Press space to continue.
➜ sbt-scalafortyfives
denotes command-line shell[sbt-scalafortyfives]>
denotes sbt shellscala>
denotes Scala REPLFrom Preview of upcoming sbt 1.0 features: Read about the new plugins
extends Build
and/or uses *.sbt
filesproject
s
file
) or remote (url
)Seq[Setting]
ScopedKey
-Initialize
pairsInspired by the video SBT Introduction & Cookbook for Scala
➜ sbt-scalafortyfives cat ~/.sbt/0.13/global.sbt
shellPrompt := { state =>
s"[${(Project extract state).currentRef.project}]> "
}
➜ sbt-scalafortyfives sbt
[sbt-scalafortyfives]>
[sbt-scalafortyfives]> console
[info] Starting scala interpreter...
[info]
Welcome to Scala version 2.11.7 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45).
Type in expressions to have them evaluated.
Type :help for more information.
scala>
Uses scalaVersion
and scalacOptions
settings
scala> trait Free[F[_], A]
warning: there was one feature warning; re-run with -feature for details
defined trait Free
What can we do to know what the warning is? (Press space)
scala> :quit
[sbt-scalafortyfives]> set scalacOptions += "-feature"
[info] Defining sbt-scalafortyfives/*:scalacOptions
[info] The new value will be used by sbt-scalafortyfives/compile:scalacOptions
[info] Reapplying settings...
[info] Set current project to sbt-scalafortyfives (in build file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/)
[sbt-scalafortyfives]> console
[info] Starting scala interpreter...
[info]
Welcome to Scala version 2.11.7 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45).
Type in expressions to have them evaluated.
Type :help for more information.
scala>
(Hit space to the next slide)
scala> trait Free[F[_], A]
<console>:10: warning: higher-kinded type should be enabled
by making the implicit value scala.language.higherKinds visible.
This can be achieved by adding the import clause 'import scala.language.higherKinds'
or by setting the compiler option -language:higherKinds.
See the Scala docs for value scala.language.higherKinds for a discussion
why the feature should be explicitly enabled.
trait Free[F[_], A]
^
defined trait Free
You're ALMOST (see next slide) done!
Save the session so it remains the default in the build
[sbt-scalafortyfives]> session save
[info] Reapplying settings...
[info] Set current project to sbt-scalafortyfives (in build file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/)
Now, you're done! Really. (see next slide)
session save
saves to build.sbt
➜ sbt-scalafortyfives cat build.sbt
scalaVersion := "2.11.7"
scalacOptions += "-feature"
That finishes the trail. Well done!
[sbt-scalafortyfives]> inspect scalaVersion
[info] Setting: java.lang.String = 2.10.4
...
[sbt-scalafortyfives]> show scalaVersion
[info] 2.10.4
[sbt-scalafortyfives]> set scalaVersion := "2.11.7"
[info] Defining sbt-scalafortyfives/*:scalaVersion
[info] The new value will be used by sbt-scalafortyfives/*:allDependencies, sbt-scalafortyfives/*:dependencyUpdatesData and 18 others.
[info] Run `last` for details.
[info] Reapplying settings...
[info] Set current project to sbt-scalafortyfives (in build file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/)
[sbt-scalafortyfives]> show scalaVersion
[info] 2.11.7
[sbt-scalafortyfives]> console
[info] Starting scala interpreter...
[info]
Welcome to Scala version 2.11.7 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_45).
Type in expressions to have them evaluated.
Type :help for more information.
scala>
scalaVersion
makes testing different versions of Scala in your project so much easier.scalaVersion
.session save
.
// build.sbt
lazy val message = settingKey[String]("The message to print")
message := "Hello, folks"
// sbt shell
[sbt-scalafortyfives]> inspect message
[info] Setting: java.lang.String = Hello, folks
...
[sbt-scalafortyfives]> inspect compile
[info] Task: sbt.inc.Analysis
...
[sbt-scalafortyfives]> inspect runMain
[info] Input task: Unit
...
// build.sbt
lazy val printHello = taskKey[Unit]("Prints hello")
printHello := {
println("Hello, folks!")
}
// sbt shell
[sbt-scalafortyfives]> printHello
Hello, folks!
// build.sbt
// given printHello is defined already
compile <<= (compile in Compile) dependsOn printHello
// sbt shell
[sbt-scalafortyfives]> compile
Hello, folks!
[info] Updating {file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/}sbt-scalafortyfives...
[info] Resolving org.fusesource.jansi#jansi;1.4 ...
[info] Done updating.
// build.sbt
// given message setting is already defined
lazy val printMessage = taskKey[Unit]("Prints message")
printMessage := {
println(message.value)
}
// sbt shell
[sbt-scalafortyfives]> printMessage
Hello, folks
// build.sbt
// compile declaration returns sbt.inc.Analysis
compile in Compile := {
println("I am doing nothing")
}
// sbt shell
[sbt-scalafortyfives]> reload
[info] Loading global plugins from /Users/jacek/.sbt/0.13/plugins
/Users/jacek/dev/sandbox/sbt-scalafortyfives/build.sbt:7: error: type mismatch;
found : Unit
required: sbt.inc.Analysis
println("I am doing nothing")
^
[error] Type error in expression
Project loading failed: (r)etry, (q)uit, (l)ast, or (i)gnore?
// build.sbt
lazy val a = project
Think of how many projects are now available in the build definition? Let's ask sbt using projects
command.
[sbt-scalafortyfives]> projects
[info] In file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/
[info] a
[info] * sbt-scalafortyfives
2 projects visible, but there are THREE, actually!
There is also the meta project build under project
.
// build.sbt
lazy val a = project
lazy val b = project
// sbt shell
[sbt-scalafortyfives]> projects
[info] In file:/Users/jacek/dev/sandbox/sbt-scalafortyfives/
[info] a
[info] b
[info] * sbt-scalafortyfives
Projects declare (visibility) scopes for settings and tasks.
// build.sbt
lazy val message = settingKey[String]("Message")
lazy val a = project
lazy val b = project
message := "Default message"
message in a := "message in project A"
message in b := "message in project B"
[sbt-scalafortyfives]> show message
[info] b/*:message
[info] message in project B
[info] a/*:message
[info] message in project A
[info] sbt-scalafortyfives/*:message
[info] Default message
[sbt-scalafortyfives]> show a/message
[info] message in project A
[sbt-scalafortyfives]> show b/message
[info] message in project B
extends sbt.Plugin
projