of data distribution, rather a spectral analysis of the matrix of point-to-point similarities. [sent-19, score-0.276]
</p><p>6 There are still open issues: (i) Selection of the appropriate scale in which the data is to be analyzed, (ii) Clustering data that is distributed according to different scales, (iii) Clustering with irregular background clutter, and, (iv) Estimating automatically the number of groups. [sent-26, score-0.23]
</p><p>7 , sn } in Rl cluster them into C clusters as follows: −d2 (s ,s ) i j 1. [sent-34, score-0.223]
</p><p>8 Form the affinity matrix A ∈ Rn×n defined by Aij = exp ( ) for i = j σ2 and Aii = 0, where d(si , sj ) is some distance function, often just the Euclidean σ = 0. [sent-35, score-0.202]
</p><p>9 03125 σ=1 Figure 1: Spectral clustering without local scaling (using the NJW algorithm. [sent-41, score-0.409]
</p><p>10 ) Top row: When the data incorporates multiple scales standard spectral clustering fails. [sent-42, score-0.48]
</p><p>11 This highlights the high impact σ has on the clustering quality. [sent-45, score-0.287]
</p><p>12 Define D to be a diagonal matrix with Dii = malized affinity matrix L = D−1/2 AD−1/2 . [sent-51, score-0.181]
</p><p>13 , xC , the C largest eigenvectors of L, and form the matrix X = [x1 , . [sent-58, score-0.33]
</p><p>14 Treat each row of Y as a point in RC and cluster via k-means. [sent-65, score-0.196]
</p><p>15 Assign the original point si to cluster c if and only if the corresponding row i of the matrix Y was assigned to cluster c. [sent-67, score-0.508]
</p><p>16 In Section 2 we analyze the effect of σ on the clustering and suggest a method for setting it automatically. [sent-68, score-0.284]
</p><p>17 In Section 3 we suggest a scheme for finding automatically the number of groups C. [sent-70, score-0.365]
</p><p>18 Our new spectral clustering algorithm is summarized in Section 4. [sent-71, score-0.41]
</p><p>19 2 Local Scaling As was suggested by [6] the scaling parameter is some measure of when two points are considered similar. [sent-73, score-0.176]
</p><p>20 [5] suggested selecting σ automatically by running their clustering algorithm repeatedly for a number of values of σ and selecting the one which provides least distorted clusters of the rows of Y . [sent-77, score-0.822]
</p><p>21 Moreover, when the input data includes clusters with different local statistics there may not be a singe value of σ that works well for all the data. [sent-80, score-0.214]
</p><p>22 When the data contains multiple scales, even using the optimal σ fails to provide good clustering (see examples at the right of top row). [sent-82, score-0.288]
</p><p>23 The affinities across clusters are larger than the affinities within the background cluster. [sent-87, score-0.174]
</p><p>24 Introducing Local Scaling: Instead of selecting a single scaling parameter σ we propose to calculate a local scaling parameter σi for each data point si . [sent-90, score-0.486]
</p><p>25 The distance from si to sj as ‘seen’ by si is d(si , sj )/σi while the converse is d(sj , si )/σj . [sent-91, score-0.776]
</p><p>26 The selection of the local scale σi can be done by studying the local statistics of the neighborhood of point si . [sent-93, score-0.379]
</p><p>27 Figure 2 provides a visualization of the effect of the suggested local scaling. [sent-97, score-0.172]
</p><p>28 Since the data resides in multiple scales (one cluster is tight and the other is sparse) the standard approach to estimating affinities fails to capture the data structure (see Figure 2. [sent-98, score-0.256]
</p><p>29 Local scaling automatically finds the two scales and results in high affinities within clusters and low affinities across clusters (see Figure 2. [sent-100, score-0.517]
</p><p>30 We tested the power of local scaling by clustering the data set of Figure 1, plus four additional examples. [sent-103, score-0.409]
</p><p>31 In spite of the multiple scales and the various types of structure, the groups now match the intuitive solution. [sent-108, score-0.238]
</p><p>32 3 Estimating the Number of Clusters Having defined a scheme to set the scale parameter automatically we are left with one more free parameter: the number of clusters. [sent-109, score-0.217]
</p><p>33 This parameter is usually set manually and Figure 3: Our clustering results. [sent-110, score-0.247]
</p><p>34 The first 10 eigenvalues of L corresponding to the top row data sets of Figure 3. [sent-134, score-0.304]
</p><p>35 The suggested scheme turns out to lead to a new spatial clustering algorithm. [sent-137, score-0.406]
</p><p>36 1 The Intuitive Solution: Analyzing the Eigenvalues One possible approach to try and discover the number of groups is to analyze the eigenvalues of the affinity matrix. [sent-139, score-0.323]
</p><p>37 1) will be a repeated eigenvalue of magnitude 1 with multiplicity equal to the number of groups C. [sent-141, score-0.396]
</p><p>38 Examining the eigenvalues of our locally scaled matrix, corresponding to clean data-sets, indeed shows that the multiplicity of eigenvalue 1 equals the number of groups. [sent-143, score-0.392]
</p><p>39 An alternative approach would be to search for a drop in the magnitude of the eigenvalues (this was pursued to some extent by Polito and Perona in [7]). [sent-145, score-0.194]
</p><p>40 The eigenvalues of L are the union of the eigenvalues of the sub-matrices corresponding to each cluster. [sent-147, score-0.353]
</p><p>41 This implies the eigenvalues depend on the structure of the individual clusters and thus no assumptions can be placed on their values. [sent-148, score-0.29]
</p><p>42 Figure 4 shows the first 10 eigenvalues corresponding to the top row examples of Figure 3. [sent-150, score-0.304]
</p><p>43 It highlights the different patterns of distribution of eigenvalues for different data sets. [sent-151, score-0.195]
</p><p>44 , C), its eigenvalues and eigenvectors are the union of the eigenvalues and eigenvectors of its blocks padded appropriately with zeros (see [6, 5]). [sent-159, score-0.968]
</p><p>45 However, as was shown above, the eigenvalue 1 is bound to be a repeated eigenvalue with multiplicity equal to the number of groups C. [sent-161, score-0.434]
</p><p>46 This, however, implies that even if the eigensolver provided us the rotated set of vectors, ˆ ˆ we are still guaranteed that there exists a rotation R such that each row in the matrix X R has a single non-zero entry. [sent-164, score-0.38]
</p><p>47 Since the eigenvectors of L are the union of the eigenvectors of its individual blocks (padded with zeros), taking more than the first C eigenvectors will result in more than one non-zero entry in some of the rows. [sent-165, score-0.832]
</p><p>48 Taking fewer eigenvectors we do not have a full basis spanning the subspace, thus depending on the initial X there might or might not exist such a rotation. [sent-166, score-0.228]
</p><p>49 For each possible group number C we recover the rotation which best aligns X’s columns with the canonical coordinate system. [sent-169, score-0.536]
</p><p>50 Let Z ∈ Rn×C be the matrix obtained after rotating the eigenvector matrix X, i. [sent-170, score-0.234]
</p><p>51 We wish to recover the rotation R for which in every row in Z there will be at most one non-zero entry. [sent-173, score-0.329]
</p><p>52 We thus define a cost function: n C J= i=1 j=1 2 Zij Mi2 (3) Minimizing this cost function over all possible rotations will provide the best alignment with the canonical coordinate system. [sent-174, score-0.526]
</p><p>53 This is done using the gradient descent scheme described in Appendix A. [sent-175, score-0.186]
</p><p>54 The number of groups is taken as the one providing the minimal cost (if several group numbers yield practically the same minimal cost, the largest of those is selected). [sent-176, score-0.52]
</p><p>55 We start by aligning the top two eigenvectors (as well as possible). [sent-178, score-0.362]
</p><p>56 Then, at each step of the search (up to the maximal group number), we add a single eigenvector to the already rotated ones. [sent-179, score-0.283]
</p><p>57 This can be viewed as taking the alignment result of the previous group number as an initialization to the current one. [sent-180, score-0.322]
</p><p>58 The alignment of this new set of eigenvectors is extremely fast (typically a few iterations) since the initialization is good. [sent-181, score-0.406]
</p><p>59 The overall run time of this incremental procedure is just slightly longer than aligning all the eigenvectors in a non-incremental way. [sent-182, score-0.321]
</p><p>60 Using this scheme to estimate the number of groups on the data set of Figure 3 provided a correct result for all but one (for the right-most dataset at the bottom row we predicted 2 clusters instead of 3). [sent-183, score-0.477]
</p><p>61 Corresponding plots of the alignment quality for different group numbers are shown in Figure 5. [sent-184, score-0.325]
</p><p>62 Yu and Shi [11] suggested rotating normalized eigenvectors to obtain an optimal segmentation. [sent-185, score-0.377]
</p><p>63 , setting Mi = 1 and Zij = 0 otherwise) and using SVD to recover the rotation which best aligns the columns of X with those of Z. [sent-188, score-0.247]
</p><p>64 In our experiments we noticed that this iterative method can easily get stuck in local minima and thus does not reliably find the optimal alignment and the group number. [sent-189, score-0.364]
</p><p>65 [3] who assigned points to clusters according to the maximal entry in the corresponding row of the eigenvector matrix. [sent-191, score-0.396]
</p><p>66 This works well when there are no repeated eigenvalues as then the eigenvectors 0. [sent-192, score-0.422]
</p><p>67 (3)) for varying group numbers corresponding to the top row data sets of Figure 3. [sent-210, score-0.293]
</p><p>68 The selected group number marked by a red circle, corresponds to the largest group number providing minimal cost (costs up to 0. [sent-211, score-0.455]
</p><p>69 used a non-normalized affinity matrix thus were not certain to obtain a repeated eigenvalue, however, this could easily happen and then the clustering would fail. [sent-215, score-0.346]
</p><p>70 (ii) Since the final clustering can be conducted by non-maximum suppression, we obtain clustering results for all the inspected group numbers at a tiny additional cost. [sent-217, score-0.638]
</p><p>71 When the data is highly noisy, one can still employ k-means, or better, EM, to cluster the rows of Z. [sent-218, score-0.187]
</p><p>72 However, since the data is now aligned with the canonical coordinate scheme we can obtain by non-maximum suppression an excellent initialization so very few iterations suffice. [sent-219, score-0.325]
</p><p>73 Compute the local scale σi for each point si ∈ S using Eq. [sent-224, score-0.3]
</p><p>74 Define D to be a diagonal matrix with Dii = j=1 Aij and construct the nor−1/2 ˆ −1/2 malized affinity matrix L = D . [sent-230, score-0.181]
</p><p>75 , xC the C largest eigenvectors of L and form the matrix X = [x1 , . [sent-235, score-0.33]
</p><p>76 , xC ] ∈ Rn×C , where C is the largest possible group number. [sent-238, score-0.186]
</p><p>77 Recover the rotation R which best aligns X’s columns with the canonical coordinate system using the incremental gradient descent scheme (see also Appendix A). [sent-240, score-0.518]
</p><p>78 Grade the cost of the alignment for each group number, up to C, according to Eq. [sent-242, score-0.369]
</p><p>79 Set the final group number Cb est to be the largest group number with minimal alignment cost. [sent-245, score-0.56]
</p><p>80 Take the alignment result Z of the top Cb est eigenvectors and assign the original 2 2 point si to cluster c if and only if maxj (Zij ) = Zic . [sent-247, score-0.75]
</p><p>81 If highly noisy data, use the previous step result to initialize k-means, or EM, clustering on the rows of Z. [sent-249, score-0.346]
</p><p>82 The number of groups and the corresponding segmentation were obtained automatically. [sent-252, score-0.232]
</p><p>83 html 5 Discussion & Conclusions Spectral clustering practitioners know that selecting good parameters to tune the clustering process is an art requiring skill and patience. [sent-260, score-0.608]
</p><p>84 Automating spectral clustering was the main motivation for this study. [sent-261, score-0.41]
</p><p>85 The key ideas we introduced are three: (a) using a local scale, rather than a global one, (b) estimating the scale from the data, and (c) rotating the eigenvectors to create the maximally sparse representation. [sent-262, score-0.473]
</p><p>86 We proposed an automated spectral clustering algorithm based on these ideas: it computes automatically the scale and the number of groups and it can handle multi-scale data which are problematic for previous approaches. [sent-263, score-0.729]
</p><p>87 For instance, the local scale σ might be better estimated by a method which relies on more informative local statistics. [sent-265, score-0.215]
</p><p>88 Acknowledgments: Finally, we wish to thank Yair Weiss for providing us his code for spectral clustering. [sent-270, score-0.201]
</p><p>89 Longuet-Higgins “Feature grouping by ‘relocalisation’ of eigenvectors of the proximity matrix” In Proc. [sent-301, score-0.27]
</p><p>90 A Recovering the Aligning Rotation To find the best alignment for a set of eigenvectors we adopt a gradient descent scheme similar to that suggested in [2]. [sent-314, score-0.648]
</p><p>91 There, Givens rotations where used to recover a rotation which diagonalizes a symmetric matrix by minimizing a cost function which measures the diagonality of the matrix. [sent-315, score-0.399]
</p><p>92 Similarly, here, we define a cost function which measures the alignment quality of a set of vectors and prove that the gradient descent, using Givens rotations, converges. [sent-316, score-0.316]
</p><p>93 Note, that the indices mi of the maximal entries of the rows of X might be different than those of the optimal Z. [sent-320, score-0.315]
</p><p>94 Using the gradient descent scheme allows to increase the cost corresponding to part of the rows as long as the overall cost is reduced, thus enabling changing the indices mi . [sent-322, score-0.624]
</p><p>95 Similar to [2] we wish to represent the rotation matrix R in terms of the smallest possible ˜ number of parameters. [sent-323, score-0.221]
</p><p>96 Let Gi,j,θ denote a Givens rotation [1] of θ radians (counterclockwise) in the (i, j) coordinate plane. [sent-324, score-0.2]
</p><p>97 Hence, finding the aligning rotation amounts to minimizing the cost function J over Θ ∈ [−π/2, π/2)K . [sent-329, score-0.3]
</p><p>98 Note, that at Θ = 0 we have Zij = 0 for j = mi , Zimi = Mi , and ∂Mi ∂θk = Θ=0 ∂Zimi = ∂θk (k) Aimi (i. [sent-338, score-0.171]
</p><p>99 , near Θ = 0 the maximal ∂2J ∂θl ∂θk entry for each row does not change its index). [sent-340, score-0.203]
</p><p>100 mi = ik or mi = jk 0 if k = l otherwise where (ik , jk ) is the pair (i, j) corresponding to the index k in the index re-mapping discussed above. [sent-344, score-0.51]
</p>
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<p>Author: Lihi Zelnik-manor, Pietro Perona</p><p>Abstract: We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. We first propose that a ‘local’ scale should be used to compute the affinity between each pair of points. This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. This leads to a new algorithm in which the final randomly initialized k-means stage is eliminated. 1</p><p>2 0.25222942 <a title="161-tfidf-2" href="./nips-2004-Hierarchical_Eigensolver_for_Transition_Matrices_in_Spectral_Methods.html">79 nips-2004-Hierarchical Eigensolver for Transition Matrices in Spectral Methods</a></p>
<p>Author: Chakra Chennubhotla, Allan D. Jepson</p><p>Abstract: We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest eigenvalues, and the corresponding eigenvectors. In particular, the eigen problem is first solved at the coarsest level of the representation. The approximate eigen solution is then interpolated over successive levels of the hierarchy. A small number of power iterations are employed at each stage to correct the eigen solution. The typical speedups obtained by a Matlab implementation of our fast eigensolver over a standard sparse matrix eigensolver [13] are at least a factor of ten for large image sizes. The hierarchical representation has proven to be effective in a min-cut based segmentation algorithm that we proposed recently [8]. 1 Spectral Methods Graph-theoretic spectral methods have gained popularity in a variety of application domains: segmenting images [22]; embedding in low-dimensional spaces [4, 5, 8]; and clustering parallel scientific computation tasks [19]. Spectral methods enable the study of properties global to a dataset, using only local (pairwise) similarity or affinity measurements between the data points. The global properties that emerge are best understood in terms of a random walk formulation on the graph. For example, the graph can be partitioned into clusters by analyzing the perturbations to the stationary distribution of a Markovian relaxation process defined in terms of the affinity weights [17, 18, 24, 7]. The Markovian relaxation process need never be explicitly carried out; instead, it can be analytically expressed using the leading order eigenvectors, and eigenvalues, of the Markov transition matrix. In this paper we consider the practical application of spectral methods to large datasets. In particular, the eigen decomposition can be very expensive, on the order of O(n 3 ), where n is the number of nodes in the graph. While it is possible to compute analytically the first eigenvector (see §3 below), the remaining subspace of vectors (necessary for say clustering) has to be explicitly computed. A typical approach to dealing with this difficulty is to first sparsify the links in the graph [22] and then apply an efficient eigensolver [13, 23, 3]. In comparison, we propose in this paper a specialized eigensolver suitable for large stochastic matrices with known stationary distributions. In particular, we exploit the spectral properties of the Markov transition matrix to generate hierarchical, successively lower-ranked approximations to the full transition matrix. The eigen problem is solved directly at the coarsest level of representation. The approximate eigen solution is then interpolated over successive levels of the hierarchy, using a small number of power iterations to correct the solution at each stage. 2 Previous Work One approach to speeding up the eigen decomposition is to use the fact that the columns of the affinity matrix are typically correlated. The idea then is to pick a small number of representative columns to perform eigen decomposition via SVD. For example, in the Nystrom approximation procedure, originally proposed for integral eigenvalue problems, the idea is to randomly pick a small set of m columns; generate the corresponding affinity matrix; solve the eigenproblem and finally extend the solution to the complete graph [9, 10]. The Nystrom method has also been recently applied in the kernel learning methods for fast Gaussian process classification and regression [25]. Other sampling-based approaches include the work reported in [1, 2, 11]. Our starting point is the transition matrix generated from affinity weights and we show how building a representational hierarchy follows naturally from considering the stochastic matrix. A closely related work is the paper by Lin on reduced rank approximations of transition matrices [14]. We differ in how we approximate the transition matrices, in particular our objective function is computationally less expensive to solve. In particular, one of our goals in reducing transition matrices is to develop a fast, specialized eigen solver for spectral clustering. Fast eigensolving is also the goal in ACE [12], where successive levels in the hierarchy can potentially have negative affinities. A graph coarsening process for clustering was also pursued in [21, 3]. 3 Markov Chain Terminology We first provide a brief overview of the Markov chain terminology here (for more details see [17, 15, 6]). We consider an undirected graph G = (V, E) with vertices vi , for i = {1, . . . , n}, and edges ei,j with non-negative weights ai,j . Here the weight ai,j represents the affinity between vertices vi and vj . The affinities are represented by a non-negative, symmetric n × n matrix A having weights ai,j as elements. The degree of a node j is n n defined to be: dj = i=1 ai,j = j=1 aj,i , where we define D = diag(d1 , . . . , dn ). A Markov chain is defined using these affinities by setting a transition probability matrix M = AD −1 , where the columns of M each sum to 1. The transition probability matrix defines the random walk of a particle on the graph G. The random walk need never be explicitly carried out; instead, it can be analytically expressed using the leading order eigenvectors, and eigenvalues, of the Markov transition matrix. Because the stochastic matrices need not be symmetric in general, a direct eigen decomposition step is not preferred for reasons of instability. This problem is easily circumvented by considering a normalized affinity matrix: L = D −1/2 AD−1/2 , which is related to the stochastic matrix by a similarity transformation: L = D −1/2 M D1/2 . Because L is symmetric, it can be diagonalized: L = U ΛU T , where U = [u1 , u2 , · · · , un ] is an orthogonal set of eigenvectors and Λ is a diagonal matrix of eigenvalues [λ1 , λ2 , · · · , λn ] sorted in decreasing order. The eigenvectors have unit length uk = 1 and from the form of A and D it can be shown that the eigenvalues λi ∈ (−1, 1], with at least one eigenvalue equal to one. Without loss of generality, we take λ1 = 1. Because L and M are similar we can perform an eigen decomposition of the Markov transition matrix as: M = D1/2 LD−1/2 = D1/2 U Λ U T D−1/2 . Thus an eigenvector u of L corresponds to an eigenvector D 1/2 u of M with the same eigenvalue λ. The Markovian relaxation process after β iterations, namely M β , can be represented as: M β = D1/2 U Λβ U T D−1/2 . Therefore, a particle undertaking a random walk with an initial distribution p 0 acquires after β steps a distribution p β given by: p β = M β p 0 . Assuming the graph is connected, as β → ∞, the Markov chain approaches a unique n stationary distribution given by π = diag(D)/ i=1 di , and thus, M ∞ = π1T , where 1 is a n-dim column vector of all ones. Observe that π is an eigenvector of M as it is easy to show that M π = π and the corresponding eigenvalue is 1. Next, we show how to generate hierarchical, successively low-ranked approximations for the transition matrix M . 4 Building a Hierarchy of Transition Matrices The goal is to generate a very fast approximation, while simultaneously achieving sufficient accuracy. For notational ease, we think of M as a fine-scale representation and M as some coarse-scale approximation to be derived here. By coarsening M further, we can generate successive levels of the representation hierarchy. We use the stationary distribution π to construct a corresponding coarse-scale stationary distribution δ. As we just discussed a critical property of the fine scale Markov matrix M is that it is similar to the symmetric matrix L and we wish to preserve this property at every level of the representation hierarchy. 4.1 Deriving Coarse-Scale Stationary Distribution We begin by expressing the stationary distribution π as a probabilistic mixture of latent distributions. In matrix notation, we have (1) π = K δ, where δ is an unknown mixture coefficient vector of length m, K is an n × m non-negative n kernel matrix whose columns are latent distributions that each sum to 1: i=1 Ki,j = 1 and m n. It is easy to derive a maximum likelihood approximation of δ using an EM type algorithm [16]. The main step is to find a stationary point δ, λ for the Lagrangian: m n i=1 m Ki,j δj + λ πi ln E≡− j=1 δj − 1 . (2) j=1 An implicit step in this EM procedure is to compute the the ownership probability r i,j of the j th kernel (or node) at the coarse scale for the ith node on the fine scale and is given by ri,j = δj Ki,j . m k=1 δk Ki,k (3) The EM procedure allows for an update of both δ and the latent distributions in the kernel matrix K (see §8.3.1 in [6]). For initialization, δ is taken to be uniform over the coarse-scale states. But in choosing kernels K, we provide a good initialization for the EM procedure. Specifically, the Markov matrix M is diffused using a small number of iterations to get M β . The diffusion causes random walks from neighboring nodes to be less distinguishable. This in turn helps us select a small number of columns of M β in a fast and greedy way to be the kernel matrix K. We defer the exact details on kernel selection to a later section (§4.3). 4.2 Deriving the Coarse-Scale Transition Matrix In order to define M , the coarse-scale transition matrix, we break it down into three steps. First, the Markov chain propagation at the coarse scale can be defined as: q k+1 = M q k , (4) where q is the coarse scale probability distribution after k steps of the random walk. Second, we expand q k into the fine scale using the kernels K resulting in a fine scale probability distribution p k : p k = Kq k . (5) k Finally, we lift p k back into the coarse scale by using the ownership probability of the j th kernel for the ith node on the fine grid: n qjk+1 = ri,j pik i=1 (6) Substituting for Eqs.(3) and (5) in Eq. 6 gives n m qjk+1 = i=1 n Ki,t qtk = ri,j t=1 i=1 δj Ki,j m k=1 δk Ki,k m Ki,t qtk . (7) t=1 We can write the preceding equation in a matrix form: q k+1 = diag( δ ) K T diag K δ −1 Kq k . (8) Comparing this with Eq. 4, we can derive the transition matrix M as: M = diag( δ ) K T diag K δ −1 (9) K. It is easy to see that δ = M δ, so δ is the stationary distribution for M . Following the definition of M , and its stationary distribution δ, we can generate a symmetric coarse scale affinity matrix A given by A = M diag(δ) = diag( δ ) K T diag K δ −1 Kdiag(δ) , (10) where we substitute for the expression M from Eq. 9. The coarse-scale affinity matrix A is then normalized to get: L = D−1/2 AD−1/2 ; D = diag(d1 , d2 , · · · , dm ), (11) where dj is the degree of node j in the coarse-scale graph represented by the matrix A (see §3 for degree definition). Thus, the coarse scale Markov matrix M is precisely similar to a symmetric matrix L. 4.3 Selecting Kernels For demonstration purpose, we present the kernel selection details on the image of an eye shown below. To begin with, a random walk is defined where each pixel in the test image is associated with a vertex of the graph G. The edges in G are defined by the standard 8-neighbourhood of each pixel. For the demonstrations in this paper, the edge weight ai,j between neighbouring pixels xi and xj is given by a function of the difference in the 2 corresponding intensities I(xi ) and I(xj ): ai,j = exp(−(I(xi ) − I(xj ))2 /2σa ), where σa is set according to the median absolute difference |I(xi ) − I(xj )| between neighbours measured over the entire image. The affinity matrix A with the edge weights is then used to generate a Markov transition matrix M . The kernel selection process we use is fast and greedy. First, the fine scale Markov matrix M is diffused to M β using β = 4. The Markov matrix M is sparse as we make the affinity matrix A sparse. Every column in the diffused matrix M β is a potential kernel. To facilitate the selection process, the second step is to rank order the columns of M β based on a probability value in the stationary distribution π. Third, the kernels (i.e. columns of M β ) are picked in such a way that for a kernel Ki all of the neighbours of pixel i which are within the half-height of the the maximum value in the kernel Ki are suppressed from the selection process. Finally, the kernel selection is continued until every pixel in the image is within a half-height of the peak value of at least one kernel. If M is a full matrix, to avoid the expense of computing M β explicitly, random kernel centers can be selected, and only the corresponding columns of M β need be computed. We show results from a three-scale hierarchy on the eye image (below). The image has 25 × 20 pixels but is shown here enlarged for clarity. At the first coarse scale 83 kernels are picked. The kernels each correspond to a different column in the fine scale transition matrix and the pixels giving rise to these kernels are shown numbered on the image. Using these kernels as an initialization, the EM procedure derives a coarse-scale stationary distribution δ 21 14 26 4 (Eq. 2), while simultaneously updating the kernel ma12 27 2 19 trix. Using the newly updated kernel matrix K and the 5 8 13 23 30 18 6 9 derived stationary distribution δ a transition matrix M 28 20 15 32 10 22 is generated (Eq. 9). The coarse scale Markov matrix 24 17 7 is then diffused to M β , again using β = 4. The kernel Coarse Scale 1 Coarse Scale 2 selection algorithm is reapplied, this time picking 32 kernels for the second coarse scale. Larger values of β cause the coarser level to have fewer elements. But the exact number of elements depends on the form of the kernels themselves. For the random experiments that we describe later in §6 we found β = 2 in the first iteration and 4 thereafter causes the number of kernels to be reduced by a factor of roughly 1/3 to 1/4 at each level. 72 28 35 44 51 64 82 4 12 31 56 19 77 36 45 52 65 13 57 23 37 5 40 53 63 73 14 29 6 66 38 74 47 24 7 30 41 54 71 78 58 15 8 20 39 48 59 67 25 68 79 21 16 2 11 26 42 49 55 60 75 32 83 43 9 76 50 17 27 61 33 69 80 3 46 18 70 81 34 10 62 22 1 25 11 1 3 16 31 29 At coarser levels of the hierarchy, we expect the kernels to get less sparse and so will the affinity and the transition matrices. In order to promote sparsity at successive levels of the hierarchy we sparsify A by zeroing out elements associated with “small” transition probabilities in M . However, in the experiments described later in §6, we observe this sparsification step to be not critical. To summarize, we use the stationary distribution π at the fine-scale to derive a transition matrix M , and its stationary distribution δ, at the coarse-scale. The coarse scale transition in turn helps to derive an affinity matrix A and its normalized version L. It is obvious that this procedure can be repeated recursively. We describe next how to use this representation hierarchy for building a fast eigensolver. 5 Fast EigenSolver Our goal in generating a hierarchical representation of a transition matrix is to develop a fast, specialized eigen solver for spectral clustering. To this end, we perform a full eigen decomposition of the normalized affinity matrix only at the coarsest level. As discussed in the previous section, the affinity matrix at the coarsest level is not likely to be sparse, hence it will need a full (as opposed to a sparse) version of an eigen solver. However it is typically the case that e ≤ m n (even in the case of the three-scale hierarchy that we just considered) and hence we expect this step to be the least expensive computationally. The resulting eigenvectors are interpolated to the next lower level of the hierarchy by a process which will be described next. Because the eigen interpolation process between every adjacent pair of scales in the hierarchy is similar, we will assume we have access to the leading eigenvectors U (size: m × e) for the normalized affinity matrix L (size: m × m) and describe how to generate the leading eigenvectors U (size: n × e), and the leading eigenvalues S (size: e × 1), for the fine-scale normalized affinity matrix L (size: n × n). There are several steps to the eigen interpolation process and in the discussion that follows we refer to the lines in the pseudo-code presented below. First, the coarse-scale eigenvectors U can be interpolated using the kernel matrix K to generate U = K U , an approximation for the fine-scale eigenvectors (line 9). Second, interpolation alone is unlikely to set the directions of U exactly aligned with U L , the vectors one would obtain by a direct eigen decomposition of the fine-scale normalized affinity matrix L. We therefore update the directions in U by applying a small number of power iterations with L, as given in lines 13-15. e e function (U, S) = CoarseToFine(L, K, U , S) 1: INPUT 2: L, K ⇐ {L is n × n and K is n × m where m n} e e e e 3: U /S ⇐ {leading coarse-scale eigenvectors/eigenvalues of L. U is of size m × e, e ≤ m} 4: OUTPUT 5: U, S ⇐ {leading fine-scale eigenvectors/eigenvalues of L. U is n × e and S is e × 1.} x 10 0.4 3 0.96 0.94 0.92 0.9 0.35 2.5 Relative Error Absolute Relative Error 0.98 Eigen Value |δλ|λ−1 −3 Eigen Spectrum 1 2 1.5 1 5 10 15 20 Eigen Index (a) 25 30 0.2 0.15 0.1 0.5 0.88 0.3 0.25 0.05 5 10 15 20 Eigen Index (b) 25 30 5 10 15 20 Eigen Index 25 30 (c) Figure 1: Hierarchical eigensolver results. (a) comparing ground truth eigenvalues S L (red circles) with multi-scale eigensolver spectrum S (blue line) (b) Relative absolute error between eigenvalues: |S−SL | (c) Eigenvector mismatch: 1 − diag |U T UL | , between SL eigenvectors U derived by the multi-scale eigensolver and the ground truth U L . Observe the slight mismatch in the last few eigenvectors, but excellent agreement in the leading eigenvectors (see text). 6: CONSTANTS: TOL = 1e-4; POWER ITERS = 50 7: “ ” e 8: TPI = min POWER ITERS, log(e × eps/TOL)/ log(min(S)) {eps: machine accuracy} e 9: U = K U {interpolation from coarse to fine} 10: while not converged do 11: Uold = U {n × e matrix, e n} 12: for i = 1 to TPI do 13: U ⇐ LU 14: end for 15: U ⇐ Gram-Schmidt(U ) {orthogonalize U } 16: Le = U T LU {L may be sparse, but Le need not be.} 17: Ue Se UeT = svd(Le ) {eigenanalysis of Le , which is of size e × e.} 18: U ⇐ U Ue {update the leading eigenvectors of L} 19: S = diag(Se ) {grab the leading eigenvalues of L} T 20: innerProd = 1 − diag( Uold U ) {1 is a e × 1 vector of all ones} 21: converged = max[abs(innerProd)] < TOL 22: end while The number of power iterations TPI can be bounded as discussed next. Suppose v = U c where U is a matrix of true eigenvectors and c is a coefficient vector for an arbitrary vector v. After TPI power iterations v becomes v = U diag(S TPI )c, where S has the exact eigenvalues. In order for the component of a vector v in the direction Ue (the eth column of U ) not to be swamped by other components, we can limit it’s decay after TPI iterations as TPI follows: (S(e)/S(1)) >= e×eps/TOL, where S(e) is the exact eth eigenvalue, S(1) = 1, eps is the machine precision, TOL is requested accuracy. Because we do not have access to the exact value S(e) at the beginning of the interpolation procedure, we estimate it from the coarse eigenvalues S. This leads to a bound on the power iterations TPI, as derived on the line 9 above. Third, the interpolation process and the power iterations need not preserve orthogonality in the eigenvectors in U . We fix this by Gram-Schmidt orthogonalization procedure (line 16). Finally, there is a still a problem with power iterations that needs to be resolved, in that it is very hard to separate nearby eigenvalues. In particular, for the convergence of the power iterations the ratio that matters is between the (e + 1) st and eth eigenvalues. So the idea we pursue is to use the power iterations only to separate the reduced space of eigenvectors (of dimension e) from the orthogonal subspace (of dimension n − e). We then use a full SVD on the reduced space to update the leading eigenvectors U , and eigenvalues S, for the fine-scale (lines 17-20). This idea is similar to computing the Ritz values and Ritz vectors in a Rayleigh-Ritz method. 6 Interpolation Results Our multi-scale decomposition code is in Matlab. For the direct eigen decomposition, we have used the Matlab program svds.m which invokes the compiled ARPACKC routine [13], with a default convergence tolerance of 1e-10. In Fig. 1a we compare the spectrum S obtained from a three-scale decomposition on the eye image (blue line) with the ground truth, which is the spectrum SL resulting from direct eigen decomposition of the fine-scale normalized affinity matrices L (red circles). There is an excellent agreement in the leading eigenvalues. To illustrate this, we show absolute relative error between the spectra: |S−SL | in Fig. 1b. The spectra agree mostly, except for SL the last few eigenvalues. For a quantitative comparison between the eigenvectors, we plot in Fig. 1c the following measure: 1 − diag(|U T UL |), where U is the matrix of eigenvectors obtained by the multi-scale approximation, UL is the ground-truth resulting from a direct eigen decomposition of the fine-scale affinity matrix L and 1 is a vector of all ones. The relative error plot demonstrates a close match, within the tolerance threshold of 1e-4 that we chose for the multi-scale method, in the leading eigenvector directions between the two methods. The relative error is high with the last few eigen vectors, which suggests that the power iterations have not clearly separated them from other directions. So, the strategy we suggest is to pad the required number of leading eigen basis by about 20% before invoking the multi-scale procedure. Obviously, the number of hierarchical stages for the multi-scale procedure must be chosen such that the transition matrix at the coarsest scale can accommodate the slight increase in the subspace dimensions. For lack of space we are omitting extra results (see Ch.8 in [6]). Next we measure the time the hierarchical eigensolver takes to compute the leading eigenbasis for various input sizes, in comparison with the svds.m procedure [13]. We form images of different input sizes by Gaussian smoothing of i.i.d noise. The Gaussian function has a standard deviation of 3 pixels. The edges in graph G are defined by the standard 8-neighbourhood of each pixel. The edge weights between neighbouring pixels are simply given by a function of the difference in the corresponding intensities (see §4.3). The affinity matrix A with the edge weights is then used to generate a Markov transition matrix M . The fast eigensolver is run on ten different instances of the input image of a given size and the average of these times is reported here. For a fair comparison between the two procedures, we set the convergence tolerance value for the svds.m procedure to be 1e-4, the same as the one used for the fast eigensolver. We found the hierarchical representation derived from this tolerance threshold to be sufficiently accurate for a novel min-cut based segmentation results that we reported in [8]. Also, the subspace dimensionality is fixed to be 51 where we expect (and indeed observe) the leading 40 eigenpairs derived from the multi-scale procedure to be accurate. Hence, while invoking svds.m we compute only the leading 41 eigenpairs. In the table shown below, the first column corresponds to the number of nodes in the graph, while the second and third columns report the time taken in seconds by the svds.m procedure and the Matlab implementation of the multi-scale eigensolver respectively. The fourth column reports the speedups of the multi-scale eigensolver over svds.m procedure on a standard desktop (Intel P4, 2.5GHz, 1GB RAM). Lowering the tolerance threshold for svds.m made it faster by about 20 − 30%. Despite this, the multi-scale algorithm clearly outperforms the svds.m procedure. The most expensive step in the multi-scale algorithm is the power iteration required in the last stage, that is interpolating eigenvectors from the first coarse scale to the required fine scale. The complexity is of the order of n × e where e is the subspace dimensionality and n is the size of the graph. Indeed, from the table we can see that the multi-scale procedure is taking time roughly proportional to n. Deviations from the linear trend are observed at specific values of n, which we believe are due to the n 322 632 642 652 1002 1272 1282 1292 1602 2552 2562 2572 5112 5122 5132 6002 7002 8002 svds.m 1.6 10.8 20.5 12.6 44.2 91.1 230.9 96.9 179.3 819.2 2170.8 871.7 7977.2 20269 7887.2 10841.4 15048.8 Multi-Scale 1.5 4.9 5.5 5.1 13.1 20.4 35.2 20.9 34.4 90.3 188.7 93.3 458.8 739.3 461.9 644.2 1162.4 1936.6 Speedup 1.1 2.2 3.7 2.5 3.4 4.5 6.6 4.6 5.2 9.1 11.5 9.3 17.4 27.4 17.1 16.8 12.9 variations in the difficulty of the specific eigenvalue problem (eg. nearly multiple eigenvalues). The hierarchical representation has proven to be effective in a min-cut based segmentation algorithm that we proposed recently [8]. Here we explored the use of random walks and associated spectral embedding techniques for the automatic generation of suitable proposal (source and sink) regions for a min-cut based algorithm. The multiscale algorithm was used to generate the 40 leading eigenvectors of large transition matrices (eg. size 20K × 20K). In terms of future work, it will be useful to compare our work with other approximate methods for SVD such as [23]. Ack: We thank S. Roweis, F. Estrada and M. Sakr for valuable comments. References [1] D. Achlioptas and F. McSherry. Fast Computation of Low-Rank Approximations. STOC, 2001. [2] D. Achlioptas et al Sampling Techniques for Kernel Methods. NIPS, 2001. [3] S. Barnard and H. Simon Fast Multilevel Implementation of Recursive Spectral Bisection for Partitioning Unstructured Problems. PPSC, 627-632. [4] M. Belkin et al Laplacian Eigenmaps and Spectral Techniques for Embedding. NIPS, 2001. [5] M. Brand et al A unifying theorem for spectral embedding and clustering. AI & STATS, 2002. [6] C. Chennubhotla. Spectral Methods for Multi-scale Feature Extraction and Spectral Clustering. http://www.cs.toronto.edu/˜chakra/thesis.pdf Ph.D Thesis, Department of Computer Science, University of Toronto, Canada, 2004. [7] C. Chennubhotla and A. Jepson. Half-Lives of EigenFlows for Spectral Clustering. NIPS, 2002. [8] F. Estrada, A. Jepson and C. Chennubhotla. Spectral Embedding and Min-Cut for Image Segmentation. Manuscript Under Review, 2004. [9] C. Fowlkes et al Efficient spatiotemporal grouping using the Nystrom method. CVPR, 2001. [10] S. Belongie et al Spectral Partitioning with Indefinite Kernels using Nystrom app. ECCV, 2002. [11] A. Frieze et al Fast Monte-Carlo Algorithms for finding low-rank approximations. FOCS, 1998. [12] Y. Koren et al ACE: A Fast Multiscale Eigenvectors Computation for Drawing Huge Graphs IEEE Symp. on InfoVis 2002, pp. 137-144 [13] R. B. Lehoucq, D. C. Sorensen and C. Yang. ARPACK User Guide: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods. SIAM 1998. [14] J. J. Lin. Reduced Rank Approximations of Transition Matrices. AI & STATS, 2002. [15] L. Lova’sz. Random Walks on Graphs: A Survey Combinatorics, 1996, 353–398. [16] G. J. McLachlan et al Mixture Models: Inference and Applications to Clustering. 1988 [17] M. Meila and J. Shi. A random walks view of spectral segmentation. AI & STATS, 2001. [18] A. Ng, M. Jordan and Y. Weiss. On Spectral Clustering: analysis and an algorithm NIPS, 2001. [19] A. Pothen Graph partitioning algorithms with applications to scientific computing. Parallel Numerical Algorithms, D. E. Keyes et al (eds.), Kluwer Academic Press, 1996. [20] G. L. Scott et al Feature grouping by relocalization of eigenvectors of the proximity matrix. BMVC, pg. 103-108, 1990. [21] E. Sharon et al Fast Multiscale Image Segmentation CVPR, I:70-77, 2000. [22] J. Shi and J. Malik. Normalized cuts and image segmentation. 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The hierarchical representation has proven to be effective in a min-cut based segmentation algorithm that we proposed recently [8]. 1 Spectral Methods Graph-theoretic spectral methods have gained popularity in a variety of application domains: segmenting images [22]; embedding in low-dimensional spaces [4, 5, 8]; and clustering parallel scientific computation tasks [19]. Spectral methods enable the study of properties global to a dataset, using only local (pairwise) similarity or affinity measurements between the data points. The global properties that emerge are best understood in terms of a random walk formulation on the graph. For example, the graph can be partitioned into clusters by analyzing the perturbations to the stationary distribution of a Markovian relaxation process defined in terms of the affinity weights [17, 18, 24, 7]. The Markovian relaxation process need never be explicitly carried out; instead, it can be analytically expressed using the leading order eigenvectors, and eigenvalues, of the Markov transition matrix. In this paper we consider the practical application of spectral methods to large datasets. In particular, the eigen decomposition can be very expensive, on the order of O(n 3 ), where n is the number of nodes in the graph. While it is possible to compute analytically the first eigenvector (see §3 below), the remaining subspace of vectors (necessary for say clustering) has to be explicitly computed. A typical approach to dealing with this difficulty is to first sparsify the links in the graph [22] and then apply an efficient eigensolver [13, 23, 3]. In comparison, we propose in this paper a specialized eigensolver suitable for large stochastic matrices with known stationary distributions. In particular, we exploit the spectral properties of the Markov transition matrix to generate hierarchical, successively lower-ranked approximations to the full transition matrix. The eigen problem is solved directly at the coarsest level of representation. The approximate eigen solution is then interpolated over successive levels of the hierarchy, using a small number of power iterations to correct the solution at each stage. 2 Previous Work One approach to speeding up the eigen decomposition is to use the fact that the columns of the affinity matrix are typically correlated. The idea then is to pick a small number of representative columns to perform eigen decomposition via SVD. For example, in the Nystrom approximation procedure, originally proposed for integral eigenvalue problems, the idea is to randomly pick a small set of m columns; generate the corresponding affinity matrix; solve the eigenproblem and finally extend the solution to the complete graph [9, 10]. The Nystrom method has also been recently applied in the kernel learning methods for fast Gaussian process classification and regression [25]. Other sampling-based approaches include the work reported in [1, 2, 11]. Our starting point is the transition matrix generated from affinity weights and we show how building a representational hierarchy follows naturally from considering the stochastic matrix. A closely related work is the paper by Lin on reduced rank approximations of transition matrices [14]. We differ in how we approximate the transition matrices, in particular our objective function is computationally less expensive to solve. In particular, one of our goals in reducing transition matrices is to develop a fast, specialized eigen solver for spectral clustering. Fast eigensolving is also the goal in ACE [12], where successive levels in the hierarchy can potentially have negative affinities. A graph coarsening process for clustering was also pursued in [21, 3]. 3 Markov Chain Terminology We first provide a brief overview of the Markov chain terminology here (for more details see [17, 15, 6]). We consider an undirected graph G = (V, E) with vertices vi , for i = {1, . . . , n}, and edges ei,j with non-negative weights ai,j . Here the weight ai,j represents the affinity between vertices vi and vj . The affinities are represented by a non-negative, symmetric n × n matrix A having weights ai,j as elements. The degree of a node j is n n defined to be: dj = i=1 ai,j = j=1 aj,i , where we define D = diag(d1 , . . . , dn ). A Markov chain is defined using these affinities by setting a transition probability matrix M = AD −1 , where the columns of M each sum to 1. The transition probability matrix defines the random walk of a particle on the graph G. The random walk need never be explicitly carried out; instead, it can be analytically expressed using the leading order eigenvectors, and eigenvalues, of the Markov transition matrix. Because the stochastic matrices need not be symmetric in general, a direct eigen decomposition step is not preferred for reasons of instability. This problem is easily circumvented by considering a normalized affinity matrix: L = D −1/2 AD−1/2 , which is related to the stochastic matrix by a similarity transformation: L = D −1/2 M D1/2 . Because L is symmetric, it can be diagonalized: L = U ΛU T , where U = [u1 , u2 , · · · , un ] is an orthogonal set of eigenvectors and Λ is a diagonal matrix of eigenvalues [λ1 , λ2 , · · · , λn ] sorted in decreasing order. The eigenvectors have unit length uk = 1 and from the form of A and D it can be shown that the eigenvalues λi ∈ (−1, 1], with at least one eigenvalue equal to one. Without loss of generality, we take λ1 = 1. Because L and M are similar we can perform an eigen decomposition of the Markov transition matrix as: M = D1/2 LD−1/2 = D1/2 U Λ U T D−1/2 . Thus an eigenvector u of L corresponds to an eigenvector D 1/2 u of M with the same eigenvalue λ. The Markovian relaxation process after β iterations, namely M β , can be represented as: M β = D1/2 U Λβ U T D−1/2 . Therefore, a particle undertaking a random walk with an initial distribution p 0 acquires after β steps a distribution p β given by: p β = M β p 0 . Assuming the graph is connected, as β → ∞, the Markov chain approaches a unique n stationary distribution given by π = diag(D)/ i=1 di , and thus, M ∞ = π1T , where 1 is a n-dim column vector of all ones. Observe that π is an eigenvector of M as it is easy to show that M π = π and the corresponding eigenvalue is 1. Next, we show how to generate hierarchical, successively low-ranked approximations for the transition matrix M . 4 Building a Hierarchy of Transition Matrices The goal is to generate a very fast approximation, while simultaneously achieving sufficient accuracy. For notational ease, we think of M as a fine-scale representation and M as some coarse-scale approximation to be derived here. By coarsening M further, we can generate successive levels of the representation hierarchy. We use the stationary distribution π to construct a corresponding coarse-scale stationary distribution δ. As we just discussed a critical property of the fine scale Markov matrix M is that it is similar to the symmetric matrix L and we wish to preserve this property at every level of the representation hierarchy. 4.1 Deriving Coarse-Scale Stationary Distribution We begin by expressing the stationary distribution π as a probabilistic mixture of latent distributions. In matrix notation, we have (1) π = K δ, where δ is an unknown mixture coefficient vector of length m, K is an n × m non-negative n kernel matrix whose columns are latent distributions that each sum to 1: i=1 Ki,j = 1 and m n. It is easy to derive a maximum likelihood approximation of δ using an EM type algorithm [16]. The main step is to find a stationary point δ, λ for the Lagrangian: m n i=1 m Ki,j δj + λ πi ln E≡− j=1 δj − 1 . (2) j=1 An implicit step in this EM procedure is to compute the the ownership probability r i,j of the j th kernel (or node) at the coarse scale for the ith node on the fine scale and is given by ri,j = δj Ki,j . m k=1 δk Ki,k (3) The EM procedure allows for an update of both δ and the latent distributions in the kernel matrix K (see §8.3.1 in [6]). For initialization, δ is taken to be uniform over the coarse-scale states. But in choosing kernels K, we provide a good initialization for the EM procedure. Specifically, the Markov matrix M is diffused using a small number of iterations to get M β . The diffusion causes random walks from neighboring nodes to be less distinguishable. This in turn helps us select a small number of columns of M β in a fast and greedy way to be the kernel matrix K. We defer the exact details on kernel selection to a later section (§4.3). 4.2 Deriving the Coarse-Scale Transition Matrix In order to define M , the coarse-scale transition matrix, we break it down into three steps. First, the Markov chain propagation at the coarse scale can be defined as: q k+1 = M q k , (4) where q is the coarse scale probability distribution after k steps of the random walk. Second, we expand q k into the fine scale using the kernels K resulting in a fine scale probability distribution p k : p k = Kq k . (5) k Finally, we lift p k back into the coarse scale by using the ownership probability of the j th kernel for the ith node on the fine grid: n qjk+1 = ri,j pik i=1 (6) Substituting for Eqs.(3) and (5) in Eq. 6 gives n m qjk+1 = i=1 n Ki,t qtk = ri,j t=1 i=1 δj Ki,j m k=1 δk Ki,k m Ki,t qtk . (7) t=1 We can write the preceding equation in a matrix form: q k+1 = diag( δ ) K T diag K δ −1 Kq k . (8) Comparing this with Eq. 4, we can derive the transition matrix M as: M = diag( δ ) K T diag K δ −1 (9) K. It is easy to see that δ = M δ, so δ is the stationary distribution for M . Following the definition of M , and its stationary distribution δ, we can generate a symmetric coarse scale affinity matrix A given by A = M diag(δ) = diag( δ ) K T diag K δ −1 Kdiag(δ) , (10) where we substitute for the expression M from Eq. 9. The coarse-scale affinity matrix A is then normalized to get: L = D−1/2 AD−1/2 ; D = diag(d1 , d2 , · · · , dm ), (11) where dj is the degree of node j in the coarse-scale graph represented by the matrix A (see §3 for degree definition). Thus, the coarse scale Markov matrix M is precisely similar to a symmetric matrix L. 4.3 Selecting Kernels For demonstration purpose, we present the kernel selection details on the image of an eye shown below. To begin with, a random walk is defined where each pixel in the test image is associated with a vertex of the graph G. The edges in G are defined by the standard 8-neighbourhood of each pixel. For the demonstrations in this paper, the edge weight ai,j between neighbouring pixels xi and xj is given by a function of the difference in the 2 corresponding intensities I(xi ) and I(xj ): ai,j = exp(−(I(xi ) − I(xj ))2 /2σa ), where σa is set according to the median absolute difference |I(xi ) − I(xj )| between neighbours measured over the entire image. The affinity matrix A with the edge weights is then used to generate a Markov transition matrix M . The kernel selection process we use is fast and greedy. First, the fine scale Markov matrix M is diffused to M β using β = 4. The Markov matrix M is sparse as we make the affinity matrix A sparse. Every column in the diffused matrix M β is a potential kernel. To facilitate the selection process, the second step is to rank order the columns of M β based on a probability value in the stationary distribution π. Third, the kernels (i.e. columns of M β ) are picked in such a way that for a kernel Ki all of the neighbours of pixel i which are within the half-height of the the maximum value in the kernel Ki are suppressed from the selection process. Finally, the kernel selection is continued until every pixel in the image is within a half-height of the peak value of at least one kernel. If M is a full matrix, to avoid the expense of computing M β explicitly, random kernel centers can be selected, and only the corresponding columns of M β need be computed. We show results from a three-scale hierarchy on the eye image (below). The image has 25 × 20 pixels but is shown here enlarged for clarity. At the first coarse scale 83 kernels are picked. The kernels each correspond to a different column in the fine scale transition matrix and the pixels giving rise to these kernels are shown numbered on the image. Using these kernels as an initialization, the EM procedure derives a coarse-scale stationary distribution δ 21 14 26 4 (Eq. 2), while simultaneously updating the kernel ma12 27 2 19 trix. Using the newly updated kernel matrix K and the 5 8 13 23 30 18 6 9 derived stationary distribution δ a transition matrix M 28 20 15 32 10 22 is generated (Eq. 9). The coarse scale Markov matrix 24 17 7 is then diffused to M β , again using β = 4. The kernel Coarse Scale 1 Coarse Scale 2 selection algorithm is reapplied, this time picking 32 kernels for the second coarse scale. Larger values of β cause the coarser level to have fewer elements. But the exact number of elements depends on the form of the kernels themselves. For the random experiments that we describe later in §6 we found β = 2 in the first iteration and 4 thereafter causes the number of kernels to be reduced by a factor of roughly 1/3 to 1/4 at each level. 72 28 35 44 51 64 82 4 12 31 56 19 77 36 45 52 65 13 57 23 37 5 40 53 63 73 14 29 6 66 38 74 47 24 7 30 41 54 71 78 58 15 8 20 39 48 59 67 25 68 79 21 16 2 11 26 42 49 55 60 75 32 83 43 9 76 50 17 27 61 33 69 80 3 46 18 70 81 34 10 62 22 1 25 11 1 3 16 31 29 At coarser levels of the hierarchy, we expect the kernels to get less sparse and so will the affinity and the transition matrices. In order to promote sparsity at successive levels of the hierarchy we sparsify A by zeroing out elements associated with “small” transition probabilities in M . However, in the experiments described later in §6, we observe this sparsification step to be not critical. To summarize, we use the stationary distribution π at the fine-scale to derive a transition matrix M , and its stationary distribution δ, at the coarse-scale. The coarse scale transition in turn helps to derive an affinity matrix A and its normalized version L. It is obvious that this procedure can be repeated recursively. We describe next how to use this representation hierarchy for building a fast eigensolver. 5 Fast EigenSolver Our goal in generating a hierarchical representation of a transition matrix is to develop a fast, specialized eigen solver for spectral clustering. To this end, we perform a full eigen decomposition of the normalized affinity matrix only at the coarsest level. As discussed in the previous section, the affinity matrix at the coarsest level is not likely to be sparse, hence it will need a full (as opposed to a sparse) version of an eigen solver. However it is typically the case that e ≤ m n (even in the case of the three-scale hierarchy that we just considered) and hence we expect this step to be the least expensive computationally. The resulting eigenvectors are interpolated to the next lower level of the hierarchy by a process which will be described next. Because the eigen interpolation process between every adjacent pair of scales in the hierarchy is similar, we will assume we have access to the leading eigenvectors U (size: m × e) for the normalized affinity matrix L (size: m × m) and describe how to generate the leading eigenvectors U (size: n × e), and the leading eigenvalues S (size: e × 1), for the fine-scale normalized affinity matrix L (size: n × n). There are several steps to the eigen interpolation process and in the discussion that follows we refer to the lines in the pseudo-code presented below. First, the coarse-scale eigenvectors U can be interpolated using the kernel matrix K to generate U = K U , an approximation for the fine-scale eigenvectors (line 9). Second, interpolation alone is unlikely to set the directions of U exactly aligned with U L , the vectors one would obtain by a direct eigen decomposition of the fine-scale normalized affinity matrix L. We therefore update the directions in U by applying a small number of power iterations with L, as given in lines 13-15. e e function (U, S) = CoarseToFine(L, K, U , S) 1: INPUT 2: L, K ⇐ {L is n × n and K is n × m where m n} e e e e 3: U /S ⇐ {leading coarse-scale eigenvectors/eigenvalues of L. U is of size m × e, e ≤ m} 4: OUTPUT 5: U, S ⇐ {leading fine-scale eigenvectors/eigenvalues of L. U is n × e and S is e × 1.} x 10 0.4 3 0.96 0.94 0.92 0.9 0.35 2.5 Relative Error Absolute Relative Error 0.98 Eigen Value |δλ|λ−1 −3 Eigen Spectrum 1 2 1.5 1 5 10 15 20 Eigen Index (a) 25 30 0.2 0.15 0.1 0.5 0.88 0.3 0.25 0.05 5 10 15 20 Eigen Index (b) 25 30 5 10 15 20 Eigen Index 25 30 (c) Figure 1: Hierarchical eigensolver results. (a) comparing ground truth eigenvalues S L (red circles) with multi-scale eigensolver spectrum S (blue line) (b) Relative absolute error between eigenvalues: |S−SL | (c) Eigenvector mismatch: 1 − diag |U T UL | , between SL eigenvectors U derived by the multi-scale eigensolver and the ground truth U L . Observe the slight mismatch in the last few eigenvectors, but excellent agreement in the leading eigenvectors (see text). 6: CONSTANTS: TOL = 1e-4; POWER ITERS = 50 7: “ ” e 8: TPI = min POWER ITERS, log(e × eps/TOL)/ log(min(S)) {eps: machine accuracy} e 9: U = K U {interpolation from coarse to fine} 10: while not converged do 11: Uold = U {n × e matrix, e n} 12: for i = 1 to TPI do 13: U ⇐ LU 14: end for 15: U ⇐ Gram-Schmidt(U ) {orthogonalize U } 16: Le = U T LU {L may be sparse, but Le need not be.} 17: Ue Se UeT = svd(Le ) {eigenanalysis of Le , which is of size e × e.} 18: U ⇐ U Ue {update the leading eigenvectors of L} 19: S = diag(Se ) {grab the leading eigenvalues of L} T 20: innerProd = 1 − diag( Uold U ) {1 is a e × 1 vector of all ones} 21: converged = max[abs(innerProd)] < TOL 22: end while The number of power iterations TPI can be bounded as discussed next. Suppose v = U c where U is a matrix of true eigenvectors and c is a coefficient vector for an arbitrary vector v. After TPI power iterations v becomes v = U diag(S TPI )c, where S has the exact eigenvalues. In order for the component of a vector v in the direction Ue (the eth column of U ) not to be swamped by other components, we can limit it’s decay after TPI iterations as TPI follows: (S(e)/S(1)) >= e×eps/TOL, where S(e) is the exact eth eigenvalue, S(1) = 1, eps is the machine precision, TOL is requested accuracy. Because we do not have access to the exact value S(e) at the beginning of the interpolation procedure, we estimate it from the coarse eigenvalues S. This leads to a bound on the power iterations TPI, as derived on the line 9 above. Third, the interpolation process and the power iterations need not preserve orthogonality in the eigenvectors in U . We fix this by Gram-Schmidt orthogonalization procedure (line 16). Finally, there is a still a problem with power iterations that needs to be resolved, in that it is very hard to separate nearby eigenvalues. In particular, for the convergence of the power iterations the ratio that matters is between the (e + 1) st and eth eigenvalues. So the idea we pursue is to use the power iterations only to separate the reduced space of eigenvectors (of dimension e) from the orthogonal subspace (of dimension n − e). We then use a full SVD on the reduced space to update the leading eigenvectors U , and eigenvalues S, for the fine-scale (lines 17-20). This idea is similar to computing the Ritz values and Ritz vectors in a Rayleigh-Ritz method. 6 Interpolation Results Our multi-scale decomposition code is in Matlab. For the direct eigen decomposition, we have used the Matlab program svds.m which invokes the compiled ARPACKC routine [13], with a default convergence tolerance of 1e-10. In Fig. 1a we compare the spectrum S obtained from a three-scale decomposition on the eye image (blue line) with the ground truth, which is the spectrum SL resulting from direct eigen decomposition of the fine-scale normalized affinity matrices L (red circles). There is an excellent agreement in the leading eigenvalues. To illustrate this, we show absolute relative error between the spectra: |S−SL | in Fig. 1b. The spectra agree mostly, except for SL the last few eigenvalues. For a quantitative comparison between the eigenvectors, we plot in Fig. 1c the following measure: 1 − diag(|U T UL |), where U is the matrix of eigenvectors obtained by the multi-scale approximation, UL is the ground-truth resulting from a direct eigen decomposition of the fine-scale affinity matrix L and 1 is a vector of all ones. The relative error plot demonstrates a close match, within the tolerance threshold of 1e-4 that we chose for the multi-scale method, in the leading eigenvector directions between the two methods. The relative error is high with the last few eigen vectors, which suggests that the power iterations have not clearly separated them from other directions. So, the strategy we suggest is to pad the required number of leading eigen basis by about 20% before invoking the multi-scale procedure. Obviously, the number of hierarchical stages for the multi-scale procedure must be chosen such that the transition matrix at the coarsest scale can accommodate the slight increase in the subspace dimensions. For lack of space we are omitting extra results (see Ch.8 in [6]). Next we measure the time the hierarchical eigensolver takes to compute the leading eigenbasis for various input sizes, in comparison with the svds.m procedure [13]. We form images of different input sizes by Gaussian smoothing of i.i.d noise. The Gaussian function has a standard deviation of 3 pixels. The edges in graph G are defined by the standard 8-neighbourhood of each pixel. The edge weights between neighbouring pixels are simply given by a function of the difference in the corresponding intensities (see §4.3). The affinity matrix A with the edge weights is then used to generate a Markov transition matrix M . The fast eigensolver is run on ten different instances of the input image of a given size and the average of these times is reported here. For a fair comparison between the two procedures, we set the convergence tolerance value for the svds.m procedure to be 1e-4, the same as the one used for the fast eigensolver. We found the hierarchical representation derived from this tolerance threshold to be sufficiently accurate for a novel min-cut based segmentation results that we reported in [8]. Also, the subspace dimensionality is fixed to be 51 where we expect (and indeed observe) the leading 40 eigenpairs derived from the multi-scale procedure to be accurate. Hence, while invoking svds.m we compute only the leading 41 eigenpairs. In the table shown below, the first column corresponds to the number of nodes in the graph, while the second and third columns report the time taken in seconds by the svds.m procedure and the Matlab implementation of the multi-scale eigensolver respectively. The fourth column reports the speedups of the multi-scale eigensolver over svds.m procedure on a standard desktop (Intel P4, 2.5GHz, 1GB RAM). Lowering the tolerance threshold for svds.m made it faster by about 20 − 30%. Despite this, the multi-scale algorithm clearly outperforms the svds.m procedure. The most expensive step in the multi-scale algorithm is the power iteration required in the last stage, that is interpolating eigenvectors from the first coarse scale to the required fine scale. The complexity is of the order of n × e where e is the subspace dimensionality and n is the size of the graph. Indeed, from the table we can see that the multi-scale procedure is taking time roughly proportional to n. Deviations from the linear trend are observed at specific values of n, which we believe are due to the n 322 632 642 652 1002 1272 1282 1292 1602 2552 2562 2572 5112 5122 5132 6002 7002 8002 svds.m 1.6 10.8 20.5 12.6 44.2 91.1 230.9 96.9 179.3 819.2 2170.8 871.7 7977.2 20269 7887.2 10841.4 15048.8 Multi-Scale 1.5 4.9 5.5 5.1 13.1 20.4 35.2 20.9 34.4 90.3 188.7 93.3 458.8 739.3 461.9 644.2 1162.4 1936.6 Speedup 1.1 2.2 3.7 2.5 3.4 4.5 6.6 4.6 5.2 9.1 11.5 9.3 17.4 27.4 17.1 16.8 12.9 variations in the difficulty of the specific eigenvalue problem (eg. nearly multiple eigenvalues). The hierarchical representation has proven to be effective in a min-cut based segmentation algorithm that we proposed recently [8]. Here we explored the use of random walks and associated spectral embedding techniques for the automatic generation of suitable proposal (source and sink) regions for a min-cut based algorithm. The multiscale algorithm was used to generate the 40 leading eigenvectors of large transition matrices (eg. size 20K × 20K). In terms of future work, it will be useful to compare our work with other approximate methods for SVD such as [23]. Ack: We thank S. Roweis, F. Estrada and M. Sakr for valuable comments. References [1] D. Achlioptas and F. McSherry. Fast Computation of Low-Rank Approximations. STOC, 2001. [2] D. Achlioptas et al Sampling Techniques for Kernel Methods. NIPS, 2001. [3] S. Barnard and H. Simon Fast Multilevel Implementation of Recursive Spectral Bisection for Partitioning Unstructured Problems. PPSC, 627-632. [4] M. Belkin et al Laplacian Eigenmaps and Spectral Techniques for Embedding. NIPS, 2001. [5] M. Brand et al A unifying theorem for spectral embedding and clustering. AI & STATS, 2002. [6] C. Chennubhotla. Spectral Methods for Multi-scale Feature Extraction and Spectral Clustering. http://www.cs.toronto.edu/˜chakra/thesis.pdf Ph.D Thesis, Department of Computer Science, University of Toronto, Canada, 2004. [7] C. Chennubhotla and A. Jepson. Half-Lives of EigenFlows for Spectral Clustering. NIPS, 2002. [8] F. Estrada, A. Jepson and C. Chennubhotla. Spectral Embedding and Min-Cut for Image Segmentation. Manuscript Under Review, 2004. [9] C. Fowlkes et al Efficient spatiotemporal grouping using the Nystrom method. CVPR, 2001. [10] S. Belongie et al Spectral Partitioning with Indefinite Kernels using Nystrom app. ECCV, 2002. [11] A. Frieze et al Fast Monte-Carlo Algorithms for finding low-rank approximations. FOCS, 1998. [12] Y. Koren et al ACE: A Fast Multiscale Eigenvectors Computation for Drawing Huge Graphs IEEE Symp. on InfoVis 2002, pp. 137-144 [13] R. B. Lehoucq, D. C. Sorensen and C. Yang. ARPACK User Guide: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods. SIAM 1998. [14] J. J. Lin. Reduced Rank Approximations of Transition Matrices. AI & STATS, 2002. [15] L. Lova’sz. Random Walks on Graphs: A Survey Combinatorics, 1996, 353–398. [16] G. J. McLachlan et al Mixture Models: Inference and Applications to Clustering. 1988 [17] M. Meila and J. Shi. A random walks view of spectral segmentation. AI & STATS, 2001. [18] A. Ng, M. Jordan and Y. Weiss. On Spectral Clustering: analysis and an algorithm NIPS, 2001. [19] A. Pothen Graph partitioning algorithms with applications to scientific computing. Parallel Numerical Algorithms, D. E. Keyes et al (eds.), Kluwer Academic Press, 1996. [20] G. L. Scott et al Feature grouping by relocalization of eigenvectors of the proximity matrix. BMVC, pg. 103-108, 1990. [21] E. Sharon et al Fast Multiscale Image Segmentation CVPR, I:70-77, 2000. [22] J. Shi and J. Malik. Normalized cuts and image segmentation. 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<p>Author: Aharon Bar-hillel, Adam Spiro, Eran Stark</p><p>Abstract: Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters. 1</p><p>4 0.87231034 <a title="161-lda-4" href="./nips-2004-The_Power_of_Selective_Memory%3A_Self-Bounded_Learning_of_Prediction_Suffix_Trees.html">189 nips-2004-The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees</a></p>
<p>Author: Ofer Dekel, Shai Shalev-shwartz, Yoram Singer</p><p>Abstract: Prediction suffix trees (PST) provide a popular and effective tool for tasks such as compression, classification, and language modeling. In this paper we take a decision theoretic view of PSTs for the task of sequence prediction. Generalizing the notion of margin to PSTs, we present an online PST learning algorithm and derive a loss bound for it. The depth of the PST generated by this algorithm scales linearly with the length of the input. We then describe a self-bounded enhancement of our learning algorithm which automatically grows a bounded-depth PST. We also prove an analogous mistake-bound for the self-bounded algorithm. The result is an efficient algorithm that neither relies on a-priori assumptions on the shape or maximal depth of the target PST nor does it require any parameters. To our knowledge, this is the first provably-correct PST learning algorithm which generates a bounded-depth PST while being competitive with any fixed PST determined in hindsight. 1</p><p>5 0.87219959 <a title="161-lda-5" href="./nips-2004-Adaptive_Discriminative_Generative_Model_and_Its_Applications.html">16 nips-2004-Adaptive Discriminative Generative Model and Its Applications</a></p>
<p>Author: Ruei-sung Lin, David A. Ross, Jongwoo Lim, Ming-Hsuan Yang</p><p>Abstract: This paper presents an adaptive discriminative generative model that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminative model as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.</p><p>6 0.87184381 <a title="161-lda-6" href="./nips-2004-Nonparametric_Transforms_of_Graph_Kernels_for_Semi-Supervised_Learning.html">133 nips-2004-Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning</a></p>
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<p>8 0.87118679 <a title="161-lda-8" href="./nips-2004-Support_Vector_Classification_with_Input_Data_Uncertainty.html">178 nips-2004-Support Vector Classification with Input Data Uncertainty</a></p>
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<p>10 0.87082565 <a title="161-lda-10" href="./nips-2004-Blind_One-microphone_Speech_Separation%3A_A_Spectral_Learning_Approach.html">31 nips-2004-Blind One-microphone Speech Separation: A Spectral Learning Approach</a></p>
<p>11 0.86954325 <a title="161-lda-11" href="./nips-2004-A_Generalized_Bradley-Terry_Model%3A_From_Group_Competition_to_Individual_Skill.html">4 nips-2004-A Generalized Bradley-Terry Model: From Group Competition to Individual Skill</a></p>
<p>12 0.86855799 <a title="161-lda-12" href="./nips-2004-Worst-Case_Analysis_of_Selective_Sampling_for_Linear-Threshold_Algorithms.html">206 nips-2004-Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms</a></p>
<p>13 0.86837023 <a title="161-lda-13" href="./nips-2004-Non-Local_Manifold_Tangent_Learning.html">131 nips-2004-Non-Local Manifold Tangent Learning</a></p>
<p>14 0.86788476 <a title="161-lda-14" href="./nips-2004-Limits_of_Spectral_Clustering.html">103 nips-2004-Limits of Spectral Clustering</a></p>
<p>15 0.86783689 <a title="161-lda-15" href="./nips-2004-Confidence_Intervals_for_the_Area_Under_the_ROC_Curve.html">45 nips-2004-Confidence Intervals for the Area Under the ROC Curve</a></p>
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<br/><br/><br/>
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| 503.917476 | 28,527 | 0.772241 |
717fbb9ca2b406375dfdb2dad766b6a864ed082a | 2,317 | asm | Assembly | boot/asmhead.asm | TTwotree/AntzOS | 1e420858ba398efe866f25b1f8f6aa5088a0d929 | [
"MIT"
] | 460 | 2018-08-02T10:02:34.000Z | 2022-03-04T09:41:20.000Z | boot/asmhead.asm | TTwotree/AntzOS | 1e420858ba398efe866f25b1f8f6aa5088a0d929 | [
"MIT"
] | 3 | 2018-12-09T07:09:58.000Z | 2019-09-19T06:39:37.000Z | boot/asmhead.asm | TTwotree/AntzOS | 1e420858ba398efe866f25b1f8f6aa5088a0d929 | [
"MIT"
] | 53 | 2018-10-21T14:10:03.000Z | 2022-03-13T08:54:38.000Z | [INSTRSET "i486p"]
VBEMODE EQU 0x105
BOTPAK EQU 0x00280000
DSKCAC EQU 0x00100000
DSKCAC0 EQU 0x00008000
; BOOT_INFO 相关
CYLS EQU 0x0ff0
LEDS EQU 0x0ff1
VMODE EQU 0x0ff2
SCRNX EQU 0x0ff4
SCRNY EQU 0x0ff6
VRAM EQU 0x0ff8
ORG 0xc200
MOV AX,0x9000
MOV ES,AX
MOV DI,0
MOV AX,0x4f00
INT 0x10
CMP AX,0x004f
JNE scrn320
MOV AX,[ES:DI+4]
CMP AX,0x0200
JB scrn320
MOV CX,VBEMODE
MOV AX,0x4f01
INT 0x10
CMP AX,0x004f
JNE scrn320
CMP BYTE [ES:DI+0x19],8 ;颜色数必须为8
JNE scrn320
CMP BYTE [ES:DI+0x1b],4
JNE scrn320
MOV AX,[ES:DI+0x00]
AND AX,0x0080
JZ scrn320
MOV BX,VBEMODE+0x4000
MOV AX,0x4f02
INT 0x10
MOV BYTE [VMODE],8 ;模式
MOV AX,[ES:DI+0x12]
MOV [SCRNX],AX
MOV AX,[ES:DI+0x14]
MOV [SCRNY],AX
MOV EAX,[ES:DI+0x28]
MOV [VRAM],EAX
JMP keystatus
scrn320:
MOV AL,0x13
MOV AH,0x00
INT 0x10
MOV BYTE [VMODE],8
MOV WORD [SCRNX],320
MOV WORD [SCRNY],200
MOV DWORD [VRAM],0x000a0000
keystatus:
MOV AH,0x02
INT 0x16
MOV [LEDS],AL
MOV AL,0xff
OUT 0x21,AL
NOP
OUT 0xa1,AL
CLI
CALL waitkbdout
MOV AL,0xd1
OUT 0x64,AL
CALL waitkbdout
MOV AL,0xdf
OUT 0x60,AL
CALL waitkbdout
[INSTRSET "i486p"]
LGDT [GDTR0] ; GDT
MOV EAX,CR0
AND EAX,0x7fffffff
OR EAX,0x00000001 ;
MOV CR0,EAX
JMP pipelineflush
pipelineflush:
MOV AX,1*8
MOV DS,AX
MOV ES,AX
MOV FS,AX
MOV GS,AX
MOV SS,AX
MOV ESI,bootpack
MOV EDI,BOTPAK
MOV ECX,512*1024/4
CALL memcpy
MOV ESI,0x7c00
MOV EDI,DSKCAC
MOV ECX,512/4
CALL memcpy
MOV ESI,DSKCAC0+512
MOV EDI,DSKCAC+512
MOV ECX,0
MOV CL,BYTE [CYLS]
IMUL ECX,512*18*2/4
SUB ECX,512/4
CALL memcpy
MOV EBX,BOTPAK
MOV ECX,[EBX+16]
ADD ECX,3
SHR ECX,2
JZ skip
MOV ESI,[EBX+20] ; 转送源
ADD ESI,EBX
MOV EDI,[EBX+12] ; 转送目标
CALL memcpy
skip:
MOV ESP,[EBX+12] ; 堆栈的初始化
JMP DWORD 2*8:0x0000001b
waitkbdout:
IN AL,0x64
AND AL,0x02
JNZ waitkbdout
RET
memcpy:
MOV EAX,[ESI]
ADD ESI,4
MOV [EDI],EAX
ADD EDI,4
SUB ECX,1
JNZ memcpy
RET
; memcpy地址前缀大小
ALIGNB 16
GDT0:
RESB 8
DW 0xffff,0x0000,0x9200,0x00cf
DW 0xffff,0x0000,0x9a28,0x0047
DW 0
GDTR0:
DW 8*3-1
DD GDT0
ALIGNB 16
bootpack:
| 13.874251 | 36 | 0.634441 |
ee2e46ca2dc2941a16e5db5282909a7a651c1571 | 80 | asm | Assembly | data/maps/headers/PewterNidoranHouse.asm | opiter09/ASM-Machina | 75d8e457b3e82cc7a99b8e70ada643ab02863ada | [
"CC0-1.0"
] | 1 | 2022-02-15T00:19:44.000Z | 2022-02-15T00:19:44.000Z | data/maps/headers/PewterNidoranHouse.asm | opiter09/ASM-Machina | 75d8e457b3e82cc7a99b8e70ada643ab02863ada | [
"CC0-1.0"
] | null | null | null | data/maps/headers/PewterNidoranHouse.asm | opiter09/ASM-Machina | 75d8e457b3e82cc7a99b8e70ada643ab02863ada | [
"CC0-1.0"
] | null | null | null |
map_header PewterNidoranHouse, PEWTER_NIDORAN_HOUSE, HOUSE, 0
end_map_header
| 20 | 62 | 0.85 |
b900e9db83c859e8e2e7d24bb608cefa759b7be7 | 459 | asm | Assembly | programs/oeis/146/A146083.asm | jmorken/loda | 99c09d2641e858b074f6344a352d13bc55601571 | [
"Apache-2.0"
] | 1 | 2021-03-15T11:38:20.000Z | 2021-03-15T11:38:20.000Z | programs/oeis/146/A146083.asm | jmorken/loda | 99c09d2641e858b074f6344a352d13bc55601571 | [
"Apache-2.0"
] | null | null | null | programs/oeis/146/A146083.asm | jmorken/loda | 99c09d2641e858b074f6344a352d13bc55601571 | [
"Apache-2.0"
] | null | null | null | ; A146083: Expansion of 1/(1 - x*(1 - 11*x)).
; 1,1,-10,-21,89,320,-659,-4179,3070,49039,15269,-524160,-692119,5073641,12686950,-43123101,-182679551,291674560,2301149621,-907270539,-26219916370,-16239940441,272179139629,450818484480,-2543152051439,-7502155380719,20472517185110,102996226373019,-122201462663191,-1255159952766400,89056136528701
mov $1,2
mov $2,2
lpb $0
sub $0,1
mul $1,11
sub $2,$1
add $1,$2
lpe
sub $1,2
div $1,22
mul $1,11
add $1,1
| 28.6875 | 297 | 0.729847 |
f3bee1ddaa385f1770c891eb37419cfe6a57bd7d | 132 | sql | SQL | packaging/dbscripts/upgrade/03_06_0940_drop_vm_dynamic_guest_last_logxxx_time_columns.sql | leongold/ovirt-engine | 8b915dab8ad8157849b36b60eb0ca159b1923faf | [
"Apache-2.0"
] | null | null | null | packaging/dbscripts/upgrade/03_06_0940_drop_vm_dynamic_guest_last_logxxx_time_columns.sql | leongold/ovirt-engine | 8b915dab8ad8157849b36b60eb0ca159b1923faf | [
"Apache-2.0"
] | null | null | null | packaging/dbscripts/upgrade/03_06_0940_drop_vm_dynamic_guest_last_logxxx_time_columns.sql | leongold/ovirt-engine | 8b915dab8ad8157849b36b60eb0ca159b1923faf | [
"Apache-2.0"
] | null | null | null | SELECT fn_db_drop_column('vm_dynamic', 'guest_last_login_time');
SELECT fn_db_drop_column('vm_dynamic', 'guest_last_logout_time');
| 33 | 65 | 0.825758 |
2314f8ee0fda40bc2ef803b56bd6f773498fc78f | 58 | rs | Rust | workspace_tests/src/rt_logic.rs | azriel91/choochoo | 61835c5c9e9e44d7ca99eba3a3a1c78957186cee | [
"Apache-2.0",
"MIT"
] | 1 | 2022-01-17T18:12:00.000Z | 2022-01-17T18:12:00.000Z | workspace_tests/src/rt_logic.rs | azriel91/choochoo | 61835c5c9e9e44d7ca99eba3a3a1c78957186cee | [
"Apache-2.0",
"MIT"
] | 30 | 2020-12-23T07:32:08.000Z | 2022-03-13T02:34:38.000Z | workspace_tests/src/rt_logic.rs | azriel91/choochoo | 61835c5c9e9e44d7ca99eba3a3a1c78957186cee | [
"Apache-2.0",
"MIT"
] | null | null | null | mod integrity_strat;
mod train;
mod visit_status_updater;
| 14.5 | 25 | 0.844828 |
f03934ae8a6f4c97b9e55d2ce3b5fe4a8d8e7f9c | 1,809 | js | JavaScript | controllers/trafficData.js | Ligengxin96/fetchRepooTrafficData | 8c38e6a5cc27beff1e48af9248ac21785a1300f2 | [
"MIT"
] | null | null | null | controllers/trafficData.js | Ligengxin96/fetchRepooTrafficData | 8c38e6a5cc27beff1e48af9248ac21785a1300f2 | [
"MIT"
] | null | null | null | controllers/trafficData.js | Ligengxin96/fetchRepooTrafficData | 8c38e6a5cc27beff1e48af9248ac21785a1300f2 | [
"MIT"
] | null | null | null | import moment from "moment";
export const getRecord = async (date, model) => {
if (!model) {
throw new Error(`Model can't be null`);
}
console.log(`Need be get ${model.modelName} record date: ${moment(date).format('yyyy-MM-DD')}`);
try {
const data = await model.findOne({ date });
if (data) {
console.log(`Get ${model.modelName} record successful, ${model.modelName} record info: ${JSON.stringify(data)}`);
}
return data;
} catch (error) {
const errorMessage = `Get ${model.modelName} record from mongoose failed with error: ${error.message}`;
console.error(errorMessage);
}
}
export const createReocrd = async (data, model) => {
if (!model) {
throw new Error(`Model can't be null`);
}
try {
console.log(`Need be created ${model.modelName} record date: ${moment(data.date).format('yyyy-MM-DD')}`);
const newData = new model(data);
await newData.save();
console.log(`Save ${model.modelName} record to mongoose successful, ${model.modelName} record: ${JSON.stringify(newData)}`);
} catch (error) {
const errorMessage = `Save ${model.modelName} record to mongoose failed with error: ${error.message}`;
console.error(errorMessage);
}
}
export const updatReocrd = async (date, data, model) => {
if (!model) {
throw new Error(`Model can't be null`);
}
console.log(`Need be updated ${model.modelName} record date: ${moment(date).format('yyyy-MM-DD')}`);
try {
const newData = await model.findOneAndUpdate({ date }, data, { new: true });
console.log(`Update ${model.modelName} record successful, ${model.modelName} record: ${JSON.stringify(newData)}`);
} catch (error) {
const errorMessage = `Update ${model.modelName} record failed with error: ${error.message}`;
console.error(errorMessage);
}
}
| 37.6875 | 128 | 0.658928 |
5b6fe44de96807db91f33645a98426aa909fdf98 | 67 | kt | Kotlin | intellij2checkstyle-core/src/test/kotlin/integration/extension/types/OutputFolder.kt | theodm/intellij2checkstyle | 2607e1f9e4f64e7b4e8140c56918ad5d856a62eb | [
"Apache-2.0"
] | null | null | null | intellij2checkstyle-core/src/test/kotlin/integration/extension/types/OutputFolder.kt | theodm/intellij2checkstyle | 2607e1f9e4f64e7b4e8140c56918ad5d856a62eb | [
"Apache-2.0"
] | null | null | null | intellij2checkstyle-core/src/test/kotlin/integration/extension/types/OutputFolder.kt | theodm/intellij2checkstyle | 2607e1f9e4f64e7b4e8140c56918ad5d856a62eb | [
"Apache-2.0"
] | null | null | null | package integration.extension.types
annotation class OutputFolder
| 16.75 | 35 | 0.880597 |
405fc0a64bd2a08ed23474494053caa8a0b6fbbd | 311 | py | Python | proxy.py | Raccoonwao/BookGrepper | 5223f773629f8c3aad9c8b86df3aeb805cc939a9 | [
"Unlicense"
] | null | null | null | proxy.py | Raccoonwao/BookGrepper | 5223f773629f8c3aad9c8b86df3aeb805cc939a9 | [
"Unlicense"
] | null | null | null | proxy.py | Raccoonwao/BookGrepper | 5223f773629f8c3aad9c8b86df3aeb805cc939a9 | [
"Unlicense"
] | null | null | null | class Proxy:
def __init__(self, host, port):
self.host=host
self.port=port
self.succeed=0
self.fail=0
def markSucceed(self):
self.succeed +=1
def markFail(self):
self.fail +=1
def __str__(self):
return f'http://{self.host}:{self.port}'
| 19.4375 | 48 | 0.55627 |
e8ed02dac89d480ead9705b1ad919290dfc731c8 | 934 | py | Python | fabfile/text.py | nprapps/austin | 45237e878260678bbeb57801e798b89e67ad4e0b | [
"MIT"
] | 7 | 2015-01-26T16:02:49.000Z | 2015-04-01T12:37:52.000Z | fabfile/text.py | nprapps/austin | 45237e878260678bbeb57801e798b89e67ad4e0b | [
"MIT"
] | 272 | 2015-01-26T16:37:22.000Z | 2016-04-04T17:08:55.000Z | fabfile/text.py | nprapps/austin | 45237e878260678bbeb57801e798b89e67ad4e0b | [
"MIT"
] | 4 | 2015-03-05T00:38:17.000Z | 2021-02-23T10:26:28.000Z | #!/usr/bin/env python
"""
Commands related to syncing copytext from Google Docs.
"""
from fabric.api import task
from termcolor import colored
import app_config
from etc.gdocs import GoogleDoc
@task(default=True)
def update():
"""
Downloads a Google Doc as an Excel file.
"""
if app_config.COPY_GOOGLE_DOC_URL == None:
print colored('You have set COPY_GOOGLE_DOC_URL to None. If you want to use a Google Sheet, set COPY_GOOGLE_DOC_URL to the URL of your sheet in app_config.py', 'blue')
return
else:
doc = {}
url = app_config.COPY_GOOGLE_DOC_URL
if 'key' in url:
bits = url.split('key=')
bits = bits[1].split('&')
doc['key'] = bits[0]
else:
bits = url.split('/d/')
bits = bits[1].split('/')
doc['key'] = bits[0]
g = GoogleDoc(**doc)
g.get_auth()
g.get_document()
| 24.578947 | 175 | 0.586724 |
dd967b2d2273673a2ddee8888af00a5ad0ecb06c | 462 | go | Go | pkg/xbits/types.go | the-xlang/xxc | a3abddc58e88dbb33955b5670a10f7473fef338d | [
"BSD-3-Clause"
] | 1 | 2022-03-23T19:31:58.000Z | 2022-03-23T19:31:58.000Z | pkg/xbits/types.go | the-xlang/xxc | a3abddc58e88dbb33955b5670a10f7473fef338d | [
"BSD-3-Clause"
] | null | null | null | pkg/xbits/types.go | the-xlang/xxc | a3abddc58e88dbb33955b5670a10f7473fef338d | [
"BSD-3-Clause"
] | 1 | 2022-03-26T21:24:20.000Z | 2022-03-26T21:24:20.000Z | package xbits
import "github.com/the-xlang/xxc/pkg/xtype"
// BitsizeType returns bit-size of
// data type of specified type code.
func BitsizeType(t uint8) int {
switch t {
case xtype.I8, xtype.U8:
return 0b1000
case xtype.I16, xtype.U16:
return 0b00010000
case xtype.I32, xtype.U32, xtype.F32:
return 0b00100000
case xtype.I64, xtype.U64, xtype.F64:
return 0b01000000
case xtype.UInt, xtype.Int:
return xtype.BitSize
default:
return 0
}
}
| 20.086957 | 43 | 0.731602 |
cd61252038cede38877b226d9566c3bbe138f4f3 | 28,876 | sql | SQL | SQL/controleGames_APS_BD_SQL.sql | dsambugaro/APS_BD_1_UTFPR | b7755a67cb71dabbe01179ca2636f1be2163a1ee | [
"MIT"
] | null | null | null | SQL/controleGames_APS_BD_SQL.sql | dsambugaro/APS_BD_1_UTFPR | b7755a67cb71dabbe01179ca2636f1be2163a1ee | [
"MIT"
] | null | null | null | SQL/controleGames_APS_BD_SQL.sql | dsambugaro/APS_BD_1_UTFPR | b7755a67cb71dabbe01179ca2636f1be2163a1ee | [
"MIT"
] | null | null | null | -- -----------------------------------------------------
-- APS Banco de Dados 1 | UTFPR-CM 2017/1
-- Prof.º André Luis Schwerz
-- Alunos: Danilo Sambugaro | Rafael Soratto
-- -----------------------------------------------------
-- -----------------------------------------------------
-- Schema controleGames_APS_BD
-- -----------------------------------------------------
DROP SCHEMA IF EXISTS `controleGames_APS_BD` ;
-- -----------------------------------------------------
-- Schema controleGames_APS_BD
-- -----------------------------------------------------
CREATE SCHEMA IF NOT EXISTS `controleGames_APS_BD` DEFAULT CHARACTER SET utf8 ;
USE `controleGames_APS_BD` ;
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`PESSOA`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`PESSOA` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`PESSOA` (
`CPF` CHAR(11) NOT NULL,
`nome_pessoa` VARCHAR(255) NOT NULL,
`data_nasc_pessoa` DATE NOT NULL,
PRIMARY KEY (`CPF`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`ESTADO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`ESTADO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`ESTADO` (
`ID` INT AUTO_INCREMENT,
`nome` VARCHAR(255) NOT NULL,
UNIQUE(`nome`),
PRIMARY KEY (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`CIDADE`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`CIDADE` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`CIDADE` (
`ID` INT AUTO_INCREMENT,
`nome` VARCHAR(255) NOT NULL,
`ESTADO_ID` INT NOT NULL,
PRIMARY KEY (`ID`),
CONSTRAINT `fk_CIDADE_ESTADO1`
FOREIGN KEY (`ESTADO_ID`)
REFERENCES `controleGames_APS_BD`.`ESTADO` (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`ENDERECO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`ENDERECO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`ENDERECO` (
`PESSOA_CPF` CHAR(11) NOT NULL,
`logradouro` VARCHAR(255) NOT NULL,
`nome` VARCHAR(255) NOT NULL,
`numero` INT NOT NULL,
`bairro` VARCHAR(255) NOT NULL,
`CEP` INT NOT NULL,
`CIDADE_ID` INT NOT NULL,
PRIMARY KEY (`PESSOA_CPF`),
CONSTRAINT `fk_ENDERECO_PESSOA`
FOREIGN KEY (`PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`PESSOA` (`CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_ENDERECO_CIDADE1`
FOREIGN KEY (`CIDADE_ID`)
REFERENCES `controleGames_APS_BD`.`CIDADE` (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`USUARIO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`USUARIO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`USUARIO` (
`ID` INT AUTO_INCREMENT,
`user` VARCHAR(20) NOT NULL,
`senha` VARCHAR(255) NOT NULL,
UNIQUE(`user`),
PRIMARY KEY (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`CLIENTE`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`CLIENTE` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`CLIENTE` (
`PESSOA_CPF` CHAR(11) NOT NULL,
`usuario` INT NOT NULL,
`ultima_compra` DATE NULL,
`email` VARCHAR(255) NOT NULL,
PRIMARY KEY (`PESSOA_CPF`),
UNIQUE(`usuario`),
CONSTRAINT `fk_CLIENTE_PESSOA1`
FOREIGN KEY (`PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`PESSOA` (`CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_CLIENTE_USUARIO`
FOREIGN KEY (`usuario`)
REFERENCES `controleGames_APS_BD`.`USUARIO` (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`FUNCIONARIO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`FUNCIONARIO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`FUNCIONARIO` (
`cracha` INT NOT NULL AUTO_INCREMENT,
`PESSOA_CPF` CHAR(11) NOT NULL,
`telefone` CHAR(11),
PRIMARY KEY (`PESSOA_CPF`),
UNIQUE (`cracha`),
CONSTRAINT `fk_FUNCIONARIO_PESSOA1`
FOREIGN KEY (`PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`PESSOA` (`CPF`) ON UPDATE CASCADE ON DELETE CASCADE);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`SUPERVISOR`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`SUPERVISOR` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`SUPERVISOR` (
`usuario` INT NOT NULL,
`FUNCIONARIO_PESSOA_CPF` CHAR(11) NOT NULL,
PRIMARY KEY (`FUNCIONARIO_PESSOA_CPF`),
UNIQUE(`usuario`),
CONSTRAINT `fk_SUPERVISOR_FUNCIONARIO1`
FOREIGN KEY (`FUNCIONARIO_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`FUNCIONARIO` (`PESSOA_CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_SUPERVISOR_USUARIO`
FOREIGN KEY (`usuario`)
REFERENCES `controleGames_APS_BD`.`USUARIO` (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`FISCALIZADO_POR`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`FISCALIZADO_POR` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`FISCALIZADO_POR` (
`FUNCIONARIO_PESSOA_CPF` CHAR(11) NOT NULL,
`SUPERVISOR_FUNCIONARIO_PESSOA_CPF` CHAR(11),
PRIMARY KEY (`FUNCIONARIO_PESSOA_CPF`),
CONSTRAINT `fk_FISCALIZADO_POR_FUNCIONARIO1`
FOREIGN KEY (`FUNCIONARIO_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`FUNCIONARIO` (`PESSOA_CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_FISCALIZADO_POR_SUPERVISOR1`
FOREIGN KEY (`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`SUPERVISOR` (`FUNCIONARIO_PESSOA_CPF`) ON UPDATE CASCADE ON DELETE SET NULL);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`EMPRESA`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`EMPRESA` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`EMPRESA` (
`CNPJ` CHAR(14) NOT NULL,
`nome` VARCHAR(255) NOT NULL,
`telefone` CHAR(11) NULL,
PRIMARY KEY (`CNPJ`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`COMPRA`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`COMPRA` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`COMPRA` (
`ID` INT NOT NULL AUTO_INCREMENT,
`preco_total` DECIMAL(10,2) NULL,
`data` DATE NOT NULL,
`EMPRESA_CNPJ` CHAR(14) NOT NULL,
`SUPERVISOR_FUNCIONARIO_PESSOA_CPF` CHAR(11) NOT NULL,
PRIMARY KEY (`ID`),
CONSTRAINT `fk_COMPRA_EMPRESA1`
FOREIGN KEY (`EMPRESA_CNPJ`)
REFERENCES `controleGames_APS_BD`.`EMPRESA` (`CNPJ`) ON UPDATE CASCADE,
CONSTRAINT `fk_COMPRA_SUPERVISOR1`
FOREIGN KEY (`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`SUPERVISOR` (`FUNCIONARIO_PESSOA_CPF`) ON UPDATE CASCADE);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`PLATAFORMA`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`PLATAFORMA` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`PLATAFORMA` (
`ID` INT AUTO_INCREMENT,
`nome` VARCHAR(20) NOT NULL,
UNIQUE(`nome`),
PRIMARY KEY (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`GENERO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`GENERO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`GENERO` (
`ID` INT AUTO_INCREMENT,
`nome` VARCHAR(20) NOT NULL,
UNIQUE(`nome`),
PRIMARY KEY (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`JOGO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`JOGO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`JOGO` (
`codigo` INT NOT NULL,
`titulo` VARCHAR(255) NOT NULL,
`genero` INT NOT NULL,
`plataforma` INT NOT NULL,
`sinopse` LONGTEXT NULL,
`lancamento` DATE NOT NULL,
`faixa_etaria` INT NOT NULL,
`preco` DECIMAL(10,2) NOT NULL,
`qtd_estoque` INT NOT NULL,
`EMPRESA_CNPJ` CHAR(14) NOT NULL,
PRIMARY KEY (`codigo`),
CONSTRAINT `fk_JOGO_EMPRESA1`
FOREIGN KEY (`EMPRESA_CNPJ`)
REFERENCES `controleGames_APS_BD`.`EMPRESA` (`CNPJ`) ON UPDATE CASCADE,
CONSTRAINT `fk_JOGO_PLATAFORMA`
FOREIGN KEY (`plataforma`)
REFERENCES `controleGames_APS_BD`.`PLATAFORMA` (`ID`),
CONSTRAINT `fk_JOGO_GENERO`
FOREIGN KEY (`genero`)
REFERENCES `controleGames_APS_BD`.`GENERO` (`ID`)
);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`COMPRA_CONTEM`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`COMPRA_CONTEM` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`COMPRA_CONTEM` (
`JOGO_codigo` INT NOT NULL,
`COMPRA_ID` INT NOT NULL,
`quantidade` INT NOT NULL,
`preco_unit` DECIMAL(10,2) NOT NULL,
PRIMARY KEY (`JOGO_codigo`, `COMPRA_ID`),
CONSTRAINT `fk_JOGO_has_COMPRA_JOGO1`
FOREIGN KEY (`JOGO_codigo`)
REFERENCES `controleGames_APS_BD`.`JOGO` (`codigo`) ON UPDATE CASCADE,
CONSTRAINT `fk_JOGO_has_COMPRA_COMPRA1`
FOREIGN KEY (`COMPRA_ID`)
REFERENCES `controleGames_APS_BD`.`COMPRA` (`ID`) ON DELETE CASCADE);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO` (
`CLIENTE_PESSOA_CPF` CHAR(11) NOT NULL,
`JOGO_codigo` INT NOT NULL,
`nota` INT NOT NULL,
PRIMARY KEY (`CLIENTE_PESSOA_CPF`, `JOGO_codigo`),
CONSTRAINT `fk_CLIENTE_has_JOGO_CLIENTE1`
FOREIGN KEY (`CLIENTE_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_CLIENTE_has_JOGO_JOGO1`
FOREIGN KEY (`JOGO_codigo`)
REFERENCES `controleGames_APS_BD`.`JOGO` (`codigo`) ON UPDATE CASCADE ON DELETE CASCADE);
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`METODO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`METODO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`METODO` (
`ID` INT AUTO_INCREMENT,
`nome` VARCHAR(45) NOT NULL,
UNIQUE(`nome`),
PRIMARY KEY (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`PEDIDO`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`PEDIDO` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`PEDIDO` (
`ID` INT AUTO_INCREMENT,
`frete` DECIMAL(10,2) NULL,
`data` DATE NOT NULL,
`valor_total` DECIMAL(10,2) NOT NULL,
`metodo_pagamento` INT NOT NULL,
`CLIENTE_PESSOA_CPF` CHAR(11) NOT NULL,
PRIMARY KEY (`ID`),
CONSTRAINT `fk_PEDIDO_CLIENTE1`
FOREIGN KEY (`CLIENTE_PESSOA_CPF`)
REFERENCES `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`) ON UPDATE CASCADE ON DELETE CASCADE,
CONSTRAINT `fk_PEDIDO_METODO`
FOREIGN KEY (`metodo_pagamento`)
REFERENCES `controleGames_APS_BD`.`METODO` (`ID`));
-- -----------------------------------------------------
-- Table `controleGames_APS_BD`.`PEDIDO_CONTEM`
-- -----------------------------------------------------
DROP TABLE IF EXISTS `controleGames_APS_BD`.`PEDIDO_CONTEM` ;
CREATE TABLE IF NOT EXISTS `controleGames_APS_BD`.`PEDIDO_CONTEM` (
`PEDIDO_ID` INT NOT NULL,
`JOGO_codigo` INT NOT NULL,
`quantidade` INT NOT NULL,
PRIMARY KEY (`PEDIDO_ID`, `JOGO_codigo`),
CONSTRAINT `fk_PEDIDO_has_JOGO_PEDIDO1`
FOREIGN KEY (`PEDIDO_ID`)
REFERENCES `controleGames_APS_BD`.`PEDIDO` (`ID`) ON DELETE CASCADE,
CONSTRAINT `fk_PEDIDO_has_JOGO_JOGO1`
FOREIGN KEY (`JOGO_codigo`)
REFERENCES `controleGames_APS_BD`.`JOGO` (`codigo`) ON UPDATE CASCADE);
DELIMITER //
CREATE TRIGGER USER_REMOVE_CLIENTE AFTER DELETE
ON CLIENTE FOR EACH ROW
BEGIN
DELETE FROM USUARIO WHERE ID = OLD.usuario;
END//
CREATE TRIGGER USER_REMOVE_SUPERVISOR AFTER DELETE
ON SUPERVISOR FOR EACH ROW
BEGIN
DELETE FROM USUARIO WHERE ID = OLD.usuario;
END//
CREATE TRIGGER ATUALIZA_ESTOQUE AFTER INSERT
ON PEDIDO_CONTEM FOR EACH ROW
BEGIN
UPDATE JOGO SET qtd_estoque = (qtd_estoque - NEW.quantidade)
WHERE codigo = NEW.JOGO_codigo;
END//
DELIMITER ;
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`PESSOA`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('78234536494', 'Jurema Romana', '1997-02-15');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('11736653660', 'João Camara', '1990-12-07');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('52867465435', 'Antonio Fernandes', '1999-03-17');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('73314993510', 'Mairieli Wessel', '1995-06-02');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('12345567898', 'Marcos Antonio Godoy', '1995-06-02');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('56380781188', 'Bruno Mendes Souza', '1996-05-15');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('54560161330', 'Darlan Felipe', '1998-09-11');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('54651643108', 'Lucas Henrique', '1989-08-30');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('12490072323', 'Michel Gomes', '2000-02-28');
INSERT INTO `controleGames_APS_BD`.`PESSOA` (`CPF`, `nome_pessoa`, `data_nasc_pessoa`) VALUES ('90146845170', 'Mohammed Lee', '1995-01-18');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`ESTADO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`ESTADO` (`nome`) VALUES ('Parana');
INSERT INTO `controleGames_APS_BD`.`ESTADO` (`nome`) VALUES ('Santa Catarina');
INSERT INTO `controleGames_APS_BD`.`ESTADO` (`nome`) VALUES ('Sao Paulo');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`CIDADE`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`CIDADE` (`nome`, `ESTADO_ID`) VALUES ('Campo Mourao', 1);
INSERT INTO `controleGames_APS_BD`.`CIDADE` (`nome`, `ESTADO_ID`) VALUES ('Maringa', 1);
INSERT INTO `controleGames_APS_BD`.`CIDADE` (`nome`, `ESTADO_ID`) VALUES ('Jundiai', 3);
INSERT INTO `controleGames_APS_BD`.`CIDADE` (`nome`, `ESTADO_ID`) VALUES ('Florianopolis', 2);
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`ENDERECO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('78234536494', 'Rua', 'Palmeira', 59, 'Arvoredo', 98765400, 1);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('11736653660', 'Avenida', 'Capitao Indio Bandeira', 829, 'Centro', 87359005, 1);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('52867465435', 'Praca', 'Constantinopla', 42, 'Prometheus', 78694200, 2);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('73314993510', 'Rua', 'Capoeira', 489, 'Centro', 87298489, 3);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('12345567898', 'Avenida', '29 de Novembro', 1580, 'Centro', 87260278, 4);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('56380781188', 'Rua', 'Pombo', 125, 'Floriano das Neves', 12978942, 3);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('54651643108', 'Praca', 'João Alvarez', 1470, 'Centro', 12345678, 4);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('12490072323', 'Avenida', 'Iamar J. Santos', 375, 'Ocarina', 78945612, 2);
INSERT INTO `controleGames_APS_BD`.`ENDERECO` (`PESSOA_CPF`, `logradouro`, `nome`, `numero`, `bairro`, `CEP`, `CIDADE_ID`) VALUES ('90146845170', 'Rua', 'José Borges', 441, 'Centro', 35715982, 1);
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`USUARIO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('romana', '53e6086284353cb73d4979f08537d950');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('camaraJose', '5527af1a9848339f9d78e74b4b7472c3');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('demonioDaGaroa', 'bbc667e139476e37b5f5bd3d35b6ec6b');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('mWessel', '908d3f20d1d1473fb16bd7599cf47928');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('Godoy', '54ad4196220b983a194b7a22d9f68b23');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('mlee', 'cb17bd2285f26466a477579632350588');
INSERT INTO `controleGames_APS_BD`.`USUARIO` (`user`, `senha`) VALUES ('sup42', 'cb17bd2285f26466a477579632350588');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`CLIENTE`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`, `usuario`, `ultima_compra`, `email`) VALUES ('78234536494', 1, '2017-02-15', 'jurema@romana.com');
INSERT INTO `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`, `usuario`, `ultima_compra`, `email`) VALUES ('11736653660', 2,'2015-10-07', 'jose@camara.com');
INSERT INTO `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`, `usuario`, `ultima_compra`, `email`) VALUES ('52867465435', 3,'2017-01-20', 'fernandes@gmail.com');
INSERT INTO `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`, `usuario`, `ultima_compra`, `email`) VALUES ('73314993510', 4, '2010-05-15', 'mwessel@gmail.com');
INSERT INTO `controleGames_APS_BD`.`CLIENTE` (`PESSOA_CPF`, `usuario`, `ultima_compra`, `email`) VALUES ('12345567898', 5, '2016-12-02', 'godoy2017@hotmail.com');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`FUNCIONARIO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`FUNCIONARIO` (`cracha`, `PESSOA_CPF`) VALUES (12, '56380781188');
INSERT INTO `controleGames_APS_BD`.`FUNCIONARIO` (`cracha`, `PESSOA_CPF`) VALUES (23, '54560161330');
INSERT INTO `controleGames_APS_BD`.`FUNCIONARIO` (`cracha`, `PESSOA_CPF`) VALUES (34, '54651643108');
INSERT INTO `controleGames_APS_BD`.`FUNCIONARIO` (`cracha`, `PESSOA_CPF`) VALUES (45, '12490072323');
INSERT INTO `controleGames_APS_BD`.`FUNCIONARIO` (`cracha`, `PESSOA_CPF`) VALUES (56, '90146845170');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`SUPERVISOR`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`SUPERVISOR` (`usuario`, `FUNCIONARIO_PESSOA_CPF`) VALUES (6, '90146845170');
INSERT INTO `controleGames_APS_BD`.`SUPERVISOR` (`usuario`, `FUNCIONARIO_PESSOA_CPF`) VALUES (7, '12490072323');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`FISCALIZADO_POR`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`FISCALIZADO_POR` (`FUNCIONARIO_PESSOA_CPF`,`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES ('56380781188','90146845170');
INSERT INTO `controleGames_APS_BD`.`FISCALIZADO_POR` (`FUNCIONARIO_PESSOA_CPF`,`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES ('54560161330','12490072323');
INSERT INTO `controleGames_APS_BD`.`FISCALIZADO_POR` (`FUNCIONARIO_PESSOA_CPF`,`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES ('54651643108','12490072323');
INSERT INTO `controleGames_APS_BD`.`FISCALIZADO_POR` (`FUNCIONARIO_PESSOA_CPF`,`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES ('12490072323','90146845170');
INSERT INTO `controleGames_APS_BD`.`FISCALIZADO_POR` (`FUNCIONARIO_PESSOA_CPF`,`SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES ('90146845170', NULL);
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`EMPRESA`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`EMPRESA` (`CNPJ`, `nome`, `telefone`) VALUES ('27082686000163', 'RockStar', '55121345678');
INSERT INTO `controleGames_APS_BD`.`EMPRESA` (`CNPJ`, `nome`, `telefone`) VALUES ('93111580000175', 'Activision Blizzard', '11258369458');
INSERT INTO `controleGames_APS_BD`.`EMPRESA` (`CNPJ`, `nome`, `telefone`) VALUES ('38644428000140', 'Ubisoft', '99123789654');
INSERT INTO `controleGames_APS_BD`.`EMPRESA` (`CNPJ`, `nome`, `telefone`) VALUES ('47462545000183', 'Nintendo', '21258741369');
INSERT INTO `controleGames_APS_BD`.`EMPRESA` (`CNPJ`, `nome`, `telefone`) VALUES ('62313357000187', 'Eletronic Arts', '12365478955');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`COMPRA`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`COMPRA` (`preco_total`, `data`, `EMPRESA_CNPJ`, `SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES (613.9, '2016-11-25', '47462545000183', '90146845170');
INSERT INTO `controleGames_APS_BD`.`COMPRA` (`preco_total`, `data`, `EMPRESA_CNPJ`, `SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES (1513.5, '2016-12-29', '38644428000140', '90146845170');
INSERT INTO `controleGames_APS_BD`.`COMPRA` (`preco_total`, `data`, `EMPRESA_CNPJ`, `SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES (300.0, '2017-01-25', '62313357000187', '90146845170');
INSERT INTO `controleGames_APS_BD`.`COMPRA` (`preco_total`, `data`, `EMPRESA_CNPJ`, `SUPERVISOR_FUNCIONARIO_PESSOA_CPF`) VALUES (329.5, '2017-02-17', '93111580000175', '90146845170');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`PLATAFORMA`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`PLATAFORMA` (`nome`) VALUES ('Wii');
INSERT INTO `controleGames_APS_BD`.`PLATAFORMA` (`nome`) VALUES ('Xbox');
INSERT INTO `controleGames_APS_BD`.`PLATAFORMA` (`nome`) VALUES ('PC');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`METODO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`METODO` (`nome`) VALUES ('Boleto');
INSERT INTO `controleGames_APS_BD`.`METODO` (`nome`) VALUES ('Depósito Bancário');
INSERT INTO `controleGames_APS_BD`.`METODO` (`nome`) VALUES ('Cartão de Crédito');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`GENERO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`GENERO` (`nome`) VALUES ('Plataforma');
INSERT INTO `controleGames_APS_BD`.`GENERO` (`nome`) VALUES ('FPS');
INSERT INTO `controleGames_APS_BD`.`GENERO` (`nome`) VALUES ('Corrida');
INSERT INTO `controleGames_APS_BD`.`GENERO` (`nome`) VALUES ('Ação');
INSERT INTO `controleGames_APS_BD`.`GENERO` (`nome`) VALUES ('Aventura');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`JOGO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`JOGO` (`codigo`, `titulo`, `genero`, `plataforma`, `sinopse`, `lancamento`, `faixa_etaria`, `preco`, `qtd_estoque`, `EMPRESA_CNPJ`) VALUES (4589, 'Super Mario', 1, 1, 'Super Mario Bros. é um jogo eletrônico lançado pela Nintendo em 1985. Considerado um clássico, Super Mario Bros. foi um dos primeiros JOGO de plataforma com rolagem lateral, recurso conhecido em inglês como side-scrolling', '2006-12-25', 0, 60.0, 5, '47462545000183');
INSERT INTO `controleGames_APS_BD`.`JOGO` (`codigo`, `titulo`, `genero`, `plataforma`, `sinopse`, `lancamento`, `faixa_etaria`, `preco`, `qtd_estoque`, `EMPRESA_CNPJ`) VALUES (7894, 'Call of Duty: Infite Warface', 2, 2, 'Call of Duty (frequentemente abreviado como CoD) é uma série de videoJOGO na primeira pessoa. A série começou no PC, mais tarde expandindo-se para os vários tipos de consolas. Também foram lançados vários JOGO spin-off. ', '2003-11-04', 16, 95.9, 5, '93111580000175');
INSERT INTO `controleGames_APS_BD`.`JOGO` (`codigo`, `titulo`, `genero`, `plataforma`, `sinopse`, `lancamento`, `faixa_etaria`, `preco`, `qtd_estoque`, `EMPRESA_CNPJ`) VALUES (1549, 'Need For Speed', 3, 3, 'Need for Speed é um jogo eletrônico de corrida que foi produzido pelo estúdio Ghost Games e lançado pela Electronic Arts para as plataformas PlayStation 4, Xbox One e para Microsoft Windows. O game, que possui uma jogabilidade não linear dá ao jogador a liberdade de explorar totalmente os cenários, é o vigésimo primeiro da franquia Need for Speed, sendo, porém, um reboot a esta popular série.', '2015-11-03', 12, 90.0, 5, '62313357000187');
INSERT INTO `controleGames_APS_BD`.`JOGO` (`codigo`, `titulo`, `genero`, `plataforma`, `sinopse`, `lancamento`, `faixa_etaria`, `preco`, `qtd_estoque`, `EMPRESA_CNPJ`) VALUES (1548, 'Far Cry Primal', 4, 3, 'Far Cry Primal é um videojogo de ação-aventura na primeira pessoa desenvolvido pela Ubisoft Montreal com a assistência de Ubisoft Toronto, Ubisoft Kiev e Ubisoft Shanghai e publicado pela Ubisoft.', '2016-03-01', 18, 165.0, 5, '38644428000140');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`COMPRA_CONTEM`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`COMPRA_CONTEM` (`JOGO_codigo`, `COMPRA_ID`, `quantidade`, `preco_unit`) VALUES (4589, 1, 15, 40.9);
INSERT INTO `controleGames_APS_BD`.`COMPRA_CONTEM` (`JOGO_codigo`, `COMPRA_ID`, `quantidade`, `preco_unit`) VALUES (1548, 2, 15, 120.9);
INSERT INTO `controleGames_APS_BD`.`COMPRA_CONTEM` (`JOGO_codigo`, `COMPRA_ID`, `quantidade`, `preco_unit`) VALUES (1549, 3, 5, 60);
INSERT INTO `controleGames_APS_BD`.`COMPRA_CONTEM` (`JOGO_codigo`, `COMPRA_ID`, `quantidade`, `preco_unit`) VALUES (7894, 4, 5, 65.9);
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO` (`CLIENTE_PESSOA_CPF`, `JOGO_codigo`, `nota`) VALUES ('78234536494', 1548, 5);
INSERT INTO `controleGames_APS_BD`.`CLIENTE_AVALIACAO_JOGO` (`CLIENTE_PESSOA_CPF`, `JOGO_codigo`, `nota`) VALUES ('73314993510', 4589, 3);
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`PEDIDO`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`PEDIDO` (`frete`, `data`, `valor_total`, `metodo_pagamento`, `CLIENTE_PESSOA_CPF`) VALUES (12.90, '2017-01-25', 72.90, 1, '73314993510');
INSERT INTO `controleGames_APS_BD`.`PEDIDO` (`frete`, `data`, `valor_total`, `metodo_pagamento`, `CLIENTE_PESSOA_CPF`) VALUES (15.60, '2016-12-11', 345.60, 2, '52867465435');
-- -----------------------------------------------------
-- Data for table `controleGames_APS_BD`.`PEDIDO_CONTEM`
-- -----------------------------------------------------
INSERT INTO `controleGames_APS_BD`.`PEDIDO_CONTEM` (`PEDIDO_ID`, `JOGO_codigo`, `quantidade`) VALUES (1, 4589, 1);
INSERT INTO `controleGames_APS_BD`.`PEDIDO_CONTEM` (`PEDIDO_ID`, `JOGO_codigo`, `quantidade`) VALUES (2, 1548, 2);
| 49.786207 | 650 | 0.616463 |
16c0767dc5bf700ff0adeb4aabb64b71f01d29c0 | 2,229 | ts | TypeScript | webapp/src/scenes/PreloadScene.ts | torives/poker-updater-demo | 0a7be9e53ab3bd973296ced0561bbbd1a9dd1493 | [
"Apache-2.0"
] | 12 | 2021-06-30T17:04:00.000Z | 2022-03-11T18:34:51.000Z | webapp/src/scenes/PreloadScene.ts | torives/poker-updater-demo | 0a7be9e53ab3bd973296ced0561bbbd1a9dd1493 | [
"Apache-2.0"
] | 9 | 2021-06-29T06:45:44.000Z | 2022-02-11T23:26:13.000Z | webapp/src/scenes/PreloadScene.ts | torives/poker-updater-demo | 0a7be9e53ab3bd973296ced0561bbbd1a9dd1493 | [
"Apache-2.0"
] | 1 | 2022-01-07T12:03:19.000Z | 2022-01-07T12:03:19.000Z | import { Howl } from "howler";
import { AudioManager } from "../AudioManager";
import { GameConstants } from "../GameConstants";
import { GameManager } from "../GameManager";
import { GameVars } from "../GameVars";
export class PreloadScene extends Phaser.Scene {
public static currentInstance: PreloadScene;
private progressBar: Phaser.GameObjects.Graphics;
constructor() {
super("PreloadScene");
PreloadScene.currentInstance = this;
}
public preload(): void {
GameVars.currentScene = this;
this.composeScene();
this.loadAssets();
}
public create(): void {
GameManager.setCurrentScene(this);
this.loadHowl();
}
public loadAssets(): void {
this.load.html("raiseform", "assets/raiseform.html");
this.load.image("bg", "assets/bg.png");
this.load.atlas("texture_atlas_1", "assets/texture_atlas_1.png", "assets/texture_atlas_1.json");
this.load.atlas("texture_atlas_2", "assets/texture_atlas_2.png", "assets/texture_atlas_2.json");
this.load.json("audiosprite", "assets/audio/audiosprite.json");
this.load.html("input-text", "assets/dom/inputText.html");
this.load.on("progress", this.updateLoadedPercentage, this);
}
private updateLoadedPercentage(percentageLoaded: number): void {
// los valores del porcentaje cargado disminuyen por algun bug
if (this.progressBar.scaleX < percentageLoaded) {
this.progressBar.scaleX = percentageLoaded;
}
}
private composeScene(): void {
this.progressBar = this.add.graphics();
this.progressBar.fillStyle(0xFFFFFF);
this.progressBar.fillRect(0, GameConstants.GAME_HEIGHT - 10, GameConstants.GAME_WIDTH, 10);
this.progressBar.scaleX = 0;
}
private loadHowl(): void {
let json = this.cache.json.get("audiosprite");
json = JSON.parse(JSON.stringify(json).replace("urls", "src"));
AudioManager.sound = new Howl(json);
AudioManager.sound.on("load", function (): void {
GameManager.onGameAssetsLoaded();
PreloadScene.currentInstance.scene.setVisible(false);
});
}
}
| 27.518519 | 104 | 0.645581 |
7e9d0be37d6e550de74c59f9a97b98ac20771f2b | 346 | sql | SQL | am-lib/src/main/resources/db/migration/V9__Add_Access_Management_Type_To_Roles_Table.sql | hmcts/am-lib | 5851a157a443e9b7887dbcf4b3d749e66fcad15f | [
"MIT"
] | null | null | null | am-lib/src/main/resources/db/migration/V9__Add_Access_Management_Type_To_Roles_Table.sql | hmcts/am-lib | 5851a157a443e9b7887dbcf4b3d749e66fcad15f | [
"MIT"
] | 568 | 2019-01-29T10:46:39.000Z | 2021-05-14T05:21:13.000Z | am-lib/src/main/resources/db/migration/V9__Add_Access_Management_Type_To_Roles_Table.sql | hmcts/am-lib | 5851a157a443e9b7887dbcf4b3d749e66fcad15f | [
"MIT"
] | 3 | 2019-02-04T15:53:20.000Z | 2021-04-10T22:38:45.000Z | CREATE TYPE ACCESS_TYPE AS enum ('ROLE_BASED', 'EXPLICIT');
CREATE TYPE ROLE_TYPE AS enum ('IDAM', 'RESOURCE');
ALTER TABLE roles
ADD COLUMN access_management_type ACCESS_TYPE NOT NULL;
ALTER TABLE roles
ALTER COLUMN role_type TYPE ROLE_TYPE USING role_type::ROLE_TYPE;
ALTER TYPE SECURITYCLASSIFICATION RENAME TO SECURITY_CLASSIFICATION;
| 31.454545 | 68 | 0.806358 |
2f66ee7857473ec0473c13962f2c422cc610ee4c | 6,861 | php | PHP | application/libraries/MY_Table.php | pandigresik/easyHRIS | e28cbf802112920ba304b28df22d11350f510772 | [
"MIT"
] | 1 | 2021-03-14T02:50:49.000Z | 2021-03-14T02:50:49.000Z | application/libraries/MY_Table.php | pandigresik/message_manager | a99f3907af217df9b420317c0a5bdce7025aa282 | [
"MIT"
] | null | null | null | application/libraries/MY_Table.php | pandigresik/message_manager | a99f3907af217df9b420317c0a5bdce7025aa282 | [
"MIT"
] | 1 | 2020-05-03T21:33:11.000Z | 2020-05-03T21:33:11.000Z | <?php
if (!defined('BASEPATH')) {
exit('No direct script access allowed');
}
class MY_Table extends CI_Table
{
public $extra_columns = array();
public $extra_header = array();
public $key_record = array();
public $hiddenField = [];
public $withNumber = true;
private $startNumber = 1;
public function __construct(array $config = array())
{
parent::__construct($config);
}
// --------------------------------------------------------------------
/**
* Generate the table.
*
* @param mixed $table_data
*
* @return string
*/
public function generate($table_data = null)
{
// The table data can optionally be passed to this function
// either as a database result object or an array
if (!empty($table_data)) {
if ($table_data instanceof CI_DB_result) {
$this->_set_from_db_result($table_data);
} elseif (is_array($table_data)) {
$this->_set_from_array($table_data);
}
}
// Is there anything to display? No? Smite them!
if (empty($this->heading) && empty($this->rows)) {
return 'Undefined table data';
}
// Compile and validate the template date
$this->_compile_template();
// Validate a possibly existing custom cell manipulation function
if (isset($this->function) && !is_callable($this->function)) {
$this->function = null;
}
// Build the table!
$out = $this->template['table_open'].$this->newline;
// Add any caption here
if ($this->caption) {
$out .= '<caption>'.$this->caption.'</caption>'.$this->newline;
}
// Is there a table heading to display?
if (!empty($this->heading)) {
if(!empty($this->extra_columns)){
$this->extra_header = empty($this->extra_header) ? [['data' => 'Aksi', 'rowspan' => count($this->heading)]] : $this->extra_header;
}
if(!empty($this->extra_header)){
foreach($this->extra_header as $_extra_header){
array_push($this->heading[0],$_extra_header);
}
}
if ($this->withNumber) {
array_unshift($this->heading[0], ['data' => 'No', 'rowspan' => count($this->heading)]);
}
$out .= $this->template['thead_open'].$this->newline;
foreach ($this->heading as $headings) {
$out .= $this->template['heading_row_start'].$this->newline;
foreach ($headings as $heading) {
$temp = $this->template['heading_cell_start'];
foreach ($heading as $key => $val) {
if ($key !== 'data') {
$temp = str_replace('<th', '<th '.$key.'="'.$val.'"', $temp);
}
}
$out .= $temp.(isset($heading['data']) ? $heading['data'] : '').$this->template['heading_cell_end'];
}
$out .= $this->template['heading_row_end'].$this->newline;
}
$out .= $this->template['thead_close'].$this->newline;
}
// Build the table rows
if (!empty($this->rows)) {
$out .= $this->template['tbody_open'].$this->newline;
$i = 1;
foreach ($this->rows as $row) {
if (!is_array($row)) {
break;
}
// We use modulus to alternate the row colors
$name = fmod($i++, 2) ? '' : 'alt_';
if (!empty($this->key_record)) {
$tmp_key = array();
foreach ($this->key_record as $_v) {
$tmp_key[$_v] = $row[$_v]['data'];
}
$out .= substr($this->template['row_'.$name.'start'], 0, strlen($this->template['row_'.$name.'start']) - 1).' data-key=\''.json_encode($tmp_key).'\'>'.$this->newline;
} else {
$out .= $this->template['row_'.$name.'start'].$this->newline;
}
if (!empty($this->extra_columns)) {
$row = array_merge($row, $this->extra_columns);
}
if ($this->withNumber) {
array_unshift($row, ['data' => $this->startNumber]);
++$this->startNumber;
}
foreach ($row as $indexCell => $cell) {
$temp = $this->template['cell_'.$name.'start'];
if (!empty($this->hiddenField)) {
if (in_array($indexCell, $this->hiddenField)) {
if (!empty($indexCell)) {
continue;
}
//die();
}
}
foreach ($cell as $key => $val) {
if ($key !== 'data') {
$temp = str_replace('<td', '<td '.$key.'="'.$val.'"', $temp);
}
}
$cell = isset($cell['data']) ? $cell['data'] : '';
$out .= $temp;
if ($cell === '' or $cell === null) {
$out .= $this->empty_cells;
} elseif (isset($this->function)) {
$out .= call_user_func($this->function, $indexCell, $cell);
} else {
$out .= $cell;
}
$out .= $this->template['cell_'.$name.'end'];
}
$out .= $this->template['row_'.$name.'end'].$this->newline;
}
$out .= $this->template['tbody_close'].$this->newline;
}
$out .= $this->template['table_close'];
// Clear table class properties before generating the table
$this->clear();
return $out;
}
public function setStartNumber($number)
{
$this->startNumber = $number;
}
private function is_multi_array($arr)
{
rsort($arr);
return isset($arr[0]) && is_array($arr[0]);
}
/**
* Set the value of withNumber.
*
* @return self
*/
public function setWithNumber($withNumber)
{
$this->withNumber = $withNumber;
}
/**
* Get the value of hiddenField.
*/
public function getHiddenField()
{
return $this->hiddenField;
}
/**
* Set the value of hiddenField.
*
* @return self
*/
public function setHiddenField($hiddenField)
{
$this->hiddenField = $hiddenField;
}
}
| 32.363208 | 186 | 0.445416 |
fe51a6b80a5cd727c4772649f5a118817a3329dd | 781 | c | C | Kelas/kumpulansoalaplro2020/src/34_DNAKambing.c | Reylyer/Alpro-A1 | ce96a87fffc68cd23afbaff332b1895578530eb5 | [
"Unlicense"
] | null | null | null | Kelas/kumpulansoalaplro2020/src/34_DNAKambing.c | Reylyer/Alpro-A1 | ce96a87fffc68cd23afbaff332b1895578530eb5 | [
"Unlicense"
] | null | null | null | Kelas/kumpulansoalaplro2020/src/34_DNAKambing.c | Reylyer/Alpro-A1 | ce96a87fffc68cd23afbaff332b1895578530eb5 | [
"Unlicense"
] | null | null | null | /*
* Nama Program : 34_DNAKambing.c
* Deskripsi : menghitung relasi saudara dan tidak saudara dari kumpulan DNA kambing
* Nama : Givandra Haikal Adjie - 24060121130063
* Tanggal : 29, Maret 2022
**/
#include <stdio.h>
int main(){
// kamus
int N;
int i;
int j;
int nS=0;
int ntS=0;
int *T;
// Algoritma
//input
scanf("%d", &N);
//proses
if(N <= 0) printf("N harus positif");
else{
T = (int*) malloc(N * sizeof *T);
for(i = 0; i < N; i++) scanf("%d", T + i);
for(i = 0; i < N; i++){
for(j = i+1; j < N; j++){
if(T[j] - T[i] < 3) nS++;
else ntS++;
}
}
printf("%d %d", nS, ntS);
}
return 0;
} | 21.108108 | 89 | 0.440461 |
4e3dc2f31ce7eae2252d27a9792f68eb67bb7853 | 409 | swift | Swift | BoxOffice+ReactorKit/BoxOffice+ReactorKit/Sources/Views/DetailTableViewCellReactor.swift | haeseoklee/BoxOffice-ReactorKit | 33462cc9d495999bd2e0b3b7018f40269d0d5dec | [
"MIT"
] | null | null | null | BoxOffice+ReactorKit/BoxOffice+ReactorKit/Sources/Views/DetailTableViewCellReactor.swift | haeseoklee/BoxOffice-ReactorKit | 33462cc9d495999bd2e0b3b7018f40269d0d5dec | [
"MIT"
] | 11 | 2022-01-16T07:43:00.000Z | 2022-01-24T12:04:56.000Z | BoxOffice+ReactorKit/BoxOffice+ReactorKit/Sources/Views/DetailTableViewCellReactor.swift | haeseoklee/BoxOffice-ReactorKit | 33462cc9d495999bd2e0b3b7018f40269d0d5dec | [
"MIT"
] | null | null | null | //
// BoxOfficeDetailTableViewCellReactor.swift
// BoxOffice+ReactorKit
//
// Created by Haeseok Lee on 2022/01/13.
//
import Foundation
import ReactorKit
final class DetailTableViewCellReactor: Reactor {
// Action
typealias Action = NoAction
// Properties
let initialState: Comment
// Functions
init(comment: Comment) {
self.initialState = comment
}
}
| 17.041667 | 49 | 0.669927 |
dd756fe3c9531932c7420528b2c053f99b681215 | 191 | php | PHP | core/controllers/ConsoleController.php | wwb382899012/yii2-amqp | e749e55d5f6de4ecd14b6ff7bff16c806067d37c | [
"BSD-3-Clause"
] | null | null | null | core/controllers/ConsoleController.php | wwb382899012/yii2-amqp | e749e55d5f6de4ecd14b6ff7bff16c806067d37c | [
"BSD-3-Clause"
] | null | null | null | core/controllers/ConsoleController.php | wwb382899012/yii2-amqp | e749e55d5f6de4ecd14b6ff7bff16c806067d37c | [
"BSD-3-Clause"
] | null | null | null | <?php
/**
* Created by PhpStorm.
* User: wenwb
* Date: 2019/8/6
* Time: 16:32
*/
namespace core\controllers;
use yii\console\Controller;
class ConsoleController extends controller{
} | 12.733333 | 43 | 0.696335 |
5d643a7af7f0abeb1330f79d8ae4e5f2f871ff01 | 2,151 | kt | Kotlin | MVVMArchitectureLiveDataRetrofitPaging/app/src/main/java/com/samruddhi/mvvmarchitecturelivedataretrofitpaging/RestaurantsAdapter.kt | MuralikrishnaGS/Android-Jetpack-Components | 9bbd4118630966ec6cee9cf8b77bc24840e43065 | [
"MIT"
] | null | null | null | MVVMArchitectureLiveDataRetrofitPaging/app/src/main/java/com/samruddhi/mvvmarchitecturelivedataretrofitpaging/RestaurantsAdapter.kt | MuralikrishnaGS/Android-Jetpack-Components | 9bbd4118630966ec6cee9cf8b77bc24840e43065 | [
"MIT"
] | null | null | null | MVVMArchitectureLiveDataRetrofitPaging/app/src/main/java/com/samruddhi/mvvmarchitecturelivedataretrofitpaging/RestaurantsAdapter.kt | MuralikrishnaGS/Android-Jetpack-Components | 9bbd4118630966ec6cee9cf8b77bc24840e43065 | [
"MIT"
] | null | null | null | package com.samruddhi.mvvmarchitecturelivedataretrofitpaging
import android.annotation.SuppressLint
import android.content.Context
import android.view.LayoutInflater
import android.view.View
import android.view.ViewGroup
import android.widget.ImageView
import android.widget.TextView
import androidx.recyclerview.widget.RecyclerView
import com.bumptech.glide.Glide
import com.samruddhi.mvvmarchitecturelivedataretrofitpaging.model.RestaurantsDataList
class RestaurantsAdapter : RecyclerView.Adapter<RestaurantsAdapter.ViewHolder>() {
private var restaurantsList = mutableListOf<RestaurantsDataList>()
private var mContext: Context? = null
fun setAllRestaurantsNearBy(restaurantsDataList: List<RestaurantsDataList>, context: Context) {
this.restaurantsList = restaurantsDataList.toMutableList()
this.mContext = context
notifyDataSetChanged()
}
override fun onCreateViewHolder(parent: ViewGroup, viewType: Int): ViewHolder {
val inflater = LayoutInflater.from(parent.context)
val view: View = inflater.inflate(R.layout.adapter_restaurants, parent, false)
return ViewHolder(view)
}
@SuppressLint("SetTextI18n")
override fun onBindViewHolder(holder: ViewHolder, position: Int) {
val restaurantsDataList = restaurantsList[position]
val restaurants = restaurantsDataList.restaurant
holder.name.text = restaurants.name
holder.timings.text = "Store Timings: " + restaurants.timings
holder.costForTwo.text = "Avg Cost for Two: ${mContext!!.getString(R.string.rupee_symbol)}${restaurants.average_cost_for_two}"
Glide.with(holder.itemView.context).load(restaurants.thumb).into(holder.imageView)
}
inner class ViewHolder(view: View) : RecyclerView.ViewHolder(view) {
var name: TextView = view.findViewById(R.id.name)
var timings: TextView = view.findViewById(R.id.timings)
var costForTwo: TextView = view.findViewById(R.id.cost_for_two)
var imageView: ImageView = view.findViewById(R.id.image_view)
}
override fun getItemCount(): Int {
return restaurantsList.size
}
} | 42.176471 | 134 | 0.753138 |
1d58d024e268e51a4225982882823877ae55db86 | 2,290 | swift | Swift | Application/Application/Recent/View/RecentImageView.swift | utrpanic/funjcam-ios | 5b5855ae8d4b287b10fd837d4d6bca2068074277 | [
"MIT"
] | null | null | null | Application/Application/Recent/View/RecentImageView.swift | utrpanic/funjcam-ios | 5b5855ae8d4b287b10fd837d4d6bca2068074277 | [
"MIT"
] | null | null | null | Application/Application/Recent/View/RecentImageView.swift | utrpanic/funjcam-ios | 5b5855ae8d4b287b10fd837d4d6bca2068074277 | [
"MIT"
] | null | null | null | import UIKit
import Entity
import TinyConstraints
final class RecentImageCell: UICollectionViewListCell {
private weak var imageView: UIImageView?
private weak var nameLabel: UILabel?
private weak var fileNameLabel: UILabel?
override init(frame: CGRect) {
super.init(frame: frame)
self.setupSubview()
}
required init?(coder: NSCoder) {
super.init(coder: coder)
self.setupSubview()
}
override func preferredLayoutAttributesFitting(_ layoutAttributes: UICollectionViewLayoutAttributes) -> UICollectionViewLayoutAttributes {
let preferredHeight = CGFloat(176)
let targetSize = CGSize(width: layoutAttributes.frame.width, height: preferredHeight)
layoutAttributes.frame.size = self.contentView.systemLayoutSizeFitting(targetSize, withHorizontalFittingPriority: .required, verticalFittingPriority: .fittingSizeLevel)
return layoutAttributes
}
private func setupSubview() {
let imageView = UIImageView()
imageView.contentMode = .scaleToFill
self.contentView.addSubview(imageView)
imageView.edgesToSuperview(excluding: [.trailing])
imageView.width(88)
imageView.height(176)
self.imageView = imageView
let containerView = UIView()
self.contentView.addSubview(containerView)
containerView.leadingToTrailing(of: imageView, offset: 16)
containerView.trailingToSuperview(offset: 16)
containerView.centerYToSuperview()
let nameLabel = UILabel()
nameLabel.font = UIFont.preferredFont(forTextStyle: .title1)
nameLabel.textColor = Resource.color("textPrimary")
containerView.addSubview(nameLabel)
nameLabel.edgesToSuperview(excluding: [.bottom])
self.nameLabel = nameLabel
let fileNameLabel = UILabel()
fileNameLabel.font = UIFont.preferredFont(forTextStyle: .title2)
fileNameLabel.textColor = Resource.color("textSecondary")
containerView.addSubview(fileNameLabel)
fileNameLabel.edgesToSuperview(excluding: [.top])
fileNameLabel.topToBottom(of: nameLabel, offset: 8)
self.fileNameLabel = fileNameLabel
}
func configure(with image: RecentImage) {
self.nameLabel?.text = image.name
self.fileNameLabel?.text = image.url?.lastPathComponent
self.imageView?.setImage(url: image.url, placeholder: nil, completion: nil)
}
}
| 36.935484 | 172 | 0.757642 |
dbbd7f0b5a59721a6a1d3d80b4236659d3130fca | 763 | asm | Assembly | oeis/345/A345094.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 11 | 2021-08-22T19:44:55.000Z | 2022-03-20T16:47:57.000Z | oeis/345/A345094.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 9 | 2021-08-29T13:15:54.000Z | 2022-03-09T19:52:31.000Z | oeis/345/A345094.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 3 | 2021-08-22T20:56:47.000Z | 2021-09-29T06:26:12.000Z | ; A345094: a(n) = Sum_{k=1..n} floor(n/k)^(floor(n/k) - 1).
; Submitted by Christian Krause
; 1,3,11,68,630,7790,117664,2097224,43046801,1000000643,25937425245,743008378547,23298085130341,793714773371879,29192926025508929,1152921504608944840,48661191875668966346,2185911559738739586562,104127350297911284587436,5242880000000001000008492,278218429446951549637314774,15519448971100888998512394048,907846434775996175432678104306,55572324035428505186121405177606,3552713678800500929356364348366969,236773830007967588876818463025697515,16423203268260658146231491098837433005
add $0,1
mov $5,$0
lpb $0
mov $3,$2
mov $4,$0
cmp $4,0
add $0,$4
mov $2,$5
div $2,$0
sub $0,1
sub $2,1
cmp $3,0
add $3,$2
pow $3,$2
add $1,$3
div $2,$1
lpe
mov $0,$1
| 33.173913 | 477 | 0.777195 |
85a8d16575491d98e95de7a74f5212ff840a5348 | 25,743 | js | JavaScript | presentation/index.js | chrisco255/deus | 6b5df6c4265f42efc5c73cd9770f0896b7cc29e2 | [
"MIT"
] | null | null | null | presentation/index.js | chrisco255/deus | 6b5df6c4265f42efc5c73cd9770f0896b7cc29e2 | [
"MIT"
] | null | null | null | presentation/index.js | chrisco255/deus | 6b5df6c4265f42efc5c73cd9770f0896b7cc29e2 | [
"MIT"
] | null | null | null | // Import React
import React from "react";
// Import Spectacle Core tags
import {
BlockQuote,
Cite,
Deck,
Heading,
ListItem,
List,
Quote,
Fill,
Slide,
Text,
Table, TableRow, TableHeaderItem, TableItem, TableHeader, TableBody,
CodePane,
Image,
Layout,
Fit
} from "spectacle";
import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, Text as ChartText, ResponsiveContainer } from 'recharts';
import { Timeline, TimelineEvent } from 'react-event-timeline';
// Import image preloader util
import preloader from "spectacle/lib/utils/preloader";
// Import theme
import createTheme from "spectacle/lib/themes/default";
// Require CSS
require("normalize.css");
require("spectacle/lib/themes/default/index.css");
const images = {
city: require("../assets/city.jpg"),
kat: require("../assets/kat.png"),
logo: require("../assets/formidable-logo.svg"),
markdown: require("../assets/markdown.png"),
angular: require("../assets/angular.svg"),
react: require("../assets/react.svg"),
developerSatisfaction: require("../assets/developerSatisfaction.png"),
trends: require("../assets/trends.png"),
chefSteps: require("../assets/ionic/chefsteps-sq.png"),
pacifica: require("../assets/ionic/pacifica-sq.png"),
sworkit: require("../assets/ionic/sworkit-sq.jpg"),
untappd: require("../assets/ionic/untappd-sq.jpg"),
airbnb: require("../assets/react/airbnb-sq.png"),
baidu: require("../assets/react/baidu-sq.png"),
bloomberg: require("../assets/react/bloomberg-sq.jpg"),
facebook: require("../assets/react/facebook-sq.png"),
instagram: require("../assets/react/instagram-sq.jpg"),
tesla: require("../assets/react/tesla-sq.jpg"),
vogue: require("../assets/react/vogue-sq.jpg"),
walmart: require("../assets/react/walmart-sq.jpg"),
};
preloader(images);
const theme = createTheme({
primary: "white",
secondary: "#1F2022",
tertiary: "#03A9FC",
quartenary: "#CECECE"
}, {
primary: "Montserrat",
secondary: "Helvetica"
});
const data = [
{name: 'Q1 2015', react: 15960, angular2: 1560},
{name: 'Q2 2015', react: 21270, angular2: 3210},
{name: 'Q3 2015', react: 26610, angular2: 4830},
{name: 'Q4 2015', react: 31950, angular2: 6480},
{name: 'Q1 2016', react: 37290, angular2: 9750},
{name: 'Q2 2016', react: 44932, angular2: 13020},
{name: 'Q3 2016', react: 50720, angular2: 16290},
{name: 'Q4 2016', react: 56825, angular2: 18810},
{name: 'Q1 2017', react: 64812, angular2: 22830},
];
const TimelineIcon = ({ children }) => (
<div
style={{
position: 'relative',
left: '-25%',
top: '-25%',
height: '100%',
width: '100%',
display: 'flex',
justifyContent: 'center',
alignItems: 'center'
}}>
</div>
);
export default class Presentation extends React.Component {
render() {
return (
<Deck transition={["zoom", "slide"]} transitionDuration={500} theme={theme}>
<Slide transition={["zoom"]} bgColor="primary">
<Heading size={1} fit caps lineHeight={1} textColor="secondary">
The Case for React
</Heading>
<Text margin="10px 0 0" textColor="tertiary" size={1} fit bold>
Functional, Consistent, Flexible, Simple, Superior UI
</Text>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="tertiary" caps>The Cost of Development at NU</Heading>
<List>
<ListItem textSize={24}>NU has 16 UI Devs</ListItem>
<ListItem textSize={24}>NU closes 22 UI stories per month (41 so far in May)</ListItem>
<ListItem textSize={24}>NU generates 52 defects per month
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={18}>27 closed per month</ListItem>
<ListItem textSize={18}>10 cancelled per month</ListItem>
<ListItem textSize={18}>Carrying 15 defects per month</ListItem>
<ListItem textSize={18}>More than 50% of all defects are UI defects (27 per month)</ListItem>
</List>
</ListItem>
<ListItem textSize={24}>Each UI story takes 9 days to close per developer
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={18}>Our stack is a bottleneck</ListItem>
<ListItem textSize={18}>Y Tests (1 day), PR requests (2 days)</ListItem>
<ListItem textSize={18}>9 minutes to run 160 mobile tests. 7 min to run 60 web tests. 4 min to create environment.</ListItem>
</List>
</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="tertiary" caps>The Running Deficit</Heading>
<List>
<ListItem textSize={24}>The Roadmap does not include our current balance of 134 defects
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={18}>Adding 15 defects per month, we'll double defect count in 1 year</ListItem>
</List>
</ListItem>
<ListItem textSize={24}>Upgrading to Angular 2 will significantly increase our defect count
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={18}>It will stop development to address critical defects in order to upgrade</ListItem>
</List>
</ListItem>
<ListItem textSize={24}>Will be cheaper to move to React
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={18}>Move 2x faster on feature dev (44 stories per month)</ListItem>
<ListItem textSize={18}>Reduce bug intro rate by 5X (10 per month)</ListItem>
<ListItem textSize={18}>Move wasted bug fix capacity to feature development (27 stories)</ListItem>
<ListItem textSize={18}>Net increase in throughput from 2.5 stories per dev to more than 5</ListItem>
<ListItem textSize={18}>Achieve feature parity quickly</ListItem>
<ListItem textSize={18}>Remove the defect backlog, focus on roadmap</ListItem>
</List>
</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="tertiary" caps>The Business Case for React</Heading>
<List>
<ListItem textSize={24}>Cuts development time in half</ListItem>
<ListItem textSize={24}>React simplifies testing
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={20}>Better testing tools (Snapshots)</ListItem>
<ListItem textSize={20}>Y Tests become optional</ListItem>
</List>
</ListItem>
<ListItem textSize={24}>React speeds development time
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={20}>Single development effort</ListItem>
<ListItem textSize={20}>Start with React for Web, port to mobile via React Native</ListItem>
<ListItem textSize={20}>Feature parity between mobile and web</ListItem>
</List>
</ListItem>
<ListItem textSize={24}>React has a bigger, stronger community
<List style={{ marginLeft: 36, fontSize: 16 }}>
<ListItem textSize={20}>Standardized, documented, well-supported</ListItem>
<ListItem textSize={20}>Shopify's Polaris for Web</ListItem>
<ListItem textSize={20}>React Native for Mobile</ListItem>
<ListItem textSize={20}>Storybook took for component documentation & demonstration</ListItem>
<ListItem textSize={20}>Sketch integration</ListItem>
</List>
</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="secondary" caps>The Choice</Heading>
<div style={{ display: "flex" }}>
<Fill bgColor="tertiary">
<img src={images.angular} style={{ height: 180 }} />
<Text>Angular 2</Text>
</Fill>
<Fill>
<img src={images.react} style={{ height: 200 }} />
<Text>React</Text>
</Fill>
</div>
</Slide>
<Slide transition={["fade"]} bgColor="primary">
<div>
<Text textSize={30} style={{ fontWeight: "bold", textAlign: "left", }} textColor="tertiary" caps>Brief History of Angular & React</Text>
</div>
<div style={{ textAlign: "left" }}>
<Text textSize={24} style={{ fontWeight: "bold" }}>2010</Text>
<Text textSize={20}>Angular created as hobby project by Misko Hevery</Text>
</div>
<div style={{ textAlign: "left" }}>
<Text textSize={24} style={{ fontWeight: "bold" }}>2011</Text>
<Text textSize={20}>Facebook starts using React in production</Text>
</div>
<div style={{ textAlign: "left" }}>
<Text textSize={24} style={{ fontWeight: "bold" }}>2013</Text>
<Text textSize={20}>Facebook open sources React</Text>
</div>
<div style={{ textAlign: "left" }}>
<Text textSize={24} style={{ fontWeight: "bold" }}>2014</Text>
<Text textSize={20}>Angular team announces incompatible rewrite of popular framework</Text>
</div>
<div style={{ textAlign: "left" }}>
<Text textSize={24} style={{ fontWeight: "bold" }}>2016</Text>
<Text textSize={20}>Angular 2 released</Text>
</div>
</Slide>
<Slide>
<Heading size={6} textColor="tertiary">What was the catalyst that triggered Angular to rewrite their popular framework?</Heading>
</Slide>
<Slide>
<Heading size={5} textColor="tertiary">How React Changed the Game</Heading>
<List>
<ListItem>Component driven architecture</ListItem>
<ListItem>Simple, minimalist architecture</ListItem>
<ListItem>Virtual DOM => High Performance</ListItem>
<ListItem>Unidirectional data flow => More predictable state management</ListItem>
</List>
</Slide>
<Slide>
<Heading size={5} textColor="tertiary">The Simplicity of React</Heading>
<List>
<ListItem>Built on Functional principles</ListItem>
<ListItem>Similar to mathematical functions: <br />f(x) => x</ListItem>
<ListItem>React components are a function of <br />f(state, props) => UI</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="secondary" textColor="primary">
<BlockQuote>
<Quote>Simplicity is prerequisite for reliability.</Quote>
<Cite>Edsger W. Dijkstra</Cite>
</BlockQuote>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>A One-Line React Component</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/react-component.example")}
margin="20px auto"
/>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>An equivalent Angular Component</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/angular-component.example")}
margin="20px auto"
/>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/angular-module.example")}
margin="20px auto"
/>
</Slide>
{/*<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>A Stateful React Component</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/react-component2.example")}
margin="20px auto"
/>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>An Equivalent Stateful Angular Component 1 of 2</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/angular-component2.example")}
margin="20px auto"
/>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>A Stateful Angular Component 2 of 2</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/angular-module2.example")}
margin="20px auto"
/>
</Slide>*/}
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>Angular Is Over-Engineered</Heading>
<List>
<ListItem>React has <a href="https://facebook.github.io/react/docs/react-api.html" target="blank">11</a> top level API methods</ListItem>
<ListItem>Angular has <a href="https://angular.io/docs/ts/latest/api/" target="_blank">over 500</a> top level API concepts</ListItem>
<ListItem>Huge surface area means more complexity, more moving parts, bigger learning curve</ListItem>
<ListItem>Introduces: structural directives, pipes, declarations, modules, injectors, services, view encapsulation, decorators, observables</ListItem>
</List>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>Angular Is Over-Engineered</Heading>
<List>
<ListItem>Angular forces decisions on you <br />(i.e. TypeScript)</ListItem>
<ListItem>Angular's engine is more complex</ListItem>
<ListItem>Zone.js overrides native HTTP methods</ListItem>
<ListItem>Has two module systems</ListItem>
<ListItem>Ahead of Time compiler generates code 3-5X the size of source: <a target="_blank" href="https://docs.google.com/document/d/195L4WaDSoI_kkW094LlShH6gT3B7K1GZpSBnnLkQR-g/edit#heading=h.wt8e1dv0pljj">Link</a></ListItem>
</List>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>Angular Is Over-Engineered</Heading>
<List>
<ListItem>Change detection strategy now a concern</ListItem>
<ListItem>Built on experimental language features (i.e. decorators)</ListItem>
<ListItem>Angular DSL is not valid HTML</ListItem>
<ListItem>1 Megabyte just to do hello world</ListItem>
<ListItem>Requires HTML parser and sanitizer</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="secondary" textColor="primary">
<BlockQuote>
<Quote style={{ fontSize: 44 }}>There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies and the other way is to make it so complicated that there are no obvious deficiencies.</Quote>
<Cite>C.A.R. Hoare, The 1980 ACM Turing Award Lecture</Cite>
</BlockQuote>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>React's Consistency</Heading>
<Heading size={6}>2013</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/react2013.example")}
margin="20px auto"
/>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>React's Consistency</Heading>
<Heading size={6}>2017</Heading>
<CodePane
lang="javascript"
source={require("raw-loader!../assets/react2017.example")}
margin="20px auto"
/>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="Angular has a 64% retention rate among devs vs. React's 92% retention. Over 9000 devs surveyed.">
<Heading textColor="tertiary" size={4}>React Has Highest Satisfaction Rating</Heading>
<Fill>
<Image height={500} src={images.developerSatisfaction}/>
</Fill>
</Slide>
<Slide style={{ fontSize: 14 }} notes="React is not only more popular, it's growing faster. Gaining 77 stars a day vs Angular's 38. Hundreds more contributors.">
<Heading size={6} textColor="tertiary">React's Popularity</Heading>
<Text textSize={24}>GitHub Stars Over Time</Text>
<div style={{ display: "flex", justifyContent: "center" }}>
<ResponsiveContainer width="100%" height={400}>
<LineChart
margin={{ top: 5, right: 30, left: 20, bottom: 5 }}
data={data}>
<XAxis dataKey="name"/>
<YAxis/>
<CartesianGrid strokeDasharray="3 3"/>
<Tooltip/>
<Legend />
<Line type="monotone" dataKey="react" name="React" stroke="blue" activeDot={{r: 8}}/>
<Line type="monotone" dataKey="angular2" name="Angular 2" stroke="red" />
</LineChart>
</ResponsiveContainer>
</div>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="More devs want to learn React than Angular 2.">
<Heading textColor="tertiary" size={4}>More People Want to Learn React</Heading>
<Image height={500} src={images.trends} />
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>React Has Better Performance</Heading>
<List>
<ListItem>Consistently Faster than Angular 2: <br /> <a href="https://auth0.com/blog/updated-and-improved-more-benchmarks-virtual-dom-vs-angular-12-vs-mithril-js-vs-the-rest/" target="_blank">Auth0 Benchmark</a></ListItem>
<ListItem>Lighter weight: 43KB vs. 766KB</ListItem>
<ListItem>Less Memory Usage</ListItem>
</List>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>React's "Learn Once, Write Anywhere" Philosophy</Heading>
<List>
<ListItem>Native mobile</ListItem>
<ListItem>Native desktop</ListItem>
<ListItem>Native Mac OSX</ListItem>
<ListItem>Native Touchbar</ListItem>
<ListItem>Native Windows</ListItem>
<ListItem>HTML5 Canvas</ListItem>
<ListItem>VR</ListItem>
</List>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="tertiary" caps>Built With Ionic</Heading>
<div style={{ display: "flex", flexWrap: "wrap", justifyContent: "center" }}>
<div style={{marginRight: 15}}>
<img src={images.chefSteps} style={{ height: 180 }} />
<Text textSize={20}>Chef Steps</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.pacifica} style={{ height: 180 }} />
<Text textSize={20}>Pacifica</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.sworkit} style={{ height: 180 }} />
<Text textSize={20}>Sworkit</Text>
</div>
<div>
<img src={images.untappd} style={{ height: 180 }} />
<Text textSize={20}>Untappd</Text>
</div>
</div>
</Slide>
<Slide transition={["fade"]} bgColor="primary" textColor="secondary">
<Heading size={6} textColor="tertiary" caps>Built With React Native</Heading>
<div style={{ display: "flex", flexWrap: "wrap", justifyContent: "center" }}>
<div style={{marginRight: 15}}>
<img src={images.airbnb} style={{ height: 180 }} />
<Text textSize={20}>AirBnB</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.baidu} style={{ height: 180 }} />
<Text textSize={20}>Baidu</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.bloomberg} style={{ height: 180 }} />
<Text textSize={20}>Bloomberg</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.facebook} style={{ height: 180 }} />
<Text textSize={20}>Facebook</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.instagram} style={{ height: 180 }} />
<Text textSize={20}>Instagram</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.tesla} style={{ height: 180 }} />
<Text textSize={20}>Tesla</Text>
</div>
<div style={{marginRight: 15}}>
<img src={images.vogue} style={{ height: 180 }} />
<Text textSize={20}>Vogue</Text>
</div>
<div>
<img src={images.walmart} style={{ height: 180 }} />
<Text textSize={20}>Wal-Mart</Text>
</div>
</div>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>React Is A Joy To Work With</Heading>
<List>
<ListItem>React is faster, easier and more reliable</ListItem>
<ListItem>React has stronger JS community backing and support</ListItem>
<ListItem>This presentation was written in React</ListItem>
</List>
</Slide>
<Slide transition={["zoom", "fade"]} bgColor="primary" notes="<ul><li>talk about that</li><li>and that</li></ul>">
<Heading textColor="tertiary" size={4}>Design Systems</Heading>
</Slide>
</Deck>
);
}
}
/*<Slide transition={["zoom"]} bgColor="primary">
<Heading size={6} caps textColor="secondary">
JS Framework Timeline
</Heading>
<div style={{ height: 500, overflowY: 'auto', background: '#EEE', border: '1px solid #333' }}>
<Timeline>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>In the beginning...</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>There was JQuery.</Text>
</TimelineEvent>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>July 5, 2010</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>Knockout JS released.</Text>
</TimelineEvent>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>October 13, 2010</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>Backbone released.</Text>
</TimelineEvent>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>October 20, 2010</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>Angular 1 released.</Text>
</TimelineEvent>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>October 2010</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>Angular 1 released.</Text>
</TimelineEvent>
<TimelineEvent
contentStyle={{ padding: 30 }}
createdAt={<Text textSize={16} style={{ color: 'white' }}>October 2010</Text>}
style={{ fontWeight: 400 }}
icon={<TimelineIcon><img src={images.angular} style={{ height: '80%', }} /></TimelineIcon>}
iconColor={theme.secondary}
container="card"
>
<Text textSize={22}>Angular 1 released.</Text>
</TimelineEvent>
</Timeline>
</div>
</Slide>*/
| 47.584104 | 265 | 0.592783 |
406106568e2425ac2b699fa4570160ef70511675 | 692 | sql | SQL | P2_studies/cc2/Postgres/theta_omega.sql | chackoge/ERNIE_Plus | 7e480c47a69fc2f736ac7fb55ece35dbff919938 | [
"MIT"
] | 6 | 2017-09-26T23:45:52.000Z | 2021-10-18T22:58:38.000Z | P2_studies/cc2/Postgres/theta_omega.sql | NETESOLUTIONS/ERNIE | 454518f28b39a6f37ad8dde4f3be15d4dccc6f61 | [
"MIT"
] | null | null | null | P2_studies/cc2/Postgres/theta_omega.sql | NETESOLUTIONS/ERNIE | 454518f28b39a6f37ad8dde4f3be15d4dccc6f61 | [
"MIT"
] | 9 | 2017-11-22T13:42:32.000Z | 2021-05-16T17:58:03.000Z | \set ON_ERROR_STOP on
-- \set ECHO all
-- DataGrip: start execution from here
SET TIMEZONE = 'US/Eastern';
CALL cc2.theta_omega_calculations(:cited_1::bigint, :cited_2::bigint, :first_year::smallint);
-- DO
-- $blocks$
--
-- BEGIN
-- IF :first_year::smallint < 1998 THEN
-- CALL cc2.theta_omega_calculations(:cited_1::bigint, :cited_2::bigint, :first_year::smallint);
-- ELSE
-- RAISE NOTICE 'First cited year greater than 1997 running query on public.scopus_reference table';
-- CALL cc2.theta_omega_calculations_main_table(:cited_1::bigint, :cited_2::bigint, :first_year::smallint);
-- END IF;
--
--
-- END
-- $blocks$; | 31.454545 | 119 | 0.656069 |
916e2b5838f3021ea38b3d41a13b04204205a628 | 199 | html | HTML | Micropractica3/src/app/propiedades/propiedades.component.html | IvanAguado22/Practica_AngularComponents_DSI | ed1388a8aadc9c43e73348a07a7f4025c48eb719 | [
"MIT"
] | null | null | null | Micropractica3/src/app/propiedades/propiedades.component.html | IvanAguado22/Practica_AngularComponents_DSI | ed1388a8aadc9c43e73348a07a7f4025c48eb719 | [
"MIT"
] | null | null | null | Micropractica3/src/app/propiedades/propiedades.component.html | IvanAguado22/Practica_AngularComponents_DSI | ed1388a8aadc9c43e73348a07a7f4025c48eb719 | [
"MIT"
] | null | null | null | <div>
<p [hidden]="hide">Comunidad: {{selectedCom}}</p>
<p [hidden]="hide">Provincia: {{selectedProv}}</p>
<button (click)="hide = false">Get propiedades del CustomElement</button>
</div> | 39.8 | 77 | 0.638191 |
fbb4c3eb04c8ae73ca99f931e25487fbf034e795 | 316 | h | C | ntsmss.h | M2Team/phnt | 9ed46af8f746a8fe459f551f011523b4815e6a79 | [
"CC-BY-4.0"
] | null | null | null | ntsmss.h | M2Team/phnt | 9ed46af8f746a8fe459f551f011523b4815e6a79 | [
"CC-BY-4.0"
] | null | null | null | ntsmss.h | M2Team/phnt | 9ed46af8f746a8fe459f551f011523b4815e6a79 | [
"CC-BY-4.0"
] | null | null | null | NTSYSAPI
NTSTATUS
NTAPI
RtlConnectToSm(
_In_ PUNICODE_STRING ApiPortName,
_In_ HANDLE ApiPortHandle,
_In_ DWORD ProcessImageType,
_Out_ PHANDLE SmssConnection
);
NTSYSAPI
NTSTATUS
NTAPI
RtlSendMsgToSm(
_In_ HANDLE ApiPortHandle,
_In_ PPORT_MESSAGE MessageData
);
| 16.631579 | 38 | 0.712025 |
396da11c34ff23470398bdb0ba9a65f7d7f1f00e | 502 | html | HTML | angular/src/app/customers/customer-create/select-booking/select-booking.component.html | mikedevatos/spring-boot-rest-jwt-angular | c69d5790c5a06511dd98ac0c79368a729765ede5 | [
"MIT"
] | 2 | 2022-02-13T11:36:58.000Z | 2022-02-20T12:46:49.000Z | angular/src/app/customers/customer-create/select-booking/select-booking.component.html | bettercallmike/spring-boot-rest-jwt-angular | c69d5790c5a06511dd98ac0c79368a729765ede5 | [
"MIT"
] | 5 | 2020-10-31T21:37:11.000Z | 2020-11-03T17:38:41.000Z | angular/src/app/customers/customer-create/select-booking/select-booking.component.html | mikedevatos/spring-boot-rest-jwt-angular | c69d5790c5a06511dd98ac0c79368a729765ede5 | [
"MIT"
] | 1 | 2020-12-24T01:58:42.000Z | 2020-12-24T01:58:42.000Z | <div class="booking-dates-container">
<h3> Select Booking Dates </h3>
<div>
<nz-date-picker [(ngModel)]="dateStart" [nzFormat]="dateFormat"
[nzShowTime]="false" nzPlaceHolder="Start" (ngModelChange)="onChangeStartDate($event)"> </nz-date-picker>
</div>
<div>
<nz-date-picker [(ngModel)]="dateEnd" [nzFormat]="dateFormat" [nzShowTime]="false"
nzPlaceHolder="End" (ngModelChange)="onChangeEndDate($event)"></nz-date-picker>
</div>
</div>
| 31.375 | 114 | 0.621514 |
725cbffeea44b39ad2d15e2f373b74f47fa57bee | 1,122 | kt | Kotlin | mobile/src/main/java/com/chernowii/camcontrol/camera/goproAPI/ApiClient.kt | watchingJu/CamControl | 2a10cbdd00845c89be78a4946d0b4b55b8a922e4 | [
"MIT"
] | 102 | 2017-02-11T05:06:00.000Z | 2022-03-21T18:42:19.000Z | mobile/src/main/java/com/chernowii/camcontrol/camera/goproAPI/ApiClient.kt | watchingJu/CamControl | 2a10cbdd00845c89be78a4946d0b4b55b8a922e4 | [
"MIT"
] | 9 | 2018-03-05T23:29:52.000Z | 2022-03-21T18:48:58.000Z | mobile/src/main/java/com/chernowii/camcontrol/camera/goproAPI/ApiClient.kt | watchingJu/CamControl | 2a10cbdd00845c89be78a4946d0b4b55b8a922e4 | [
"MIT"
] | 31 | 2017-03-20T09:11:46.000Z | 2021-12-29T23:57:46.000Z | package com.chernowii.camcontrol.camera.goproAPI
import com.chernowii.camcontrol.camera.goproAPI.model.GoProResponse
import com.chernowii.camcontrol.camera.goproAPI.model.media.GoProMediaList
import com.chernowii.camcontrol.camera.goproAPI.model.media.GoProMetadata
import retrofit2.Call
import retrofit2.http.GET
import retrofit2.http.Path
import retrofit2.http.Query
interface ApiClient {
@get:GET("/gp/gpMediaList")
val mediaList: Call<GoProMediaList>
@GET("/gp/gpControl/setting/{param}/{option}")
fun config(@Path("param") param: String, @Path("option") option: String): Call<GoProResponse>
@GET("/gp/gpControl/command/{param}")
fun command(@Path("param") param: String, @Query("p") option: String): Call<GoProResponse>
@GET("gp/gpControl/execute/{param}")
fun execute(@Path("param") param: String): Call<GoProResponse>
@GET("/gp/getMediaMetadata")
fun getMediaMetadata(@Query("p") GoProMedia: String, @Query("t") MetadataOption: String): Call<GoProMetadata>
@GET("/gp/getMediaMetadata")
fun getThumbnail(@Query("p") GoProMedia: String): Call<GoProMetadata>
}
| 35.0625 | 113 | 0.742424 |
26b87c41e8ed827459600dc83c7839898e3b073d | 2,964 | java | Java | lib_obex/src/main/java/javax/obex/utils/BluetoothObexTransport.java | Yaqiok/AndroidOBEXTranslate | 433a10934c8d073961d82e3687ec5450a7df8571 | [
"Apache-2.0"
] | null | null | null | lib_obex/src/main/java/javax/obex/utils/BluetoothObexTransport.java | Yaqiok/AndroidOBEXTranslate | 433a10934c8d073961d82e3687ec5450a7df8571 | [
"Apache-2.0"
] | null | null | null | lib_obex/src/main/java/javax/obex/utils/BluetoothObexTransport.java | Yaqiok/AndroidOBEXTranslate | 433a10934c8d073961d82e3687ec5450a7df8571 | [
"Apache-2.0"
] | null | null | null | package javax.obex.utils;
/*
* Copyright (C) 2014 Samsung System LSI
* 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 android.bluetooth.BluetoothSocket;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import javax.obex.ObexTransport;
/**
* Generic Obex Transport class, to be used in OBEX based Bluetooth
* Profiles.
*/
public class BluetoothObexTransport implements ObexTransport {
private BluetoothSocket mSocket = null;
public BluetoothObexTransport(BluetoothSocket socket) {
this.mSocket = socket;
}
@Override
public void close() throws IOException {
mSocket.close();
}
@Override
public DataInputStream openDataInputStream() throws IOException {
return new DataInputStream(openInputStream());
}
@Override
public DataOutputStream openDataOutputStream() throws IOException {
return new DataOutputStream(openOutputStream());
}
@Override
public InputStream openInputStream() throws IOException {
return mSocket.getInputStream();
}
@Override
public OutputStream openOutputStream() throws IOException {
return mSocket.getOutputStream();
}
@Override
public void connect() throws IOException {
}
@Override
public void create() throws IOException {
}
@Override
public void disconnect() throws IOException {
}
@Override
public void listen() throws IOException {
}
public boolean isConnected() throws IOException {
return true;
}
@Override
public int getMaxTransmitPacketSize() {
//if (mSocket.getConnectionType() != BluetoothSocket.TYPE_L2CAP) {
// return -1;
// }
return 4096;//mSocket.getMaxTransmitPacketSize();
}
@Override
public int getMaxReceivePacketSize() {
// if (mSocket.getConnectionType() != BluetoothSocket.TYPE_L2CAP) {
// return -1;
// }
return 4096;//mSocket.getMaxReceivePacketSize();
}
public String getRemoteAddress() {
if (mSocket == null) {
return null;
}
return mSocket.getRemoteDevice().getAddress();
}
@Override
public boolean isSrmSupported() {
// if (mSocket.getConnectionType() == BluetoothSocket.TYPE_L2CAP) {
// return true;
// }
return false;
}
}
| 29.64 | 75 | 0.675439 |
a265a12ab1148189356d0fb5749da86adba55723 | 3,417 | kt | Kotlin | covidstats/src/main/java/me/tatocaster/covidstats/screens/CovidStatsFragment.kt | tatocaster/covid19-IoT | 32c3eab1e0f544d75457bc5300d82a38b91e7126 | [
"MIT"
] | 3 | 2020-04-08T06:13:16.000Z | 2020-05-21T19:54:18.000Z | covidstats/src/main/java/me/tatocaster/covidstats/screens/CovidStatsFragment.kt | tatocaster/covid19-IoT | 32c3eab1e0f544d75457bc5300d82a38b91e7126 | [
"MIT"
] | null | null | null | covidstats/src/main/java/me/tatocaster/covidstats/screens/CovidStatsFragment.kt | tatocaster/covid19-IoT | 32c3eab1e0f544d75457bc5300d82a38b91e7126 | [
"MIT"
] | null | null | null | package me.tatocaster.covidstats.screens
import android.content.Context
import android.os.Bundle
import android.view.LayoutInflater
import android.view.View
import android.view.ViewGroup
import android.widget.Button
import android.widget.TextView
import android.widget.Toast
import androidx.lifecycle.Observer
import androidx.lifecycle.ViewModelProvider
import me.tatocaster.core.base.*
import me.tatocaster.core.mixins.ProgressBarMixin
import me.tatocaster.covidstats.R
import me.tatocaster.covidstats.inject
import timber.log.Timber
import javax.inject.Inject
class CovidStatsFragment : BaseFragment(), ProgressBarMixin {
lateinit var covidStatsViewModel: CovidStatsViewModel
@Inject
lateinit var viewModelFactory: CovidStatsViewModelFactory
@Inject
lateinit var activityContext: Context
private lateinit var tvTotalCasesCount: TextView
private lateinit var tvTotalRecoveredCount: TextView
private lateinit var tvTotalDeathCount: TextView
private lateinit var bRefreshStats: Button
override fun onCreateView(
inflater: LayoutInflater, container: ViewGroup?,
savedInstanceState: Bundle?
): View? {
inject()
return inflater.inflate(R.layout.fragment_covid_stats, container, false)
}
override fun onViewCreated(view: View, savedInstanceState: Bundle?) {
super.onViewCreated(view, savedInstanceState)
findViewsById(view)
covidStatsViewModel = ViewModelProvider(this, viewModelFactory)[CovidStatsViewModel::class.java]
covidStatsViewModel.state.observe(viewLifecycleOwner, Observer { state ->
applyProgressbarMixin(state, view.findViewById(R.id.progressBar))
when (state) {
is JustInitial -> {
}
is JustLoading -> {
}
is JustError -> {
bRefreshStats.isEnabled = true
Toast.makeText(activityContext, R.string.server_error, Toast.LENGTH_SHORT)
.show()
}
is JustSuccess -> {
}
is CovidStatsViewModel.CasesLoaded -> {
bRefreshStats.isEnabled = true
tvTotalCasesCount.text = state.payload.totalConfirmed.toString()
tvTotalRecoveredCount.text = state.payload.totalRecovered.toString()
tvTotalDeathCount.text = state.payload.totalDeaths.toString()
}
is JustUnknownError -> {
Toast.makeText(activityContext, R.string.server_error, Toast.LENGTH_SHORT)
.show()
}
else -> throw UndefinedStateException(state)
}
})
bRefreshStats.setOnClickListener {
bRefreshStats.isEnabled = false
Toast.makeText(activityContext, R.string.loading, Toast.LENGTH_SHORT).show()
covidStatsViewModel.getCovidCases()
}
}
private fun findViewsById(view: View) {
tvTotalCasesCount = view.findViewById(R.id.tvTotalCasesCount)
tvTotalRecoveredCount = view.findViewById(R.id.tvTotalRecoveredCount)
tvTotalDeathCount = view.findViewById(R.id.tvTotalDeathCount)
bRefreshStats = view.findViewById(R.id.bRefreshStats)
}
companion object {
fun newInstance() = CovidStatsFragment()
}
} | 36.741935 | 104 | 0.664618 |
5b2b1bc05d0f07f67600b0ce428eb3f5b8e52077 | 19,336 | h | C | es/include/tencentcloud/es/v20180416/model/ClusterView.h | suluner/tencentcloud-sdk-cpp | a56c73cc3f488c4d1e10755704107bb15c5e000d | [
"Apache-2.0"
] | null | null | null | es/include/tencentcloud/es/v20180416/model/ClusterView.h | suluner/tencentcloud-sdk-cpp | a56c73cc3f488c4d1e10755704107bb15c5e000d | [
"Apache-2.0"
] | null | null | null | es/include/tencentcloud/es/v20180416/model/ClusterView.h | suluner/tencentcloud-sdk-cpp | a56c73cc3f488c4d1e10755704107bb15c5e000d | [
"Apache-2.0"
] | null | null | null | /*
* Copyright (c) 2017-2019 THL A29 Limited, a Tencent company. 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.
*/
#ifndef TENCENTCLOUD_ES_V20180416_MODEL_CLUSTERVIEW_H_
#define TENCENTCLOUD_ES_V20180416_MODEL_CLUSTERVIEW_H_
#include <string>
#include <vector>
#include <map>
#include <tencentcloud/core/utils/rapidjson/document.h>
#include <tencentcloud/core/utils/rapidjson/writer.h>
#include <tencentcloud/core/utils/rapidjson/stringbuffer.h>
#include <tencentcloud/core/AbstractModel.h>
namespace TencentCloud
{
namespace Es
{
namespace V20180416
{
namespace Model
{
/**
* 集群维度视图数据
*/
class ClusterView : public AbstractModel
{
public:
ClusterView();
~ClusterView() = default;
void ToJsonObject(rapidjson::Value &value, rapidjson::Document::AllocatorType& allocator) const;
CoreInternalOutcome Deserialize(const rapidjson::Value &value);
/**
* 获取集群健康状态
* @return Health 集群健康状态
*/
double GetHealth() const;
/**
* 设置集群健康状态
* @param Health 集群健康状态
*/
void SetHealth(const double& _health);
/**
* 判断参数 Health 是否已赋值
* @return Health 是否已赋值
*/
bool HealthHasBeenSet() const;
/**
* 获取集群是否可见
* @return Visible 集群是否可见
*/
double GetVisible() const;
/**
* 设置集群是否可见
* @param Visible 集群是否可见
*/
void SetVisible(const double& _visible);
/**
* 判断参数 Visible 是否已赋值
* @return Visible 是否已赋值
*/
bool VisibleHasBeenSet() const;
/**
* 获取集群是否熔断
* @return Break 集群是否熔断
*/
double GetBreak() const;
/**
* 设置集群是否熔断
* @param Break 集群是否熔断
*/
void SetBreak(const double& _break);
/**
* 判断参数 Break 是否已赋值
* @return Break 是否已赋值
*/
bool BreakHasBeenSet() const;
/**
* 获取平均磁盘使用率
* @return AvgDiskUsage 平均磁盘使用率
*/
double GetAvgDiskUsage() const;
/**
* 设置平均磁盘使用率
* @param AvgDiskUsage 平均磁盘使用率
*/
void SetAvgDiskUsage(const double& _avgDiskUsage);
/**
* 判断参数 AvgDiskUsage 是否已赋值
* @return AvgDiskUsage 是否已赋值
*/
bool AvgDiskUsageHasBeenSet() const;
/**
* 获取平均内存使用率
* @return AvgMemUsage 平均内存使用率
*/
double GetAvgMemUsage() const;
/**
* 设置平均内存使用率
* @param AvgMemUsage 平均内存使用率
*/
void SetAvgMemUsage(const double& _avgMemUsage);
/**
* 判断参数 AvgMemUsage 是否已赋值
* @return AvgMemUsage 是否已赋值
*/
bool AvgMemUsageHasBeenSet() const;
/**
* 获取平均cpu使用率
* @return AvgCpuUsage 平均cpu使用率
*/
double GetAvgCpuUsage() const;
/**
* 设置平均cpu使用率
* @param AvgCpuUsage 平均cpu使用率
*/
void SetAvgCpuUsage(const double& _avgCpuUsage);
/**
* 判断参数 AvgCpuUsage 是否已赋值
* @return AvgCpuUsage 是否已赋值
*/
bool AvgCpuUsageHasBeenSet() const;
/**
* 获取集群总存储大小
* @return TotalDiskSize 集群总存储大小
*/
uint64_t GetTotalDiskSize() const;
/**
* 设置集群总存储大小
* @param TotalDiskSize 集群总存储大小
*/
void SetTotalDiskSize(const uint64_t& _totalDiskSize);
/**
* 判断参数 TotalDiskSize 是否已赋值
* @return TotalDiskSize 是否已赋值
*/
bool TotalDiskSizeHasBeenSet() const;
/**
* 获取客户端请求节点
* @return TargetNodeTypes 客户端请求节点
*/
std::vector<std::string> GetTargetNodeTypes() const;
/**
* 设置客户端请求节点
* @param TargetNodeTypes 客户端请求节点
*/
void SetTargetNodeTypes(const std::vector<std::string>& _targetNodeTypes);
/**
* 判断参数 TargetNodeTypes 是否已赋值
* @return TargetNodeTypes 是否已赋值
*/
bool TargetNodeTypesHasBeenSet() const;
/**
* 获取在线节点数
* @return NodeNum 在线节点数
*/
int64_t GetNodeNum() const;
/**
* 设置在线节点数
* @param NodeNum 在线节点数
*/
void SetNodeNum(const int64_t& _nodeNum);
/**
* 判断参数 NodeNum 是否已赋值
* @return NodeNum 是否已赋值
*/
bool NodeNumHasBeenSet() const;
/**
* 获取总节点数
* @return TotalNodeNum 总节点数
*/
int64_t GetTotalNodeNum() const;
/**
* 设置总节点数
* @param TotalNodeNum 总节点数
*/
void SetTotalNodeNum(const int64_t& _totalNodeNum);
/**
* 判断参数 TotalNodeNum 是否已赋值
* @return TotalNodeNum 是否已赋值
*/
bool TotalNodeNumHasBeenSet() const;
/**
* 获取数据节点数
* @return DataNodeNum 数据节点数
*/
int64_t GetDataNodeNum() const;
/**
* 设置数据节点数
* @param DataNodeNum 数据节点数
*/
void SetDataNodeNum(const int64_t& _dataNodeNum);
/**
* 判断参数 DataNodeNum 是否已赋值
* @return DataNodeNum 是否已赋值
*/
bool DataNodeNumHasBeenSet() const;
/**
* 获取索引数
* @return IndexNum 索引数
*/
int64_t GetIndexNum() const;
/**
* 设置索引数
* @param IndexNum 索引数
*/
void SetIndexNum(const int64_t& _indexNum);
/**
* 判断参数 IndexNum 是否已赋值
* @return IndexNum 是否已赋值
*/
bool IndexNumHasBeenSet() const;
/**
* 获取文档数
* @return DocNum 文档数
*/
int64_t GetDocNum() const;
/**
* 设置文档数
* @param DocNum 文档数
*/
void SetDocNum(const int64_t& _docNum);
/**
* 判断参数 DocNum 是否已赋值
* @return DocNum 是否已赋值
*/
bool DocNumHasBeenSet() const;
/**
* 获取磁盘已使用字节数
* @return DiskUsedInBytes 磁盘已使用字节数
*/
int64_t GetDiskUsedInBytes() const;
/**
* 设置磁盘已使用字节数
* @param DiskUsedInBytes 磁盘已使用字节数
*/
void SetDiskUsedInBytes(const int64_t& _diskUsedInBytes);
/**
* 判断参数 DiskUsedInBytes 是否已赋值
* @return DiskUsedInBytes 是否已赋值
*/
bool DiskUsedInBytesHasBeenSet() const;
/**
* 获取分片个数
* @return ShardNum 分片个数
*/
int64_t GetShardNum() const;
/**
* 设置分片个数
* @param ShardNum 分片个数
*/
void SetShardNum(const int64_t& _shardNum);
/**
* 判断参数 ShardNum 是否已赋值
* @return ShardNum 是否已赋值
*/
bool ShardNumHasBeenSet() const;
/**
* 获取主分片个数
* @return PrimaryShardNum 主分片个数
*/
int64_t GetPrimaryShardNum() const;
/**
* 设置主分片个数
* @param PrimaryShardNum 主分片个数
*/
void SetPrimaryShardNum(const int64_t& _primaryShardNum);
/**
* 判断参数 PrimaryShardNum 是否已赋值
* @return PrimaryShardNum 是否已赋值
*/
bool PrimaryShardNumHasBeenSet() const;
/**
* 获取迁移中的分片个数
* @return RelocatingShardNum 迁移中的分片个数
*/
int64_t GetRelocatingShardNum() const;
/**
* 设置迁移中的分片个数
* @param RelocatingShardNum 迁移中的分片个数
*/
void SetRelocatingShardNum(const int64_t& _relocatingShardNum);
/**
* 判断参数 RelocatingShardNum 是否已赋值
* @return RelocatingShardNum 是否已赋值
*/
bool RelocatingShardNumHasBeenSet() const;
/**
* 获取初始化中的分片个数
* @return InitializingShardNum 初始化中的分片个数
*/
int64_t GetInitializingShardNum() const;
/**
* 设置初始化中的分片个数
* @param InitializingShardNum 初始化中的分片个数
*/
void SetInitializingShardNum(const int64_t& _initializingShardNum);
/**
* 判断参数 InitializingShardNum 是否已赋值
* @return InitializingShardNum 是否已赋值
*/
bool InitializingShardNumHasBeenSet() const;
/**
* 获取未分配的分片个数
* @return UnassignedShardNum 未分配的分片个数
*/
int64_t GetUnassignedShardNum() const;
/**
* 设置未分配的分片个数
* @param UnassignedShardNum 未分配的分片个数
*/
void SetUnassignedShardNum(const int64_t& _unassignedShardNum);
/**
* 判断参数 UnassignedShardNum 是否已赋值
* @return UnassignedShardNum 是否已赋值
*/
bool UnassignedShardNumHasBeenSet() const;
/**
* 获取企业版COS存储容量大小，单位GB
* @return TotalCosStorage 企业版COS存储容量大小，单位GB
*/
int64_t GetTotalCosStorage() const;
/**
* 设置企业版COS存储容量大小，单位GB
* @param TotalCosStorage 企业版COS存储容量大小，单位GB
*/
void SetTotalCosStorage(const int64_t& _totalCosStorage);
/**
* 判断参数 TotalCosStorage 是否已赋值
* @return TotalCosStorage 是否已赋值
*/
bool TotalCosStorageHasBeenSet() const;
/**
* 获取企业版集群可搜索快照cos存放的bucket名称
注意：此字段可能返回 null，表示取不到有效值。
* @return SearchableSnapshotCosBucket 企业版集群可搜索快照cos存放的bucket名称
注意：此字段可能返回 null，表示取不到有效值。
*/
std::string GetSearchableSnapshotCosBucket() const;
/**
* 设置企业版集群可搜索快照cos存放的bucket名称
注意：此字段可能返回 null，表示取不到有效值。
* @param SearchableSnapshotCosBucket 企业版集群可搜索快照cos存放的bucket名称
注意：此字段可能返回 null，表示取不到有效值。
*/
void SetSearchableSnapshotCosBucket(const std::string& _searchableSnapshotCosBucket);
/**
* 判断参数 SearchableSnapshotCosBucket 是否已赋值
* @return SearchableSnapshotCosBucket 是否已赋值
*/
bool SearchableSnapshotCosBucketHasBeenSet() const;
/**
* 获取企业版集群可搜索快照cos所属appid
注意：此字段可能返回 null，表示取不到有效值。
* @return SearchableSnapshotCosAppId 企业版集群可搜索快照cos所属appid
注意：此字段可能返回 null，表示取不到有效值。
*/
std::string GetSearchableSnapshotCosAppId() const;
/**
* 设置企业版集群可搜索快照cos所属appid
注意：此字段可能返回 null，表示取不到有效值。
* @param SearchableSnapshotCosAppId 企业版集群可搜索快照cos所属appid
注意：此字段可能返回 null，表示取不到有效值。
*/
void SetSearchableSnapshotCosAppId(const std::string& _searchableSnapshotCosAppId);
/**
* 判断参数 SearchableSnapshotCosAppId 是否已赋值
* @return SearchableSnapshotCosAppId 是否已赋值
*/
bool SearchableSnapshotCosAppIdHasBeenSet() const;
private:
/**
* 集群健康状态
*/
double m_health;
bool m_healthHasBeenSet;
/**
* 集群是否可见
*/
double m_visible;
bool m_visibleHasBeenSet;
/**
* 集群是否熔断
*/
double m_break;
bool m_breakHasBeenSet;
/**
* 平均磁盘使用率
*/
double m_avgDiskUsage;
bool m_avgDiskUsageHasBeenSet;
/**
* 平均内存使用率
*/
double m_avgMemUsage;
bool m_avgMemUsageHasBeenSet;
/**
* 平均cpu使用率
*/
double m_avgCpuUsage;
bool m_avgCpuUsageHasBeenSet;
/**
* 集群总存储大小
*/
uint64_t m_totalDiskSize;
bool m_totalDiskSizeHasBeenSet;
/**
* 客户端请求节点
*/
std::vector<std::string> m_targetNodeTypes;
bool m_targetNodeTypesHasBeenSet;
/**
* 在线节点数
*/
int64_t m_nodeNum;
bool m_nodeNumHasBeenSet;
/**
* 总节点数
*/
int64_t m_totalNodeNum;
bool m_totalNodeNumHasBeenSet;
/**
* 数据节点数
*/
int64_t m_dataNodeNum;
bool m_dataNodeNumHasBeenSet;
/**
* 索引数
*/
int64_t m_indexNum;
bool m_indexNumHasBeenSet;
/**
* 文档数
*/
int64_t m_docNum;
bool m_docNumHasBeenSet;
/**
* 磁盘已使用字节数
*/
int64_t m_diskUsedInBytes;
bool m_diskUsedInBytesHasBeenSet;
/**
* 分片个数
*/
int64_t m_shardNum;
bool m_shardNumHasBeenSet;
/**
* 主分片个数
*/
int64_t m_primaryShardNum;
bool m_primaryShardNumHasBeenSet;
/**
* 迁移中的分片个数
*/
int64_t m_relocatingShardNum;
bool m_relocatingShardNumHasBeenSet;
/**
* 初始化中的分片个数
*/
int64_t m_initializingShardNum;
bool m_initializingShardNumHasBeenSet;
/**
* 未分配的分片个数
*/
int64_t m_unassignedShardNum;
bool m_unassignedShardNumHasBeenSet;
/**
* 企业版COS存储容量大小，单位GB
*/
int64_t m_totalCosStorage;
bool m_totalCosStorageHasBeenSet;
/**
* 企业版集群可搜索快照cos存放的bucket名称
注意：此字段可能返回 null，表示取不到有效值。
*/
std::string m_searchableSnapshotCosBucket;
bool m_searchableSnapshotCosBucketHasBeenSet;
/**
* 企业版集群可搜索快照cos所属appid
注意：此字段可能返回 null，表示取不到有效值。
*/
std::string m_searchableSnapshotCosAppId;
bool m_searchableSnapshotCosAppIdHasBeenSet;
};
}
}
}
}
#endif // !TENCENTCLOUD_ES_V20180416_MODEL_CLUSTERVIEW_H_
| 32.442953 | 116 | 0.393928 |
9d435616ba97ec997db44a0c42ea163936b4ba0c | 13,406 | html | HTML | yrx/u/u-022.html | daniel-kelley/daniel-kelley.github.io | 5ea40ae2152437c39d9f1fec0e8fafd6859ea22f | [
"CC0-1.0"
] | null | null | null | yrx/u/u-022.html | daniel-kelley/daniel-kelley.github.io | 5ea40ae2152437c39d9f1fec0e8fafd6859ea22f | [
"CC0-1.0"
] | 4 | 2020-06-21T14:30:07.000Z | 2021-03-13T21:00:07.000Z | yrx/u/u-022.html | daniel-kelley/daniel-kelley.github.io | 5ea40ae2152437c39d9f1fec0e8fafd6859ea22f | [
"CC0-1.0"
] | null | null | null | <html>
<body>
<table>
<tr><td>dec</td><td>hex</td><td>char</td></tr>
<tr><td>5632</td><td>1600</td><td>ᘀ</td></tr>
<tr><td>5633</td><td>1601</td><td>ᘁ</td></tr>
<tr><td>5634</td><td>1602</td><td>ᘂ</td></tr>
<tr><td>5635</td><td>1603</td><td>ᘃ</td></tr>
<tr><td>5636</td><td>1604</td><td>ᘄ</td></tr>
<tr><td>5637</td><td>1605</td><td>ᘅ</td></tr>
<tr><td>5638</td><td>1606</td><td>ᘆ</td></tr>
<tr><td>5639</td><td>1607</td><td>ᘇ</td></tr>
<tr><td>5640</td><td>1608</td><td>ᘈ</td></tr>
<tr><td>5641</td><td>1609</td><td>ᘉ</td></tr>
<tr><td>5642</td><td>160a</td><td>ᘊ</td></tr>
<tr><td>5643</td><td>160b</td><td>ᘋ</td></tr>
<tr><td>5644</td><td>160c</td><td>ᘌ</td></tr>
<tr><td>5645</td><td>160d</td><td>ᘍ</td></tr>
<tr><td>5646</td><td>160e</td><td>ᘎ</td></tr>
<tr><td>5647</td><td>160f</td><td>ᘏ</td></tr>
<tr><td>5648</td><td>1610</td><td>ᘐ</td></tr>
<tr><td>5649</td><td>1611</td><td>ᘑ</td></tr>
<tr><td>5650</td><td>1612</td><td>ᘒ</td></tr>
<tr><td>5651</td><td>1613</td><td>ᘓ</td></tr>
<tr><td>5652</td><td>1614</td><td>ᘔ</td></tr>
<tr><td>5653</td><td>1615</td><td>ᘕ</td></tr>
<tr><td>5654</td><td>1616</td><td>ᘖ</td></tr>
<tr><td>5655</td><td>1617</td><td>ᘗ</td></tr>
<tr><td>5656</td><td>1618</td><td>ᘘ</td></tr>
<tr><td>5657</td><td>1619</td><td>ᘙ</td></tr>
<tr><td>5658</td><td>161a</td><td>ᘚ</td></tr>
<tr><td>5659</td><td>161b</td><td>ᘛ</td></tr>
<tr><td>5660</td><td>161c</td><td>ᘜ</td></tr>
<tr><td>5661</td><td>161d</td><td>ᘝ</td></tr>
<tr><td>5662</td><td>161e</td><td>ᘞ</td></tr>
<tr><td>5663</td><td>161f</td><td>ᘟ</td></tr>
<tr><td>5664</td><td>1620</td><td>ᘠ</td></tr>
<tr><td>5665</td><td>1621</td><td>ᘡ</td></tr>
<tr><td>5666</td><td>1622</td><td>ᘢ</td></tr>
<tr><td>5667</td><td>1623</td><td>ᘣ</td></tr>
<tr><td>5668</td><td>1624</td><td>ᘤ</td></tr>
<tr><td>5669</td><td>1625</td><td>ᘥ</td></tr>
<tr><td>5670</td><td>1626</td><td>ᘦ</td></tr>
<tr><td>5671</td><td>1627</td><td>ᘧ</td></tr>
<tr><td>5672</td><td>1628</td><td>ᘨ</td></tr>
<tr><td>5673</td><td>1629</td><td>ᘩ</td></tr>
<tr><td>5674</td><td>162a</td><td>ᘪ</td></tr>
<tr><td>5675</td><td>162b</td><td>ᘫ</td></tr>
<tr><td>5676</td><td>162c</td><td>ᘬ</td></tr>
<tr><td>5677</td><td>162d</td><td>ᘭ</td></tr>
<tr><td>5678</td><td>162e</td><td>ᘮ</td></tr>
<tr><td>5679</td><td>162f</td><td>ᘯ</td></tr>
<tr><td>5680</td><td>1630</td><td>ᘰ</td></tr>
<tr><td>5681</td><td>1631</td><td>ᘱ</td></tr>
<tr><td>5682</td><td>1632</td><td>ᘲ</td></tr>
<tr><td>5683</td><td>1633</td><td>ᘳ</td></tr>
<tr><td>5684</td><td>1634</td><td>ᘴ</td></tr>
<tr><td>5685</td><td>1635</td><td>ᘵ</td></tr>
<tr><td>5686</td><td>1636</td><td>ᘶ</td></tr>
<tr><td>5687</td><td>1637</td><td>ᘷ</td></tr>
<tr><td>5688</td><td>1638</td><td>ᘸ</td></tr>
<tr><td>5689</td><td>1639</td><td>ᘹ</td></tr>
<tr><td>5690</td><td>163a</td><td>ᘺ</td></tr>
<tr><td>5691</td><td>163b</td><td>ᘻ</td></tr>
<tr><td>5692</td><td>163c</td><td>ᘼ</td></tr>
<tr><td>5693</td><td>163d</td><td>ᘽ</td></tr>
<tr><td>5694</td><td>163e</td><td>ᘾ</td></tr>
<tr><td>5695</td><td>163f</td><td>ᘿ</td></tr>
<tr><td>5696</td><td>1640</td><td>ᙀ</td></tr>
<tr><td>5697</td><td>1641</td><td>ᙁ</td></tr>
<tr><td>5698</td><td>1642</td><td>ᙂ</td></tr>
<tr><td>5699</td><td>1643</td><td>ᙃ</td></tr>
<tr><td>5700</td><td>1644</td><td>ᙄ</td></tr>
<tr><td>5701</td><td>1645</td><td>ᙅ</td></tr>
<tr><td>5702</td><td>1646</td><td>ᙆ</td></tr>
<tr><td>5703</td><td>1647</td><td>ᙇ</td></tr>
<tr><td>5704</td><td>1648</td><td>ᙈ</td></tr>
<tr><td>5705</td><td>1649</td><td>ᙉ</td></tr>
<tr><td>5706</td><td>164a</td><td>ᙊ</td></tr>
<tr><td>5707</td><td>164b</td><td>ᙋ</td></tr>
<tr><td>5708</td><td>164c</td><td>ᙌ</td></tr>
<tr><td>5709</td><td>164d</td><td>ᙍ</td></tr>
<tr><td>5710</td><td>164e</td><td>ᙎ</td></tr>
<tr><td>5711</td><td>164f</td><td>ᙏ</td></tr>
<tr><td>5712</td><td>1650</td><td>ᙐ</td></tr>
<tr><td>5713</td><td>1651</td><td>ᙑ</td></tr>
<tr><td>5714</td><td>1652</td><td>ᙒ</td></tr>
<tr><td>5715</td><td>1653</td><td>ᙓ</td></tr>
<tr><td>5716</td><td>1654</td><td>ᙔ</td></tr>
<tr><td>5717</td><td>1655</td><td>ᙕ</td></tr>
<tr><td>5718</td><td>1656</td><td>ᙖ</td></tr>
<tr><td>5719</td><td>1657</td><td>ᙗ</td></tr>
<tr><td>5720</td><td>1658</td><td>ᙘ</td></tr>
<tr><td>5721</td><td>1659</td><td>ᙙ</td></tr>
<tr><td>5722</td><td>165a</td><td>ᙚ</td></tr>
<tr><td>5723</td><td>165b</td><td>ᙛ</td></tr>
<tr><td>5724</td><td>165c</td><td>ᙜ</td></tr>
<tr><td>5725</td><td>165d</td><td>ᙝ</td></tr>
<tr><td>5726</td><td>165e</td><td>ᙞ</td></tr>
<tr><td>5727</td><td>165f</td><td>ᙟ</td></tr>
<tr><td>5728</td><td>1660</td><td>ᙠ</td></tr>
<tr><td>5729</td><td>1661</td><td>ᙡ</td></tr>
<tr><td>5730</td><td>1662</td><td>ᙢ</td></tr>
<tr><td>5731</td><td>1663</td><td>ᙣ</td></tr>
<tr><td>5732</td><td>1664</td><td>ᙤ</td></tr>
<tr><td>5733</td><td>1665</td><td>ᙥ</td></tr>
<tr><td>5734</td><td>1666</td><td>ᙦ</td></tr>
<tr><td>5735</td><td>1667</td><td>ᙧ</td></tr>
<tr><td>5736</td><td>1668</td><td>ᙨ</td></tr>
<tr><td>5737</td><td>1669</td><td>ᙩ</td></tr>
<tr><td>5738</td><td>166a</td><td>ᙪ</td></tr>
<tr><td>5739</td><td>166b</td><td>ᙫ</td></tr>
<tr><td>5740</td><td>166c</td><td>ᙬ</td></tr>
<tr><td>5741</td><td>166d</td><td>᙭</td></tr>
<tr><td>5742</td><td>166e</td><td>᙮</td></tr>
<tr><td>5743</td><td>166f</td><td>ᙯ</td></tr>
<tr><td>5744</td><td>1670</td><td>ᙰ</td></tr>
<tr><td>5745</td><td>1671</td><td>ᙱ</td></tr>
<tr><td>5746</td><td>1672</td><td>ᙲ</td></tr>
<tr><td>5747</td><td>1673</td><td>ᙳ</td></tr>
<tr><td>5748</td><td>1674</td><td>ᙴ</td></tr>
<tr><td>5749</td><td>1675</td><td>ᙵ</td></tr>
<tr><td>5750</td><td>1676</td><td>ᙶ</td></tr>
<tr><td>5751</td><td>1677</td><td>ᙷ</td></tr>
<tr><td>5752</td><td>1678</td><td>ᙸ</td></tr>
<tr><td>5753</td><td>1679</td><td>ᙹ</td></tr>
<tr><td>5754</td><td>167a</td><td>ᙺ</td></tr>
<tr><td>5755</td><td>167b</td><td>ᙻ</td></tr>
<tr><td>5756</td><td>167c</td><td>ᙼ</td></tr>
<tr><td>5757</td><td>167d</td><td>ᙽ</td></tr>
<tr><td>5758</td><td>167e</td><td>ᙾ</td></tr>
<tr><td>5759</td><td>167f</td><td>ᙿ</td></tr>
<tr><td>5760</td><td>1680</td><td> </td></tr>
<tr><td>5761</td><td>1681</td><td>ᚁ</td></tr>
<tr><td>5762</td><td>1682</td><td>ᚂ</td></tr>
<tr><td>5763</td><td>1683</td><td>ᚃ</td></tr>
<tr><td>5764</td><td>1684</td><td>ᚄ</td></tr>
<tr><td>5765</td><td>1685</td><td>ᚅ</td></tr>
<tr><td>5766</td><td>1686</td><td>ᚆ</td></tr>
<tr><td>5767</td><td>1687</td><td>ᚇ</td></tr>
<tr><td>5768</td><td>1688</td><td>ᚈ</td></tr>
<tr><td>5769</td><td>1689</td><td>ᚉ</td></tr>
<tr><td>5770</td><td>168a</td><td>ᚊ</td></tr>
<tr><td>5771</td><td>168b</td><td>ᚋ</td></tr>
<tr><td>5772</td><td>168c</td><td>ᚌ</td></tr>
<tr><td>5773</td><td>168d</td><td>ᚍ</td></tr>
<tr><td>5774</td><td>168e</td><td>ᚎ</td></tr>
<tr><td>5775</td><td>168f</td><td>ᚏ</td></tr>
<tr><td>5776</td><td>1690</td><td>ᚐ</td></tr>
<tr><td>5777</td><td>1691</td><td>ᚑ</td></tr>
<tr><td>5778</td><td>1692</td><td>ᚒ</td></tr>
<tr><td>5779</td><td>1693</td><td>ᚓ</td></tr>
<tr><td>5780</td><td>1694</td><td>ᚔ</td></tr>
<tr><td>5781</td><td>1695</td><td>ᚕ</td></tr>
<tr><td>5782</td><td>1696</td><td>ᚖ</td></tr>
<tr><td>5783</td><td>1697</td><td>ᚗ</td></tr>
<tr><td>5784</td><td>1698</td><td>ᚘ</td></tr>
<tr><td>5785</td><td>1699</td><td>ᚙ</td></tr>
<tr><td>5786</td><td>169a</td><td>ᚚ</td></tr>
<tr><td>5787</td><td>169b</td><td>᚛</td></tr>
<tr><td>5788</td><td>169c</td><td>᚜</td></tr>
<tr><td>5789</td><td>169d</td><td>᚝</td></tr>
<tr><td>5790</td><td>169e</td><td>᚞</td></tr>
<tr><td>5791</td><td>169f</td><td>᚟</td></tr>
<tr><td>5792</td><td>16a0</td><td>ᚠ</td></tr>
<tr><td>5793</td><td>16a1</td><td>ᚡ</td></tr>
<tr><td>5794</td><td>16a2</td><td>ᚢ</td></tr>
<tr><td>5795</td><td>16a3</td><td>ᚣ</td></tr>
<tr><td>5796</td><td>16a4</td><td>ᚤ</td></tr>
<tr><td>5797</td><td>16a5</td><td>ᚥ</td></tr>
<tr><td>5798</td><td>16a6</td><td>ᚦ</td></tr>
<tr><td>5799</td><td>16a7</td><td>ᚧ</td></tr>
<tr><td>5800</td><td>16a8</td><td>ᚨ</td></tr>
<tr><td>5801</td><td>16a9</td><td>ᚩ</td></tr>
<tr><td>5802</td><td>16aa</td><td>ᚪ</td></tr>
<tr><td>5803</td><td>16ab</td><td>ᚫ</td></tr>
<tr><td>5804</td><td>16ac</td><td>ᚬ</td></tr>
<tr><td>5805</td><td>16ad</td><td>ᚭ</td></tr>
<tr><td>5806</td><td>16ae</td><td>ᚮ</td></tr>
<tr><td>5807</td><td>16af</td><td>ᚯ</td></tr>
<tr><td>5808</td><td>16b0</td><td>ᚰ</td></tr>
<tr><td>5809</td><td>16b1</td><td>ᚱ</td></tr>
<tr><td>5810</td><td>16b2</td><td>ᚲ</td></tr>
<tr><td>5811</td><td>16b3</td><td>ᚳ</td></tr>
<tr><td>5812</td><td>16b4</td><td>ᚴ</td></tr>
<tr><td>5813</td><td>16b5</td><td>ᚵ</td></tr>
<tr><td>5814</td><td>16b6</td><td>ᚶ</td></tr>
<tr><td>5815</td><td>16b7</td><td>ᚷ</td></tr>
<tr><td>5816</td><td>16b8</td><td>ᚸ</td></tr>
<tr><td>5817</td><td>16b9</td><td>ᚹ</td></tr>
<tr><td>5818</td><td>16ba</td><td>ᚺ</td></tr>
<tr><td>5819</td><td>16bb</td><td>ᚻ</td></tr>
<tr><td>5820</td><td>16bc</td><td>ᚼ</td></tr>
<tr><td>5821</td><td>16bd</td><td>ᚽ</td></tr>
<tr><td>5822</td><td>16be</td><td>ᚾ</td></tr>
<tr><td>5823</td><td>16bf</td><td>ᚿ</td></tr>
<tr><td>5824</td><td>16c0</td><td>ᛀ</td></tr>
<tr><td>5825</td><td>16c1</td><td>ᛁ</td></tr>
<tr><td>5826</td><td>16c2</td><td>ᛂ</td></tr>
<tr><td>5827</td><td>16c3</td><td>ᛃ</td></tr>
<tr><td>5828</td><td>16c4</td><td>ᛄ</td></tr>
<tr><td>5829</td><td>16c5</td><td>ᛅ</td></tr>
<tr><td>5830</td><td>16c6</td><td>ᛆ</td></tr>
<tr><td>5831</td><td>16c7</td><td>ᛇ</td></tr>
<tr><td>5832</td><td>16c8</td><td>ᛈ</td></tr>
<tr><td>5833</td><td>16c9</td><td>ᛉ</td></tr>
<tr><td>5834</td><td>16ca</td><td>ᛊ</td></tr>
<tr><td>5835</td><td>16cb</td><td>ᛋ</td></tr>
<tr><td>5836</td><td>16cc</td><td>ᛌ</td></tr>
<tr><td>5837</td><td>16cd</td><td>ᛍ</td></tr>
<tr><td>5838</td><td>16ce</td><td>ᛎ</td></tr>
<tr><td>5839</td><td>16cf</td><td>ᛏ</td></tr>
<tr><td>5840</td><td>16d0</td><td>ᛐ</td></tr>
<tr><td>5841</td><td>16d1</td><td>ᛑ</td></tr>
<tr><td>5842</td><td>16d2</td><td>ᛒ</td></tr>
<tr><td>5843</td><td>16d3</td><td>ᛓ</td></tr>
<tr><td>5844</td><td>16d4</td><td>ᛔ</td></tr>
<tr><td>5845</td><td>16d5</td><td>ᛕ</td></tr>
<tr><td>5846</td><td>16d6</td><td>ᛖ</td></tr>
<tr><td>5847</td><td>16d7</td><td>ᛗ</td></tr>
<tr><td>5848</td><td>16d8</td><td>ᛘ</td></tr>
<tr><td>5849</td><td>16d9</td><td>ᛙ</td></tr>
<tr><td>5850</td><td>16da</td><td>ᛚ</td></tr>
<tr><td>5851</td><td>16db</td><td>ᛛ</td></tr>
<tr><td>5852</td><td>16dc</td><td>ᛜ</td></tr>
<tr><td>5853</td><td>16dd</td><td>ᛝ</td></tr>
<tr><td>5854</td><td>16de</td><td>ᛞ</td></tr>
<tr><td>5855</td><td>16df</td><td>ᛟ</td></tr>
<tr><td>5856</td><td>16e0</td><td>ᛠ</td></tr>
<tr><td>5857</td><td>16e1</td><td>ᛡ</td></tr>
<tr><td>5858</td><td>16e2</td><td>ᛢ</td></tr>
<tr><td>5859</td><td>16e3</td><td>ᛣ</td></tr>
<tr><td>5860</td><td>16e4</td><td>ᛤ</td></tr>
<tr><td>5861</td><td>16e5</td><td>ᛥ</td></tr>
<tr><td>5862</td><td>16e6</td><td>ᛦ</td></tr>
<tr><td>5863</td><td>16e7</td><td>ᛧ</td></tr>
<tr><td>5864</td><td>16e8</td><td>ᛨ</td></tr>
<tr><td>5865</td><td>16e9</td><td>ᛩ</td></tr>
<tr><td>5866</td><td>16ea</td><td>ᛪ</td></tr>
<tr><td>5867</td><td>16eb</td><td>᛫</td></tr>
<tr><td>5868</td><td>16ec</td><td>᛬</td></tr>
<tr><td>5869</td><td>16ed</td><td>᛭</td></tr>
<tr><td>5870</td><td>16ee</td><td>ᛮ</td></tr>
<tr><td>5871</td><td>16ef</td><td>ᛯ</td></tr>
<tr><td>5872</td><td>16f0</td><td>ᛰ</td></tr>
<tr><td>5873</td><td>16f1</td><td>ᛱ</td></tr>
<tr><td>5874</td><td>16f2</td><td>ᛲ</td></tr>
<tr><td>5875</td><td>16f3</td><td>ᛳ</td></tr>
<tr><td>5876</td><td>16f4</td><td>ᛴ</td></tr>
<tr><td>5877</td><td>16f5</td><td>ᛵ</td></tr>
<tr><td>5878</td><td>16f6</td><td>ᛶ</td></tr>
<tr><td>5879</td><td>16f7</td><td>ᛷ</td></tr>
<tr><td>5880</td><td>16f8</td><td>ᛸ</td></tr>
<tr><td>5881</td><td>16f9</td><td>᛹</td></tr>
<tr><td>5882</td><td>16fa</td><td>᛺</td></tr>
<tr><td>5883</td><td>16fb</td><td>᛻</td></tr>
<tr><td>5884</td><td>16fc</td><td>᛼</td></tr>
<tr><td>5885</td><td>16fd</td><td>᛽</td></tr>
<tr><td>5886</td><td>16fe</td><td>᛾</td></tr>
<tr><td>5887</td><td>16ff</td><td>᛿</td></tr>
</table>
</body>
</html>
| 50.780303 | 51 | 0.538565 |
0ccb7361200b302e98746fb913273e875a9c713b | 593 | py | Python | 2019/06-hsctf/web-networked/solve.py | wani-hackase/wani-writeup | dd4ad0607d2f2193ad94c1ce65359294aa591681 | [
"MIT"
] | 25 | 2019-03-06T11:55:56.000Z | 2021-05-21T22:07:14.000Z | 2019/06-hsctf/web-networked/solve.py | wani-hackase/wani-writeup | dd4ad0607d2f2193ad94c1ce65359294aa591681 | [
"MIT"
] | 1 | 2020-06-25T07:27:15.000Z | 2020-06-25T07:27:15.000Z | 2019/06-hsctf/web-networked/solve.py | wani-hackase/wani-writeup | dd4ad0607d2f2193ad94c1ce65359294aa591681 | [
"MIT"
] | 1 | 2019-02-14T00:42:28.000Z | 2019-02-14T00:42:28.000Z | import requests
text = "0123456789abcdefghijklmnopqrstuvwxyz_}"
flag = "hsctf{"
for _ in range(30):
time = [0.1 for _ in range(38)]
for _ in range(5):
for i in range(38):
payload = {"password": flag + text[i]}
r = requests.post(
"https://networked-password.web.chal.hsctf.com", data=payload
)
response_time = r.elapsed.total_seconds()
time[i] += response_time
print(payload, " response time : ", response_time)
flag += text[time.index(max(time))]
print("flag is ", flag)
| 21.962963 | 77 | 0.563238 |
685141c17355095b33d1f7d8be59cfced1083b11 | 10,335 | lua | Lua | examples/example-22.lua | remyroez/DxLua | ac3dcdae6c3c10035a5cf9cdcfebc9e71020b396 | [
"MIT"
] | 2 | 2020-04-08T17:35:04.000Z | 2020-04-14T13:11:55.000Z | examples/example-22.lua | remyroez/DxLua | ac3dcdae6c3c10035a5cf9cdcfebc9e71020b396 | [
"MIT"
] | 1 | 2020-04-11T09:30:51.000Z | 2020-04-11T09:30:51.000Z | examples/example-22.lua | remyroez/DxLua | ac3dcdae6c3c10035a5cf9cdcfebc9e71020b396 | [
"MIT"
] | null | null | null | -- ワイプ１～５
local band = bit.band
local Key = 0
GraphHandle1, GraphHandle2 = GraphHandle1 or -1, GraphHandle2 or -1
-- DxLua: 各ワイプを一つのソースにまとめるためステートマシンの構築
-- 初期ステート
local InitialState = 'Enter'
-- カラーコード
local Yellow = dx.GetColor(0xFF, 0xFF, 0)
-- 共通の更新関数
local UpdateFn = function (self, parent, ...)
if self[self.State] then
self[self.State](self, parent, ...)
else
parent.State = InitialState
end
end
-- 共通の待機関数
local WaitFn = function (self, parent, dt)
dx.DrawString(0, 0, '\n左クリックで再実行\n右クリックで戻る')
dx.ScreenFlip()
local mouse = dx.GetMouseInput()
if band(mouse, dx.MOUSE_INPUT_LEFT) ~= 0 then
self.State = InitialState
elseif band(mouse, dx.MOUSE_INPUT_RIGHT) ~= 0 then
parent.State = InitialState
end
end
-- 各ワイプのステートマシン
local Wipe1 = { State = InitialState, Update = UpdateFn, Wait = WaitFn }
local Wipe2 = { State = InitialState, Update = UpdateFn, Wait = WaitFn }
local Wipe3 = { State = InitialState, Update = UpdateFn, Wait = WaitFn }
local Wipe4 = { State = InitialState, Update = UpdateFn, Wait = WaitFn }
local Wipe5 = { State = InitialState, Update = UpdateFn, Wait = WaitFn }
-- メインとなるステートマシン
local StateMachine = {
State = InitialState,
Wipe1 = Wipe1,
Wipe2 = Wipe2,
Wipe3 = Wipe3,
Wipe4 = Wipe4,
Wipe5 = Wipe5,
}
-- 画面モードのセット
dx.SetGraphMode(640, 480, 16)
-- ＤＸライブラリ初期化処理
function dx.Init()
-- グラフィックのロード
GraphHandle1 = dx.LoadGraph("Scene1.jpg")
GraphHandle2 = dx.LoadGraph("Scene2.jpg")
-- 描画先を裏画面にします
dx.SetDrawScreen(dx.DX_SCREEN_BACK)
dx.ChangeFontType(dx.DX_FONTTYPE_EDGE)
end
-- ループ
function dx.Update(dt)
StateMachine:Update(dt)
end
-- 開始
function StateMachine:Enter(parent, dt)
dx.ClearDrawScreen()
dx.DrawString(0, 0, 'ワイプ選択', Yellow)
dx.DrawString(0, 0, '\n１～５を入力してください')
dx.ScreenFlip()
self.State = 'Select'
end
-- ステートマシン更新
function StateMachine:Update(...)
-- 現在のステート名のメンバのタイプを取得
local mode = type(self[self.State])
if mode == 'function' then
-- 関数なら実行
self[self.State](self, ...)
elseif mode == 'table' then
-- テーブルなら更新関数があれば実行
if self[self.State].Update then
self[self.State]:Update(self, ...)
end
end
end
-- ワイプ選択
function StateMachine:Select(dt)
-- 現在のステート
local before = self.State
-- キーボードでワイプ選択
if dx.CheckHitKey(dx.KEY_INPUT_1) ~= 0 or dx.CheckHitKey(dx.KEY_INPUT_NUMPAD1) ~= 0 then
self.State = 'Wipe1'
elseif dx.CheckHitKey(dx.KEY_INPUT_2) ~= 0 or dx.CheckHitKey(dx.KEY_INPUT_NUMPAD2) ~= 0 then
self.State = 'Wipe2'
elseif dx.CheckHitKey(dx.KEY_INPUT_3) ~= 0 or dx.CheckHitKey(dx.KEY_INPUT_NUMPAD3) ~= 0 then
self.State = 'Wipe3'
elseif dx.CheckHitKey(dx.KEY_INPUT_4) ~= 0 or dx.CheckHitKey(dx.KEY_INPUT_NUMPAD4) ~= 0 then
self.State = 'Wipe4'
elseif dx.CheckHitKey(dx.KEY_INPUT_5) ~= 0 or dx.CheckHitKey(dx.KEY_INPUT_NUMPAD5) ~= 0 then
self.State = 'Wipe5'
end
-- ステートが変更されたら、子ステートのステートを初期化
if self.State ~= before then
if type(self[self.State]) == 'table' then
self[self.State].State = InitialState
end
end
end
-- 開始
function Wipe1:Enter(parent, dt)
self.count = 17
self.State = 'Wipe'
GraphHandle1, GraphHandle2 = GraphHandle2, GraphHandle1
end
-- ワイプ実行
function Wipe1:Wipe(parent, dt)
-- 画面を初期化
dx.ClearDrawScreen()
-- グラフィック１を描画します
dx.DrawGraph(0, 0, GraphHandle1, false)
-- グラフィック２を描画します
for j = 0, math.floor(480 / 16) do
-- 描画範囲を指定します
dx.SetDrawArea(0, j * 16, 640, j * 16 + self.count)
-- グラフィック２を描画します
dx.DrawGraph(0, 0, GraphHandle2, false)
end
dx.SetDrawArea(0, 0, 640, 480)
-- DxLua: 現在のワイプ
dx.DrawString(0, 0, 'ワイプ１', Yellow)
-- 裏画面の内容を表画面に反映させます
dx.ScreenFlip()
-- 時間待ち
dx.WaitTimer(15)
self.count = self.count - 1
if self.count < 0 then
self.State = 'Wait'
end
end
-- 開始
function Wipe2:Enter(parent, dt)
self.count = 80
self.Mode = self.Mode and (self.Mode == 0 and 1 or 0) or 0
self.State = 'Wipe'
GraphHandle1, GraphHandle2 = GraphHandle2, GraphHandle1
end
-- ワイプ実行
function Wipe2:Wipe(parent, dt)
local i = self.count
-- 画面を初期化
dx.ClearDrawScreen()
-- グラフィック１を描画します
dx.DrawGraph(0, 0, GraphHandle1, false)
-- グラフィック２を描画します
for j = 0, math.floor(640 / 16) do
-- 描画可能領域設定用の値セット
local k = j + i - 40
if k > 0 then
if k > 16 then k = 16 end
-- 描画可能領域を指定します
if self.Mode == 0 then
dx.SetDrawArea(624 - j * 16, 0, 624 - (j * 16 - k), 480)
else
dx.SetDrawArea(j * 16, 0, j * 16 - k, 480)
end
-- グラフィック２を描画します
dx.DrawGraph(0, 0, GraphHandle2, false)
end
end
-- 描画可能領域を元に戻します
dx.SetDrawArea(0, 0, 640, 480)
-- DxLua: 現在のワイプ
dx.DrawString(0, 0, 'ワイプ２', Yellow)
-- 裏画面の内容を表画面に反映させます
dx.ScreenFlip()
-- 時間待ち
dx.WaitTimer(32)
self.count = self.count - 1
if self.count < 0 then
self.State = 'Wait'
end
end
-- 開始
function Wipe3:Enter(parent, dt)
self.count = 640 + 256
self.Mode = self.Mode and (self.Mode == 0 and 1 or 0) or 0
self.State = 'Wipe'
GraphHandle1, GraphHandle2 = GraphHandle2, GraphHandle1
end
-- ワイプ実行
function Wipe3:Wipe(parent, dt)
local i = 640 + 256 - self.count
-- 画面を初期化
dx.ClearDrawScreen()
-- グラフィック１を描画します
dx.DrawGraph(0, 0, GraphHandle1, false)
-- グラフィック２を描画します
for j = 0, 256 do
-- 描画可能領域設定用の値セット
local k = j + i - 256
-- 描画可能領域を指定します
if k >= 0 then
if self.Mode == 0 then
dx.SetDrawArea(k, 0, k + 1, 480)
else
dx.SetDrawArea(640 - k, 0, 640 - (k + 1), 480)
end
-- アルファブレンド値をセット
dx.SetDrawBlendMode(dx.DX_BLENDMODE_ALPHA, 255 - j)
-- グラフィック２を描画します
dx.DrawGraph(0, 0, GraphHandle2, false)
end
end
-- ブレンドモードを元に戻す
dx.SetDrawBlendMode(dx.DX_BLENDMODE_NOBLEND, 0)
-- グラフィック２のアルファブレンド描画以外の部分の描画
do
-- 描画領域設定用の値をセット
local k = i - 256
if k > 0 then
if self.Mode == 0 then
dx.SetDrawArea(0, 0, k, 480)
else
dx.SetDrawArea(640 - k, 0, 640, 480)
end
dx.DrawGraph(0, 0, GraphHandle2, false)
end
-- 描画領域を元に戻す
dx.SetDrawArea(0, 0, 640, 480)
end
-- DxLua: 現在のワイプ
dx.DrawString(0, 0, 'ワイプ３', Yellow)
-- 裏画面の内容を表画面に反映させます
dx.ScreenFlip()
self.count = self.count - 8
if self.count < 0 then
self.State = 'Wait'
end
end
-- 開始
function Wipe4:Enter(parent, dt)
if self.Mode == 1 then
GraphHandle1, GraphHandle2 = GraphHandle2, GraphHandle1
end
self.count = 400
self.Mode = self.Mode and (self.Mode == 0 and 1 or 0) or 0
self.State = 'Wipe'
end
-- ワイプ実行
function Wipe4:Wipe(parent, dt)
local i = 400 - self.count
-- 画面を初期化
dx.ClearDrawScreen()
-- グラフィック１を描画します
dx.DrawGraph(0, 0, GraphHandle1, false)
-- 描画したグラフィックの上に反転円を描きます
DrawReversalCircle(320, 240, self.Mode == 0 and i or 399 - i, 0)
-- DxLua: 現在のワイプ
dx.DrawString(0, 0, 'ワイプ４', Yellow)
-- 裏画面の内容を表画面に反映させます
dx.ScreenFlip()
self.count = self.count - 8
if self.count < 0 then
self.State = 'Wait'
end
end
-- 開始
function Wipe5:Enter(parent, dt)
self.count = -160
self.Mode = self.Mode and (self.Mode == 0 and 1 or 0) or 0
self.State = 'Wipe'
GraphHandle1, GraphHandle2 = GraphHandle2, GraphHandle1
end
-- ワイプ実行
function Wipe5:Wipe(parent, dt)
local i = self.count
-- 画面を初期化
dx.ClearDrawScreen()
-- グラフィック１を描画します
dx.DrawGraph(0, 0, GraphHandle1, false)
-- 描画したグラフィックの上に円を描きます
dx.DrawCircle(320, 240, i + 100, 0);
-- その上からグラフィック２描きます
if 0 < i then
-- 直後に描いた円の中に描画可能領域をセット
dx.SetDrawArea(320 - i, 240 - i, 320 + i, 240 + i);
-- グラフィック２を描画
dx.DrawGraph(0, 0, GraphHandle2, false);
-- 反転円を描画
DrawReversalCircle(320, 240, i, 0);
-- 描画可能領域を元に戻す
dx.SetDrawArea(0, 0, 640, 480);
end
-- DxLua: 現在のワイプ
dx.DrawString(0, 0, 'ワイプ５', Yellow)
-- 裏画面の内容を表画面に反映させます
dx.ScreenFlip()
self.count = self.count + 4
if self.count > 500 then
self.State = 'Wait'
end
end
-- 反転円の描画
function DrawReversalCircle(x, y, r, Color)
-- 円反転描画領域の外側を描画
dx.DrawBox(0, 0, 640, y - r, Color, true)
dx.DrawBox(0, y - r, x - r, 480, Color, true)
dx.DrawBox(x - r, y + r + 1, 640, 480, Color, true)
dx.DrawBox(x + r, y - r, 640, y + r + 1, Color, true)
-- 描画処理
do
local Dx, Dy, F, j
local x1, x2, y1
-- 初期値セット
Dx = r
Dy = 0
F = -2 * r + 3
j = 0
-- 描画開始
do
-- まず最初に座標データを進める
if (F >= 0) then
x2 = Dy + x
x1 = -Dy + x
y1 = Dx + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
x2 = Dy + x
x1 = -Dy + x
y1 = -Dx + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
Dx = Dx - 1
F = F - 4 * Dx
end
Dy = Dy + 1
F = F + 4 * Dy + 2
-- 描き切るまでループ
while Dx >= Dy do
-- ラインを描く
x2 = Dx + x
x1 = -Dx + x
y1 = Dy + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
x2 = Dx + x
x1 = -Dx + x
y1 = -Dy + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
-- 座標データを進める
if F >= 0 then
x2 = Dy + x
x1 = -Dy + x
y1 = Dx + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
x2 = Dy + x
x1 = -Dy + x
y1 = -Dx + y
dx.DrawLine(0, y1, x1, y1, Color)
dx.DrawLine(x2, y1, 640, y1, Color)
Dx = Dx - 1
F = F - 4 * Dx
end
Dy = Dy + 1
F = F + (4 * Dy + 2)
end
end
end
-- 終了
return 0
end
| 22.51634 | 96 | 0.565651 |
cb68a4ee671e0dcf711b5b97bb8e1b47f787756a | 2,746 | html | HTML | data/CRAN/CNVassocData.html | JuliaTagBot/OSS.jl | 985ed664e484bbcc59b009968e71f2eccaaf4cd4 | [
"Zlib"
] | null | null | null | data/CRAN/CNVassocData.html | JuliaTagBot/OSS.jl | 985ed664e484bbcc59b009968e71f2eccaaf4cd4 | [
"Zlib"
] | null | null | null | data/CRAN/CNVassocData.html | JuliaTagBot/OSS.jl | 985ed664e484bbcc59b009968e71f2eccaaf4cd4 | [
"Zlib"
] | 3 | 2019-05-18T18:47:05.000Z | 2020-02-08T16:36:58.000Z | <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<title>CRAN - Package CNVassocData</title>
<link rel="stylesheet" type="text/css" href="../../CRAN_web.css" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<style type="text/css">
table td { vertical-align: top; }
</style>
</head>
<body>
<h2>CNVassocData: Example data sets for association analysis of CNV data</h2>
<p>This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.</p>
<table summary="Package CNVassocData summary">
<tr>
<td>Version:</td>
<td>1.0</td>
</tr>
<tr>
<td>Depends:</td>
<td>R (≥ 2.15.0)</td>
</tr>
<tr>
<td>Published:</td>
<td>2013-08-16</td>
</tr>
<tr>
<td>Author:</td>
<td>Juan R González, Isaac Subirana</td>
</tr>
<tr>
<td>Maintainer:</td>
<td>Juan R González <jrgonzalez at creal.cat></td>
</tr>
<tr>
<td>License:</td>
<td><a href="../../licenses/GPL-2">GPL-2</a> | <a href="../../licenses/GPL-3">GPL-3</a> [expanded from: GPL (≥ 2)]</td>
</tr>
<tr>
<td>URL:</td>
<td><a href="http://www.creal.cat/jrgonzalez/software.htm">http://www.creal.cat/jrgonzalez/software.htm</a></td>
</tr>
<tr>
<td>NeedsCompilation:</td>
<td>no</td>
</tr>
<tr>
<td>CRAN checks:</td>
<td><a href="../../checks/check_results_CNVassocData.html">CNVassocData results</a></td>
</tr>
</table>
<h4>Downloads:</h4>
<table summary="Package CNVassocData downloads">
<tr>
<td> Reference manual: </td>
<td> <a href="CNVassocData.pdf"> CNVassocData.pdf </a> </td>
</tr>
<tr>
<td> Package source: </td>
<td> <a href="../../../src/contrib/CNVassocData_1.0.tar.gz"> CNVassocData_1.0.tar.gz </a> </td>
</tr>
<tr>
<td> Windows binaries: </td>
<td> r-devel: <a href="../../../bin/windows/contrib/3.6/CNVassocData_1.0.zip">CNVassocData_1.0.zip</a>, r-release: <a href="../../../bin/windows/contrib/3.5/CNVassocData_1.0.zip">CNVassocData_1.0.zip</a>, r-oldrel: <a href="../../../bin/windows/contrib/3.4/CNVassocData_1.0.zip">CNVassocData_1.0.zip</a> </td>
</tr>
<tr>
<td> OS X binaries: </td>
<td> r-release: <a href="../../../bin/macosx/el-capitan/contrib/3.5/CNVassocData_1.0.tgz">CNVassocData_1.0.tgz</a>, r-oldrel: <a href="../../../bin/macosx/el-capitan/contrib/3.4/CNVassocData_1.0.tgz">CNVassocData_1.0.tgz</a> </td>
</tr>
</table>
<h4>Linking:</h4>
<p>Please use the canonical form
<a href="https://CRAN.R-project.org/package=CNVassocData"><samp>https://CRAN.R-project.org/package=CNVassocData</samp></a>
to link to this page.</p>
</body>
</html>
| 35.662338 | 309 | 0.653314 |
b2e71e54f3ede13551ca6c960041e280c9f907b3 | 761 | py | Python | htdocs/geojson/hsearch.py | akrherz/depbackend | d43053319227a3aaaf7553c823e8e2e748fbe95d | [
"Apache-2.0"
] | null | null | null | htdocs/geojson/hsearch.py | akrherz/depbackend | d43053319227a3aaaf7553c823e8e2e748fbe95d | [
"Apache-2.0"
] | 1 | 2022-02-17T17:43:52.000Z | 2022-02-17T17:43:52.000Z | htdocs/geojson/hsearch.py | akrherz/depbackend | d43053319227a3aaaf7553c823e8e2e748fbe95d | [
"Apache-2.0"
] | 2 | 2021-11-28T11:41:32.000Z | 2022-01-26T17:12:03.000Z | """search for HUC12 by name."""
import json
from paste.request import parse_formvars
from pyiem.util import get_dbconn
def search(q):
"""Search for q"""
pgconn = get_dbconn("idep")
cursor = pgconn.cursor()
d = dict(results=[])
cursor.execute(
"""SELECT huc_12, hu_12_name from huc12
WHERE hu_12_name ~* %s and scenario = 0 LIMIT 10""",
(q,),
)
for row in cursor:
d["results"].append(dict(huc_12=row[0], name=row[1]))
return d
def application(environ, start_response):
"""DO Something"""
form = parse_formvars(environ)
q = form.get("q", "")
headers = [("Content-type", "application/json")]
start_response("200 OK", headers)
return [json.dumps(search(q)).encode("ascii")]
| 23.78125 | 61 | 0.622865 |
5f9c6515c255fb981c594d054ccad4bedc66a80a | 2,853 | c | C | filesystems/unixfs/minixfs/itree_common.c | bispawel/macfuse | 9f5773372ec7e9a8ab35c865eda7180a95edcdab | [
"AML"
] | 7 | 2017-11-25T18:56:43.000Z | 2020-10-22T21:17:33.000Z | filesystems/unixfs/minixfs/itree_common.c | bispawel/macfuse | 9f5773372ec7e9a8ab35c865eda7180a95edcdab | [
"AML"
] | null | null | null | filesystems/unixfs/minixfs/itree_common.c | bispawel/macfuse | 9f5773372ec7e9a8ab35c865eda7180a95edcdab | [
"AML"
] | 1 | 2022-02-12T11:31:50.000Z | 2022-02-12T11:31:50.000Z | /* Generic part */
typedef struct {
block_t* p;
block_t key;
struct buffer_head* bh;
} Indirect;
static inline void add_chain(Indirect* p, struct buffer_head* bh, block_t* v)
{
p->key = *(p->p = v);
p->bh = bh;
}
static inline int verify_chain(Indirect* from, Indirect* to)
{
while (from <= to && from->key == *from->p)
from++;
return (from > to);
}
static inline block_t* block_end(struct buffer_head *bh)
{
return (block_t*)((char*)bh->b_data + bh->b_size);
}
static inline Indirect*
get_branch(struct inode* inode, int depth, int* offsets, Indirect chain[DEPTH],
int* err)
{
struct super_block* sb = inode->I_sb;
Indirect* p = chain;
struct buffer_head *bh;
*err = 0;
/* i_data is not going away, no lock needed */
add_chain (chain, NULL, i_data(inode) + *offsets);
if (!p->key)
goto no_block;
while (--depth) {
bh = malloc(sizeof(struct buffer_head));
if (sb_bread_intobh(sb, block_to_cpu(p->key), bh) != 0)
goto failure;
if (!verify_chain(chain, p))
goto changed;
add_chain(++p, bh, (block_t *)bh->b_data + *++offsets);
if (!p->key)
goto no_block;
}
return NULL;
changed:
brelse(bh);
*err = -EAGAIN;
goto no_block;
failure:
*err = -EIO;
no_block:
return p;
}
static inline int get_block(struct inode* inode, sector_t iblock, off_t* result)
{
*result = (off_t)0;
int err = -EIO;
int offsets[DEPTH];
Indirect chain[DEPTH];
Indirect* partial;
int depth = block_to_path(inode, iblock, offsets);
if (depth == 0)
goto out;
/* reread: */
partial = get_branch(inode, depth, offsets, chain, &err);
/* simplest case - block found, no allocation needed */
if (!partial) {
*result = (off_t)(block_to_cpu(chain[depth-1].key));
/* clean up and exit */
partial = chain + depth - 1; /* the whole chain */
goto cleanup;
}
/* Next simple case - plain lookup or failed read of indirect block */
cleanup:
while (partial > chain) {
brelse(partial->bh);
partial--;
}
out:
return err;
}
static inline int all_zeroes(block_t* p, block_t* q)
{
while (p < q)
if (*p++)
return 0;
return 1;
}
static inline unsigned nblocks(loff_t size, struct super_block* sb)
{
int k = sb->s_blocksize_bits - 10;
unsigned blocks, res, direct = DIRECT, i = DEPTH;
blocks = (size + sb->s_blocksize - 1) >> (BLOCK_SIZE_BITS + k);
res = blocks;
while (--i && blocks > direct) {
blocks -= direct;
blocks += sb->s_blocksize/sizeof(block_t) - 1;
blocks /= sb->s_blocksize/sizeof(block_t);
res += blocks;
direct = 1;
}
return res;
}
| 22.642857 | 80 | 0.57238 |
6b65458b9be913a7bf52765da49e2e880374b56a | 1,790 | asm | Assembly | programs/oeis/001/A001045.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 22 | 2018-02-06T19:19:31.000Z | 2022-01-17T21:53:31.000Z | programs/oeis/001/A001045.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 41 | 2021-02-22T19:00:34.000Z | 2021-08-28T10:47:47.000Z | programs/oeis/001/A001045.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 5 | 2021-02-24T21:14:16.000Z | 2021-08-09T19:48:05.000Z | ; A001045: Jacobsthal sequence (or Jacobsthal numbers): a(n) = a(n-1) + 2*a(n-2), with a(0) = 0, a(1) = 1; also a(n) = nearest integer to 2^n/3.
; 0,1,1,3,5,11,21,43,85,171,341,683,1365,2731,5461,10923,21845,43691,87381,174763,349525,699051,1398101,2796203,5592405,11184811,22369621,44739243,89478485,178956971,357913941,715827883,1431655765,2863311531,5726623061,11453246123,22906492245,45812984491,91625968981,183251937963,366503875925,733007751851,1466015503701,2932031007403,5864062014805,11728124029611,23456248059221,46912496118443,93824992236885,187649984473771,375299968947541,750599937895083,1501199875790165,3002399751580331,6004799503160661,12009599006321323,24019198012642645,48038396025285291,96076792050570581,192153584101141163,384307168202282325,768614336404564651,1537228672809129301,3074457345618258603,6148914691236517205,12297829382473034411,24595658764946068821,49191317529892137643,98382635059784275285,196765270119568550571,393530540239137101141,787061080478274202283,1574122160956548404565,3148244321913096809131,6296488643826193618261,12592977287652387236523,25185954575304774473045,50371909150609548946091,100743818301219097892181,201487636602438195784363,402975273204876391568725,805950546409752783137451,1611901092819505566274901,3223802185639011132549803,6447604371278022265099605,12895208742556044530199211,25790417485112089060398421,51580834970224178120796843,103161669940448356241593685,206323339880896712483187371,412646679761793424966374741,825293359523586849932749483,1650586719047173699865498965,3301173438094347399730997931,6602346876188694799461995861,13204693752377389598923991723,26409387504754779197847983445,52818775009509558395695966891,105637550019019116791391933781,211275100038038233582783867563
mov $1,2
pow $1,$0
add $1,1
div $1,3
mov $0,$1
| 198.888889 | 1,596 | 0.898324 |
cc26a69f4bdcd259a01c9d88077ea7c837917bff | 655 | kt | Kotlin | koverage/src/main/java/net/sarazan/koverage/testers/ConstructorTester.kt | asarazan/koverage | 4dfcf24afecd97c3d88322170adf050dbbf91aee | [
"Apache-2.0"
] | 9 | 2018-01-23T21:35:50.000Z | 2020-06-11T20:23:12.000Z | koverage/src/main/java/net/sarazan/koverage/testers/ConstructorTester.kt | asarazan/koverage | 4dfcf24afecd97c3d88322170adf050dbbf91aee | [
"Apache-2.0"
] | 16 | 2017-12-19T20:54:41.000Z | 2021-08-10T18:23:26.000Z | koverage/src/main/java/net/sarazan/koverage/testers/ConstructorTester.kt | asarazan/koverage | 4dfcf24afecd97c3d88322170adf050dbbf91aee | [
"Apache-2.0"
] | 2 | 2018-05-03T23:12:14.000Z | 2020-11-24T02:42:19.000Z | package net.sarazan.koverage.testers
import net.sarazan.koverage.Coverable
import net.sarazan.koverage.util.invokeDefault
import kotlin.reflect.KClass
import kotlin.reflect.full.primaryConstructor
import kotlin.reflect.jvm.isAccessible
/**
* Created by Aaron Sarazan on 12/21/17
*/
@Suppress("UNCHECKED_CAST")
object ConstructorTester : Coverable {
override fun <T : Any> cover(klass: KClass<T>) {
if (klass.java.isEnum) return
if (klass.isCompanion) return
if (klass.objectInstance != null) return
klass.primaryConstructor?.let {
it.isAccessible = true
it.invokeDefault()
}
}
} | 27.291667 | 52 | 0.699237 |
b9746fdc759e6b4b7627f1b285320c56c6617ccd | 124 | sql | SQL | migrations/sqls/20200221235141-AddEx10IndicesAndTable/sqlite-down.sql | rachit2501/sql-fundamentals | cb4bb1bcc6288e9d96b229e7961235773626b033 | [
"BSD-3-Clause"
] | null | null | null | migrations/sqls/20200221235141-AddEx10IndicesAndTable/sqlite-down.sql | rachit2501/sql-fundamentals | cb4bb1bcc6288e9d96b229e7961235773626b033 | [
"BSD-3-Clause"
] | null | null | null | migrations/sqls/20200221235141-AddEx10IndicesAndTable/sqlite-down.sql | rachit2501/sql-fundamentals | cb4bb1bcc6288e9d96b229e7961235773626b033 | [
"BSD-3-Clause"
] | null | null | null | -- Put your SQLite "down" migration here
DROP UNIQUE INDEX orderdetailuniqueproduct;
DROP TABLE CustomerOrderTransaction;
| 20.666667 | 43 | 0.822581 |
b2e911b19926607cd6e241f7b09f34ddcf0231cd | 2,556 | py | Python | fastinference.py | wkcw/VariousDiscriminator-CycleGan | de9c033aeed1c429f37c531056c1f74cb51a771c | [
"MIT"
] | null | null | null | fastinference.py | wkcw/VariousDiscriminator-CycleGan | de9c033aeed1c429f37c531056c1f74cb51a771c | [
"MIT"
] | null | null | null | fastinference.py | wkcw/VariousDiscriminator-CycleGan | de9c033aeed1c429f37c531056c1f74cb51a771c | [
"MIT"
] | null | null | null | """
A fast version of the original inference.
Constructing one graph to infer all the samples.
Originaly one graph for each sample.
"""
import tensorflow as tf
import os
from model import CycleGAN
import utils
import scipy.misc
import numpy as np
try:
from os import scandir
except ImportError:
# Python 2 polyfill module
from scandir import scandir
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('model', '', 'model path (.pb)')
tf.flags.DEFINE_string('input', 'data/apple', 'input image path')
tf.flags.DEFINE_string('output', 'samples/apple', 'output image path')
tf.flags.DEFINE_integer('image_size', 128, 'image size, default: 128')
def data_reader(input_dir):
file_paths = []
for img_file in scandir(input_dir):
if img_file.name.endswith('.jpg') and img_file.is_file():
file_paths.append(img_file.path)
return file_paths
def inference():
graph = tf.Graph()
with graph.as_default():
with tf.gfile.FastGFile(FLAGS.model, 'rb') as model_file:
graph_def = tf.GraphDef()
graph_def.ParseFromString(model_file.read())
input_image = tf.placeholder(tf.float32,shape=[FLAGS.image_size, FLAGS.image_size, 3])
[output_image] = tf.import_graph_def(graph_def,
input_map={'input_image': input_image},
return_elements=['output_image:0'],
name='output')
#print type(output_image), output_image
file_list = data_reader(FLAGS.input)
whole = len(file_list)
cnt = 0
with tf.Session(graph=graph) as sess:
for file in file_list:
tmp_image = scipy.misc.imread(file)
tmp_image = scipy.misc.imresize(tmp_image, (FLAGS.image_size, FLAGS.image_size, 3))
processed_image = tmp_image / 127.5 - 1
processed_image = np.asarray(processed_image, dtype=np.float32)
predicted_image = sess.run(output_image, feed_dict={input_image: processed_image})
predicted_image = np.squeeze(predicted_image)
#print tmp_image.shape, predicted_image.shape
save_image = np.concatenate((tmp_image, predicted_image), axis=1)
print cnt
output_file_name = file.split('/')[-1]
try:
os.makedirs(FLAGS.output)
except os.error, e:
pass
scipy.misc.imsave(FLAGS.output + '/{}'.format(output_file_name), save_image)
cnt += 1
if cnt//whole > 0.05:
print cnt//whole, 'done'
def main(unused_argv):
inference()
if __name__ == '__main__':
tf.app.run()
| 31.555556 | 91 | 0.658842 |
621b5c327b65ff5367f9ac87e21bef065ecb429c | 566 | kt | Kotlin | app/src/main/java/openfoodfacts/github/scrachx/openfood/category/network/CategoryResponse.kt | machiav3lli/openfoodfacts-androidapp | bbd9fed80455b434eb79674a251c740160731ea8 | [
"Apache-2.0"
] | 651 | 2017-02-10T02:30:01.000Z | 2022-03-31T19:11:05.000Z | app/src/main/java/openfoodfacts/github/scrachx/openfood/category/network/CategoryResponse.kt | machiav3lli/openfoodfacts-androidapp | bbd9fed80455b434eb79674a251c740160731ea8 | [
"Apache-2.0"
] | 3,760 | 2017-02-09T15:20:36.000Z | 2022-03-31T22:06:34.000Z | app/src/main/java/openfoodfacts/github/scrachx/openfood/category/network/CategoryResponse.kt | machiav3lli/openfoodfacts-androidapp | bbd9fed80455b434eb79674a251c740160731ea8 | [
"Apache-2.0"
] | 548 | 2017-03-13T22:23:22.000Z | 2022-03-24T09:26:02.000Z | package openfoodfacts.github.scrachx.openfood.category.network
import com.fasterxml.jackson.annotation.JsonIgnoreProperties
/**
* Class for response received from CategoryNetworkService class
*/
data class CategoryResponse @JvmOverloads constructor(
val count: Int = 0,
val tags: List<Tag> = emptyList()
) {
@JsonIgnoreProperties(ignoreUnknown = true)
data class Tag @JvmOverloads constructor(
val id: String = "",
val name: String = "",
val url: String = "",
val products: Int = 0
)
} | 28.3 | 64 | 0.657244 |
d0f323e1ee7b6c07d53cce9aea517a5cf67f8569 | 2,038 | kt | Kotlin | app/src/main/java/com/tsl/androidbase/fragment/first/FirstFragment.kt | Jowney23/AndroidBaseProject | 278cfbc6a02ee9602016b3304e8a7b285bb48987 | [
"Apache-2.0"
] | 1 | 2021-06-22T02:34:13.000Z | 2021-06-22T02:34:13.000Z | app/src/main/java/com/tsl/androidbase/fragment/first/FirstFragment.kt | Jowney23/AndroidBaseProject | 278cfbc6a02ee9602016b3304e8a7b285bb48987 | [
"Apache-2.0"
] | null | null | null | app/src/main/java/com/tsl/androidbase/fragment/first/FirstFragment.kt | Jowney23/AndroidBaseProject | 278cfbc6a02ee9602016b3304e8a7b285bb48987 | [
"Apache-2.0"
] | null | null | null | package com.tsl.androidbase.fragment.first
import android.content.Context
import android.os.Bundle
import android.util.Log
import android.view.LayoutInflater
import android.view.View
import android.view.ViewGroup
import android.widget.Button
import androidx.fragment.app.Fragment
import androidx.lifecycle.ViewModelProvider
import androidx.navigation.Navigation
import com.jowney.common.util.logger.L
import com.tsl.androidbase.R
import kotlinx.android.synthetic.main.fragment_first.*
import org.greenrobot.eventbus.EventBus
import org.greenrobot.eventbus.Subscribe
import org.greenrobot.eventbus.ThreadMode
import java.util.concurrent.Executors
class FirstFragment : Fragment() {
companion object {
var TAG = "JFragment"
}
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
EventBus.getDefault().register(this)
}
override fun onCreateView(
inflater: LayoutInflater, container: ViewGroup?,
savedInstanceState: Bundle?
): View? {
var view = inflater.inflate(R.layout.fragment_first, container, false)
view.findViewById<Button>(R.id.id_first_ft_bt1).setOnClickListener {
Navigation.findNavController(it).navigate(R.id.action_firstFragment_to_secondFragment)
}
view.findViewById<Button>(R.id.id_first_ft_bt2).setOnClickListener {
EventBus.getDefault().post("123")
}
return view
}
override fun onResume() {
super.onResume()
Executors.newSingleThreadExecutor().submit{
L.v("123 开始发送")
EventBus.getDefault().post("123")
EventBus.getDefault().post("123")
EventBus.getDefault().post("123")
EventBus.getDefault().post("123")
EventBus.getDefault().post("123")
EventBus.getDefault().post("123")
L.v("123 发送完了")
}
}
@Subscribe(threadMode = ThreadMode.ASYNC)
fun test(i: String) {
Thread.sleep(3000)
L.v(i + "执行")
}
}
| 29.536232 | 98 | 0.687929 |
fbec8503ce6d9971463e241885b62a687b178f00 | 2,843 | asm | Assembly | Lab05/Task04.asm | PrabalChowdhury/CSE-341-MICROPROCESSOR | 88f0dea914890c5aa5bc10d0131233b2ebd27586 | [
"MIT"
] | null | null | null | Lab05/Task04.asm | PrabalChowdhury/CSE-341-MICROPROCESSOR | 88f0dea914890c5aa5bc10d0131233b2ebd27586 | [
"MIT"
] | null | null | null | Lab05/Task04.asm | PrabalChowdhury/CSE-341-MICROPROCESSOR | 88f0dea914890c5aa5bc10d0131233b2ebd27586 | [
"MIT"
] | null | null | null | .MODEL SMalL
.STACK 100H
.DATA
X DB "ENTER A HEX DIGIT: $"
Y DB "IN DECIMAL IT IS 1$"
Z DB "IN DECIMAL IT IS $"
P DB "DO YOU WANT TO DO IT AGAIN? :$"
Q DB "ILLEGAL CHARACTER, $"
R DB "INSERT AGAIN: $"
.CODE
MAIN PROC
MOV AX,@DATA
MOV DS,AX
SRT:
lea dx,X
mov ah,9
int 21H
mov ah,1
int 21H
mov cl,al
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
cmp cl,41H
je AB
cmp cl,42H
je AB
cmp cl,43H
je AB
cmp cl,44H
je AB
cmp cl,45H
je AB
cmp cl,46H
je AB
cmp cl,30H
je AD
cmp cl,31H
je AD
cmp cl,32H
je AD
cmp cl,33H
je AD
cmp cl,34H
je AD
cmp cl,35H
je AD
cmp cl,36H
je AD
cmp cl,37H
je AD
cmp cl,38H
je AD
cmp cl,39H
je AD
JMP AC
AB:
sub cl,11H
lea DX, Y
mov ah,9
int 21H
mov dl, cl
mov ah,2
int 21H
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
lea DX,P
mov ah,9
int 21H
mov ah,1
int 21H
mov cl,al
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
cmp cl,59H
je SRT
cmp cl,79H
je SRT
cmp cl,4EH
je EXT
cmp cl,6EH
je EXT
AD:
lea DX,Z
mov ah,9
int 21H
mov dl, cl
mov ah,2
int 21H
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
lea DX,P
mov ah,9
int 21H
mov ah,1
int 21H
mov cl,al
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
cmp cl,59H
je SRT
cmp cl,79H
je SRT
cmp cl,4Eh
je EXT
cmp cl,6Eh
je EXT
AC:
lea DX,Q
mov ah,9
int 21H
JMP SRT2
SRT2:
lea DX,R
mov ah,9
int 21H
mov ah,1
int 21H
mov cl,al
mov ah,2
mov dl,0DH
int 21H
mov dl,0ah
int 21H
cmp cl,41H
je AB
cmp cl,42H
je AB
cmp cl,43H
je AB
cmp cl,44H
je AB
cmp cl,45H
je AB
cmp cl,46H
je AB
cmp cl,30H
je AD
cmp cl,31H
je AD
cmp cl,32H
je AD
cmp cl,33H
je AD
cmp cl,34H
je AD
cmp cl,35H
je AD
cmp cl,36H
je AD
cmp cl,37H
je AD
cmp cl,38H
je AD
cmp cl,39H
je AD
JMP AC
EXT:
MOV AX,4C00H
INT 21H
MAIN ENDP
END MAIN | 14.286432 | 38 | 0.408371 |
3970eca533564d5be68a1def6d47268b0c34e9c5 | 67 | html | HTML | help/404.html | ChoicescriptIDE/ChoiceScriptIDE | 08b35b06a76314b8a56fdd4f3a5eb8ebddccd9ae | [
"MIT"
] | 7 | 2017-04-10T05:27:32.000Z | 2021-08-18T05:45:47.000Z | help/404.html | ChoicescriptIDE/ChoiceScriptIDE | 08b35b06a76314b8a56fdd4f3a5eb8ebddccd9ae | [
"MIT"
] | 5 | 2020-02-24T20:24:35.000Z | 2022-02-26T01:19:35.000Z | help/404.html | ChoicescriptIDE/ChoiceScriptIDE | 08b35b06a76314b8a56fdd4f3a5eb8ebddccd9ae | [
"MIT"
] | 2 | 2018-09-12T23:27:15.000Z | 2020-01-03T14:58:06.000Z | <h1>Ever so sorry...</h1>
<h2>That page doesn't seem to exist!</h2> | 33.5 | 41 | 0.656716 |
50fa7b672b34598dcb789d53adabdbd1a315c667 | 555 | go | Go | Array.go | fizyomatik/learning-go | fb45fd40aa9e4c3d3d9633901a9e74c2419821b9 | [
"Apache-2.0"
] | null | null | null | Array.go | fizyomatik/learning-go | fb45fd40aa9e4c3d3d9633901a9e74c2419821b9 | [
"Apache-2.0"
] | null | null | null | Array.go | fizyomatik/learning-go | fb45fd40aa9e4c3d3d9633901a9e74c2419821b9 | [
"Apache-2.0"
] | null | null | null | package main
import (
"fmt"
"os"
"strconv"
)
const (
EUR = iota
GBP
JPY
)
func main() {
rates := [...]float64{
EUR: 0.88,
GBP: 0.78,
JPY: 113.02,
}
args := os.Args
if len(args) != 2 {
fmt.Println("Please provide the amount to be converted.")
return
}
n, err := strconv.Atoi(args[1])
if err != nil {
fmt.Println("Give me a number")
return
}
fmt.Printf("%d USD is %g EUR\n", n, float64(n)*rates[EUR])
fmt.Printf("%d USD is %g GBY\n", n, float64(n)*rates[GBP])
fmt.Printf("%d USD is %g JPY\n", n, float64(n)*rates[JPY])
}
| 15 | 59 | 0.587387 |
bef158ef7d03e60171d536d7cecd0d2b3a07ffe3 | 744 | asm | Assembly | oeis/143/A143056.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 11 | 2021-08-22T19:44:55.000Z | 2022-03-20T16:47:57.000Z | oeis/143/A143056.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 9 | 2021-08-29T13:15:54.000Z | 2022-03-09T19:52:31.000Z | oeis/143/A143056.asm | neoneye/loda-programs | 84790877f8e6c2e821b183d2e334d612045d29c0 | [
"Apache-2.0"
] | 3 | 2021-08-22T20:56:47.000Z | 2021-09-29T06:26:12.000Z | ; A143056: a(n) = Re(b(n)) where b(n)=(1+i)*b(n-1)+b(n-2), with b(1)=0, b(2)=1.
; Submitted by Christian Krause
; 0,1,1,1,0,-3,-9,-19,-32,-43,-39,5,128,377,783,1305,1728,1513,-367,-5495,-15744,-32267,-53177,-69371,-58464,21693,235305,656909,1328896,2165489,2781855,2249009,-1161856,-10052911,-27385695,-54696687,-88125696,-111427091,-86075113,58797853,428575584,1140728485,2249936377,3583923733,4457814912,3275028585,-2867726673,-18235006903,-47477885888,-92495347015,-145652953551,-178115128423,-123761676928,136277900261,774439010919,1974516504117,3800238884640,5915321847181,7107171675209,4639349077021
mov $2,1
lpb $0
sub $0,1
add $1,$3
sub $3,$4
mov $4,$2
mov $2,$3
add $5,$4
mov $3,$5
add $4,$1
add $5,$2
lpe
mov $0,$5
| 41.333333 | 493 | 0.71371 |
39feaa202c08b787d7321d1012e8ca249c9b9217 | 454 | java | Java | Slithice-Origin/src/jqian/sootex/dependency/pdg/CtrlDependenceEdge.java | coder-chenzhi/Slithice | a91fb7df2839f6f473311341c832599b23879a06 | [
"MIT"
] | null | null | null | Slithice-Origin/src/jqian/sootex/dependency/pdg/CtrlDependenceEdge.java | coder-chenzhi/Slithice | a91fb7df2839f6f473311341c832599b23879a06 | [
"MIT"
] | null | null | null | Slithice-Origin/src/jqian/sootex/dependency/pdg/CtrlDependenceEdge.java | coder-chenzhi/Slithice | a91fb7df2839f6f473311341c832599b23879a06 | [
"MIT"
] | null | null | null | package jqian.sootex.dependency.pdg;
/**
* A control dependence edge.
*/
public class CtrlDependenceEdge extends DependenceEdge {
public CtrlDependenceEdge(DependenceNode from,DependenceNode to){
super(from,to);
}
public boolean equals(Object that){
if(that.getClass()!=this.getClass())
return false;
return super.equals(that);
}
public int hashCode(){
return super.hashCode();
}
}
| 20.636364 | 69 | 0.64978 |
0b319f1e0f623f7a373575a0813d2b01691f2dd6 | 1,514 | kt | Kotlin | src/commonMain/kotlin/com/github/dwursteisen/minigdx/PlatformContext.kt | dwursteisen/mini-gdx | 4120365d3856500884df805aaf5fb8845c58b4c3 | [
"MIT"
] | 11 | 2020-02-20T13:20:58.000Z | 2021-03-24T00:30:56.000Z | src/commonMain/kotlin/com/github/dwursteisen/minigdx/PlatformContext.kt | dwursteisen/mini-gdx | 4120365d3856500884df805aaf5fb8845c58b4c3 | [
"MIT"
] | 1 | 2020-07-03T22:51:48.000Z | 2020-07-03T22:51:48.000Z | src/commonMain/kotlin/com/github/dwursteisen/minigdx/PlatformContext.kt | dwursteisen/mini-gdx | 4120365d3856500884df805aaf5fb8845c58b4c3 | [
"MIT"
] | 1 | 2021-03-08T20:25:43.000Z | 2021-03-08T20:25:43.000Z | package com.github.dwursteisen.minigdx
import com.github.dwursteisen.minigdx.file.FileHandler
import com.github.dwursteisen.minigdx.game.Game
import com.github.dwursteisen.minigdx.graphics.ViewportStrategy
import com.github.dwursteisen.minigdx.input.InputHandler
import com.github.dwursteisen.minigdx.logger.Logger
interface PlatformContext {
/**
* Configuration used to create the game and the platform.
*/
val configuration: GameConfiguration
/**
* Create the GL object, used for communicating with the GL Driver.
*/
fun createGL(): GL
/**
* Create the File Handler: the entry point to access file on disk, ...
*/
fun createFileHandler(logger: Logger, gameContext: GameContext): FileHandler
/**
* Create the Input Handler: it's the entry point to manage game inputs (keyboard, touch, mouse)
*/
fun createInputHandler(logger: Logger, gameContext: GameContext): InputHandler
/**
* Create the viewport Strategy responsible to get the displayed grahical area.
*/
fun createViewportStrategy(logger: Logger): ViewportStrategy
/**
* Create the logger
*/
fun createLogger(): Logger
/**
* Create the game options
*/
fun createOptions(): Options
/**
* Start the game using the platform specific creation code.
* The game will be created by [gameFactory] using the [gameContext]
* created in by the platform.
*/
fun start(gameFactory: (GameContext) -> Game)
}
| 29.686275 | 100 | 0.694848 |
f22cd3bacbeacc69185b83c02e0e43653439e203 | 3,660 | swift | Swift | HCCardView/Other/ViewController.swift | HC1058503505/HCCardView | b342c19038422d1948fceaf99f64f243f5649cfd | [
"MIT"
] | 1 | 2017-07-19T02:23:52.000Z | 2017-07-19T02:23:52.000Z | HCCardView/Other/ViewController.swift | HC1058503505/HCCardView | b342c19038422d1948fceaf99f64f243f5649cfd | [
"MIT"
] | null | null | null | HCCardView/Other/ViewController.swift | HC1058503505/HCCardView | b342c19038422d1948fceaf99f64f243f5649cfd | [
"MIT"
] | null | null | null | //
// ViewController.swift
// HCCardView
//
// Created by UltraPower on 2017/5/23.
// Copyright © 2017年 UltraPower. All rights reserved.
//
import UIKit
import Foundation
class ViewController: UIViewController {
override func viewDidLoad() {
super.viewDidLoad()
self.title = "HCCardView"
automaticallyAdjustsScrollViewInsets = false
let titles:[String] = ["全部","视频","图片","段子","互动区","相册","网红","投票","美女"];
let childVCs:[UIViewController] = { () -> [UIViewController] in
var childM:[UIViewController] = [UIViewController]()
let allVC = HCContentViewController(type: HCContentViewControllerType.All)
let videoVC = HCContentViewController(type: HCContentViewControllerType.Video)
let pictureVC = HCContentViewController(type: HCContentViewControllerType.Picture)
let jokeVC = HCContentViewController(type: HCContentViewControllerType.Joke)
let interactionVC = HCContentViewController(type: HCContentViewControllerType.Interaction)
let ablumVC = HCContentViewController(type: HCContentViewControllerType.Album)
let netpopularVC = HCContentViewController(type: HCContentViewControllerType.NetPopular)
let voteVC = HCContentViewController(type: HCContentViewControllerType.Vote)
let beautyVC = HCContentViewController(type: HCContentViewControllerType.Beauty)
childM.append(contentsOf: [allVC,videoVC,pictureVC,jokeVC,interactionVC,ablumVC,netpopularVC,voteVC,beautyVC])
return childM
}()
var cardViewStyle:HCCardViewStyle = HCCardViewStyle()
cardViewStyle.headerViewBGColor = UIColor.orange // 标题栏背景色
cardViewStyle.headerViewHeight = 44; // 标题栏高度
cardViewStyle.itemNormalFontSize = 15; // 标题文字Normal模式文字大小
cardViewStyle.itemSelectedFontSize = 18; // 标题文字Selected模式文字大小
cardViewStyle.itemNormalColor = UIColor.white // 标题文字Normal模式文字颜色
cardViewStyle.itemSelectedColor = UIColor.red // 标题文字Selected模式文字颜色
cardViewStyle.isGradient = true // 标题文字切换时文字颜色是否渐变
// cardViewStyle.isShowLineView = false // 是否展示下滑view
cardViewStyle.lineViewColor = UIColor.green // 下滑view背景色
// cardViewStyle.isShowCoverView = false // 是否item背景view
cardViewStyle.coverViewColor = UIColor.white // item背景view背景色
let cardV: HCCardView = HCCardView(frame: CGRect(x: 0, y: 64, width: view.bounds.width, height: view.bounds.height - 64), titles: titles, childVCs: childVCs, parentVC: self, cardViewStyle: cardViewStyle)
view.addSubview(cardV)
// HCNetWorking.request("http://s.budejie.com/topic/list/zuixin/1/bs0315-iphone-4.5.6/0-20.json", method: .GET, success: { (result) in
// do {
// let resultDic = try JSONSerialization.jsonObject(with: result, options: .allowFragments) as! [String:Any]
// print(resultDic["list"] ?? "nil")
// } catch {
// }
//
// }) { (error) in
// print(error)
// }
// HCNetWorking.request("http://s.budejie.com/topic/list/zuixin/1/bs0315-iphone-4.5.6/0-20.json")
// .responseJSON { (res) in
// print(res)
// }
// .responseString { (res) in
// print(res)
// }
}
override func didReceiveMemoryWarning() {
super.didReceiveMemoryWarning()
// Dispose of any resources that can be recreated.
}
}
| 46.329114 | 211 | 0.635519 |
3e60242f085b7979e725509458435d6d1ac79d2c | 7,008 | h | C | OpenGL4/uniformbuffer.h | freegraphics/cpputils | 3f990d4765a50c7428bd6a4929a83ff49d3567bd | [
"MIT"
] | null | null | null | OpenGL4/uniformbuffer.h | freegraphics/cpputils | 3f990d4765a50c7428bd6a4929a83ff49d3567bd | [
"MIT"
] | null | null | null | OpenGL4/uniformbuffer.h | freegraphics/cpputils | 3f990d4765a50c7428bd6a4929a83ff49d3567bd | [
"MIT"
] | null | null | null | #pragma once
#include "utils.h"
#include <utils/strconv.h>
#include "glprogram.h"
namespace detail
{
template<class _DataStruct>
interface IUniformBlockLayout
{
virtual void get_names(std::vector<const GLchar*>& _names) const = 0;
virtual void get_ptrs(const _DataStruct& _data,std::vector<const void*>& _ptrs) const = 0;
virtual void get_sizes(const _DataStruct& _data,std::vector<size_t>& _sizes) const = 0;
};
};//namespace detail
template<class _DataStruct>
struct UniformBlockLayoutBase : public detail::IUniformBlockLayout<_DataStruct>
{
protected:
interface IItemInfo
{
virtual ~IItemInfo() {}
virtual const GLchar* get_name() const = 0;
virtual const void* get_pointer(const _DataStruct& _data) const = 0;
virtual size_t get_size(const _DataStruct& _data) const = 0;
};
template<typename _VarType>
struct ItemInfo : public IItemInfo
{
std::auto_ptr<GLchar> m_name;
_VarType _DataStruct::* m_member_ptr;
ItemInfo(const CString& _name,_VarType _DataStruct::* _member_ptr)
:m_member_ptr(_member_ptr)
{
string_converter<TCHAR,GLchar> name(_name,CP_ACP);
m_name = std::auto_ptr<GLchar>(new GLchar[name.get_length()+1]);
size_t i = 0;
for(i=0;i<(size_t)name.get_length();i++)
{
m_name.get()[i] = ((GLchar*)name)[i];
}
m_name.get()[i] = 0;
}
virtual const GLchar* get_name() const
{
return m_name.get();
}
virtual const void* get_pointer(const _DataStruct& _data) const
{
return Private::to_pointer(_data.*m_member_ptr);
}
virtual size_t get_size(const _DataStruct& _data) const
{
return Private::get_size(_data.*m_member_ptr);
}
};
protected:
ptrvector<IItemInfo> items;
public:
UniformBlockLayoutBase()
{
}
template<typename _VarType>
void add(const CString& _name,_VarType _DataStruct::* _member_ptr)
{
items.push_back(new ItemInfo<_VarType>(_name,_member_ptr));
}
virtual void get_names(std::vector<const GLchar*>& _names) const
{
ptrvector<IItemInfo>::const_iterator
it = items.begin()
,ite = items.end()
;
for(;it!=ite;++it)
{
const IItemInfo* ii = *it;
_names.push_back(ii->get_name());
}
}
virtual void get_ptrs(const _DataStruct& _data,std::vector<const void*>& _ptrs) const
{
ptrvector<IItemInfo>::const_iterator
it = items.begin()
,ite = items.end()
;
for(;it!=ite;++it)
{
const IItemInfo* ii = *it;
_ptrs.push_back(ii->get_pointer(_data));
}
}
virtual void get_sizes(const _DataStruct& _data,std::vector<size_t>& _sizes) const
{
ptrvector<IItemInfo>::const_iterator
it = items.begin()
,ite = items.end()
;
for(;it!=ite;++it)
{
const IItemInfo* ii = *it;
_sizes.push_back(ii->get_size(_data));
}
}
};// template<> struct UniformBlockLayoutBase
namespace Private
{
template<typename _UniformBlockStruct> inline
const detail::IUniformBlockLayout<_UniformBlockStruct>& get_uniform_block_layout()
{
typedef typename _UniformBlockStruct::UniformBlockLayout DataUniformBlockLayout;
static DataUniformBlockLayout _;
return _;
}
template<class _UniformBlockStruct> inline
bool save_data_to_buffer(
const _UniformBlockStruct& _data
,std::vector<byte>& _buf
,const UniformBlockIndex& _block_index
)
{
VERIFY_EXIT1(!_block_index.is_error_index(),false);
const detail::IUniformBlockLayout<_UniformBlockStruct>& layout = get_uniform_block_layout<_UniformBlockStruct>();
std::vector<const GLchar*> names;
std::vector<const void*> ptrs;
std::vector<size_t> sizes;
layout.get_names(names);
layout.get_ptrs(_data,ptrs);
layout.get_sizes(_data,sizes);
return save_data_to_buffer(names,ptrs,sizes,_buf,_block_index);
}
inline
bool save_data_to_buffer(
const std::vector<const GLchar*>& _names
,const std::vector<const void*>& _ptrs
,const std::vector<size_t>& _sizes
,std::vector<byte>& _buf
,const UniformBlockIndex& _block_index
)
{
VERIFY_EXIT1(!_block_index.is_error_index(),false);
_buf.resize(_block_index.size());
std::fill(_buf.begin(),_buf.end(),0);
VERIFY_EXIT1(_names.size()==_ptrs.size(),false);
VERIFY_EXIT1(_names.size()==_sizes.size(),false);
std::vector<GLuint> indices;
std::vector<GLint> size;
std::vector<GLint> offset;
std::vector<GLint> type;
indices.resize(_names.size());
size.resize(_names.size());
offset.resize(_names.size());
type.resize(_names.size());
glGetUniformIndices(_block_index.program(),_names.size(),&_names[0],&indices[0]);
GLenum err_code = GL_NO_ERROR;
if(is_error(err_code)) return false;
glGetActiveUniformsiv(_block_index.program(),_names.size(),&indices[0]
,GL_UNIFORM_OFFSET, &offset[0]
);
if(is_error(err_code)) return false;
glGetActiveUniformsiv(_block_index.program(),_names.size(),&indices[0]
,GL_UNIFORM_SIZE, &size[0]
);
if(is_error(err_code)) return false;
glGetActiveUniformsiv(_block_index.program(),_names.size(),&indices[0]
,GL_UNIFORM_TYPE, &type[0]
);
if(is_error(err_code)) return false;
size_t i = 0;
for(i=0;i<_names.size();i++)
{
size_t sz = size[i]*get_gl_type_size(type[i]);
if(sz!=_sizes[i]) return false;
if(offset[i]>=(int)_buf.size() || offset[i]+sz>=_buf.size()) return false;
memcpy(&_buf[offset[i]],_ptrs[i],sz);
}
return true;
}
};//namespace Private
struct Buffer;
struct UniformBuffer
{
protected:
GLuint m_uniform_buffer_id;
GLenum m_err_code;
size_t m_size;
public:
UniformBuffer()
:m_uniform_buffer_id(0)
,m_err_code(GL_NO_ERROR)
,m_size(0)
{
}
~UniformBuffer()
{
clear();
}
GLenum get_last_error() const {return m_err_code;}
bool create()
{
clear();
glGenBuffers(1,&m_uniform_buffer_id);
if(is_error(m_err_code)) return false;
return bind();
}
void clear()
{
if(glIsBuffer(m_uniform_buffer_id))
{
stop_using_buffer();
glDeleteBuffers(1,&m_uniform_buffer_id);
}
m_uniform_buffer_id = 0;
m_size = 0;
}
template<typename _UniformBlockStruct>
bool set_data(
IN GLProgram& _program
,IN const CString& _block_name,IN const _UniformBlockStruct& _data
,IN GLenum _usage = GL_STATIC_DRAW
)
{
if(!prepare_buffer()) return false;
std::vector<byte> buf;
UniformBlockIndex uniform_block = _program.get_uniform_block_index(_block_name);
if(uniform_block.is_error_index()) return false;
if(!Private::save_data_to_buffer(_data,buf,uniform_block))
{
if(!is_error(m_err_code))
m_err_code = GL_INVALID_VALUE;
test_error(m_err_code);
return false;
}
glBufferData(GL_UNIFORM_BUFFER,uniform_block.size(),&buf[0],_usage);
if(is_error(m_err_code)) return false;
m_size = uniform_block.size();
glBindBufferBase(GL_UNIFORM_BUFFER,uniform_block.index(),m_uniform_buffer_id);
return !is_error(m_err_code);
}
bool bind()
{
glBindBuffer(GL_UNIFORM_BUFFER,m_uniform_buffer_id);
return !is_error(m_err_code);
}
protected:
bool prepare_buffer()
{
if(!glIsBuffer(m_uniform_buffer_id))
{
clear();
return create();
}
else
{
return bind();
}
}
friend struct Buffer;
};//struct UniformBuffer
| 24.418118 | 115 | 0.714469 |
71fcb89761cbc6bcf71c32a29bf5ae3a9f403cf8 | 1,609 | tsx | TypeScript | components/card.tsx | seanbreckenridge/projects | 4ecaa85a6a299f276783a4bfe0626f77dd325393 | [
"MIT"
] | 3 | 2020-07-04T12:18:47.000Z | 2021-04-24T22:42:49.000Z | components/card.tsx | seanbreckenridge/projects | 4ecaa85a6a299f276783a4bfe0626f77dd325393 | [
"MIT"
] | 8 | 2020-11-25T08:10:33.000Z | 2021-10-11T03:47:48.000Z | components/card.tsx | seanbreckenridge/projects | 4ecaa85a6a299f276783a4bfe0626f77dd325393 | [
"MIT"
] | null | null | null | import React from "react";
import styles from "../styles/Home.module.css";
import { Repository } from "../lib/parseData";
import { IconBrandGithub } from "@tabler/icons";
import FooterIcon, { Website } from "./icons";
import LazyImage from "./lazy_image";
interface IRepo {
repo: Repository;
}
const RepoCard = React.memo(({ repo }: IRepo) => {
const remoteURL = "https://github.com/" + repo.full_name;
return (
<div className={styles.card}>
<div className={styles.cardTitle}>
<a href={remoteURL}>
<h3>{repo.name}</h3>
</a>
<span>{repo.language}</span>
</div>
<div className={styles.cardDescription}>
<div dangerouslySetInnerHTML={{ __html: repo.description }}></div>
{repo.full_name === "seanbreckenridge/projects" ? (
<div className={styles.lazyImageContainer}>
<iframe src="https://sean.fish/projects" />
</div>
) : (
<LazyImage
src={repo.img}
dimensions={repo.dimensions}
name={repo.name}
/>
)}
</div>
<hr />
<div className={styles.cardFooter}>
<FooterIcon href={remoteURL} linkText="Github">
<IconBrandGithub />
</FooterIcon>
<Website url={repo.url} />
</div>
</div>
);
});
interface IRepoGrid {
repos: Repository[];
}
const RepoGrid = React.memo(({ repos }: IRepoGrid) => {
return (
<>
{repos.map((repo: Repository) => {
return <RepoCard key={repo.full_name} repo={repo} />;
})}
</>
);
});
export default RepoGrid;
| 24.753846 | 74 | 0.563704 |
e071d95b395566ccb04ebec889234667e3c26376 | 4,901 | swift | Swift | Source/Swift/UIDocumentPickerViewController.swift | plotfi/Swift-UIKit | ac8c37a70b635e096ae9bae9e90f61a6bb355e93 | [
"BSD-3-Clause"
] | 1 | 2021-11-28T08:08:09.000Z | 2021-11-28T08:08:09.000Z | Source/Swift/UIDocumentPickerViewController.swift | plotfi/Swift-UIKit | ac8c37a70b635e096ae9bae9e90f61a6bb355e93 | [
"BSD-3-Clause"
] | null | null | null | Source/Swift/UIDocumentPickerViewController.swift | plotfi/Swift-UIKit | ac8c37a70b635e096ae9bae9e90f61a6bb355e93 | [
"BSD-3-Clause"
] | null | null | null | @_exported import Foundation
protocol UIDocumentPickerDelegate : NSObjectProtocol {
@available(iOS 11.0, *)
optional func documentPicker(_ controller: UIDocumentPickerViewController, didPickDocumentsAt urls: [URL])
@available(iOS 11.0, *)
@available(swift, obsoleted: 3, renamed: "documentPicker(_:didPickDocumentsAt:)")
optional func documentPicker(_ controller: UIDocumentPickerViewController, didPickDocumentsAtURLs urls: [URL])
@available(iOS 8.0, *)
optional func documentPickerWasCancelled(_ controller: UIDocumentPickerViewController)
@available(iOS, introduced: 8.0, deprecated: 11.0)
optional func documentPicker(_ controller: UIDocumentPickerViewController, didPickDocumentAt url: URL)
@available(swift, obsoleted: 3, renamed: "documentPicker(_:didPickDocumentAt:)")
@available(iOS, introduced: 8.0, deprecated: 11.0)
optional func documentPicker(_ controller: UIDocumentPickerViewController, didPickDocumentAtURL url: URL)
}
@available(iOS, introduced: 8.0, deprecated: 14.0, message: "Use appropriate initializers instead")
enum UIDocumentPickerMode : UInt {
init?(rawValue: UInt)
var rawValue: UInt { get }
typealias RawValue = UInt
case `import`
@available(swift, obsoleted: 3, renamed: "import")
static var Import: UIDocumentPickerMode { get }
case open
@available(swift, obsoleted: 3, renamed: "open")
static var Open: UIDocumentPickerMode { get }
case exportToService
@available(swift, obsoleted: 3, renamed: "exportToService")
static var ExportToService: UIDocumentPickerMode { get }
case moveToService
@available(swift, obsoleted: 3, renamed: "moveToService")
static var MoveToService: UIDocumentPickerMode { get }
}
@available(iOS 8.0, *)
class UIDocumentPickerViewController : UIViewController {
@available(iOS, introduced: 8.0, deprecated: 14.0)
init(documentTypes allowedUTIs: [String], in mode: UIDocumentPickerMode)
@available(swift, obsoleted: 3, renamed: "init(documentTypes:in:)")
@available(iOS, introduced: 8.0, deprecated: 14.0)
init(documentTypes allowedUTIs: [String], inMode mode: UIDocumentPickerMode)
init?(coder: NSCoder)
@available(iOS, introduced: 8.0, deprecated: 14.0)
init(url: URL, in mode: UIDocumentPickerMode)
@available(swift, obsoleted: 3, renamed: "init(url:in:)")
@available(iOS, introduced: 8.0, deprecated: 14.0)
init(URL url: URL, inMode mode: UIDocumentPickerMode)
@available(iOS, introduced: 11.0, deprecated: 14.0)
init(urls: [URL], in mode: UIDocumentPickerMode)
@available(swift, obsoleted: 3, renamed: "init(urls:in:)")
@available(iOS, introduced: 11.0, deprecated: 14.0)
init(URLs urls: [URL], inMode mode: UIDocumentPickerMode)
/// Initializes the picker for exporting local documents to an external location. The new locations will be returned using `didPickDocumentAtURLs:`.
/// @param asCopy if true, a copy will be exported to the destination, otherwise the original document will be moved to the destination. For performance reasons and to avoid copies, we recommend you set `asCopy` to false.
@available(iOS 14.0, *)
init(forExporting urls: [URL], asCopy: Bool)
/// Initializes the picker for exporting local documents to an external location. The new locations will be returned using `didPickDocumentAtURLs:`.
/// @param asCopy if true, a copy will be exported to the destination, otherwise the original document will be moved to the destination. For performance reasons and to avoid copies, we recommend you set `asCopy` to false.
@available(iOS 14.0, *)
@available(swift, obsoleted: 3, renamed: "init(forExporting:asCopy:)")
init(forExportingURLs urls: [URL], asCopy: Bool)
/// Initializes the picker for exporting local documents to an external location. The new locations will be returned using `didPickDocumentAtURLs:`. The original document will be moved to the destination.
@available(iOS 14.0, *)
convenience init(forExporting urls: [URL])
/// Initializes the picker for exporting local documents to an external location. The new locations will be returned using `didPickDocumentAtURLs:`. The original document will be moved to the destination.
@available(iOS 14.0, *)
@available(swift, obsoleted: 3, renamed: "init(forExporting:)")
convenience init(forExportingURLs urls: [URL])
weak var delegate: @sil_weak UIDocumentPickerDelegate?
@available(iOS, introduced: 8.0, deprecated: 14.0, message: "Use appropriate initializers instead")
var documentPickerMode: UIDocumentPickerMode { get }
@available(iOS 11.0, *)
var allowsMultipleSelection: Bool
/// Force the display of supported file extensions (default: NO).
@available(iOS 13.0, *)
var shouldShowFileExtensions: Bool
/// Picker will try to display this URL when presented
@available(iOS 13.0, *)
var directoryURL: URL?
convenience init(nibName nibNameOrNil: String?, bundle nibBundleOrNil: Bundle?)
convenience init()
}
| 55.067416 | 223 | 0.759233 |
b4ad397fa3ed8939d4584ce4a2df0835484b9834 | 379 | kt | Kotlin | order/src/main/kotlin/li/doerf/microstore/order/ConfigTopics.kt | doerfli/microstore | ef6d3b228ad3665631322252cf07fcc1bab17269 | [
"MIT"
] | null | null | null | order/src/main/kotlin/li/doerf/microstore/order/ConfigTopics.kt | doerfli/microstore | ef6d3b228ad3665631322252cf07fcc1bab17269 | [
"MIT"
] | null | null | null | order/src/main/kotlin/li/doerf/microstore/order/ConfigTopics.kt | doerfli/microstore | ef6d3b228ad3665631322252cf07fcc1bab17269 | [
"MIT"
] | null | null | null | package li.doerf.microstore.order
import li.doerf.microstore.TOPIC_ORDERS
import org.apache.kafka.clients.admin.NewTopic
import org.springframework.context.annotation.Bean
import org.springframework.context.annotation.Configuration
@Configuration
class ConfigTopics {
@Bean
fun topicOrders(): NewTopic {
return NewTopic(TOPIC_ORDERS, 10, 1.toShort())
}
}
| 22.294118 | 59 | 0.778364 |
3b7ffcb7face9ab82836c657366df57b2d359f88 | 3,244 | h | C | MainLoader_fpga.h | dMajoIT/loadp2 | 05842c525e7a5c3acb570f04488857ef90c32479 | [
"MIT"
] | 7 | 2019-07-27T18:03:37.000Z | 2022-03-09T17:21:56.000Z | MainLoader_fpga.h | dMajoIT/loadp2 | 05842c525e7a5c3acb570f04488857ef90c32479 | [
"MIT"
] | 4 | 2019-11-19T13:59:42.000Z | 2021-10-02T05:35:34.000Z | MainLoader_fpga.h | dMajoIT/loadp2 | 05842c525e7a5c3acb570f04488857ef90c32479 | [
"MIT"
] | 6 | 2020-07-16T18:59:39.000Z | 2022-02-22T17:13:14.000Z | unsigned char MainLoader_fpga_bin[] = {
0x00, 0x00, 0x61, 0xfd, 0x84, 0x00, 0x88, 0xfc, 0x20, 0x7e, 0x65, 0xfd,
0x24, 0x08, 0x60, 0xfd, 0x24, 0x28, 0x60, 0xfd, 0x1f, 0x02, 0x61, 0xfd,
0x08, 0x06, 0xdc, 0xfc, 0x40, 0x7e, 0x74, 0xfd, 0x01, 0x0a, 0x85, 0xf0,
0x1f, 0x04, 0x61, 0xfd, 0x18, 0x0a, 0x45, 0xf0, 0x15, 0x0a, 0x61, 0xfd,
0xf6, 0x07, 0x6d, 0xfb, 0x00, 0x00, 0x7c, 0xfc, 0x84, 0x00, 0xe8, 0xfc,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00
};
unsigned int MainLoader_fpga_bin_len = 512;
| 69.021277 | 73 | 0.652281 |
c70d4248cf700a61ff5a7353c92105be4b6572a3 | 388 | asm | Assembly | programs/oeis/174/A174316.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 22 | 2018-02-06T19:19:31.000Z | 2022-01-17T21:53:31.000Z | programs/oeis/174/A174316.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 41 | 2021-02-22T19:00:34.000Z | 2021-08-28T10:47:47.000Z | programs/oeis/174/A174316.asm | neoneye/loda | afe9559fb53ee12e3040da54bd6aa47283e0d9ec | [
"Apache-2.0"
] | 5 | 2021-02-24T21:14:16.000Z | 2021-08-09T19:48:05.000Z | ; A174316: Sequence defined by a(0)=a(1)=a(2)=1, a(3)=2, a(4)=6 and the formula a(n)=2^(n-2)+2 for n>=5.
; 1,1,1,2,6,10,18,34,66,130,258,514,1026,2050,4098,8194,16386,32770,65538,131074,262146,524290,1048578,2097154,4194306,8388610,16777218,33554434,67108866,134217730,268435458,536870914,1073741826,2147483650
trn $0,2
mov $1,$0
trn $0,2
sub $0,$1
mov $2,2
pow $2,$1
bin $0,$2
add $0,1
| 32.333333 | 205 | 0.698454 |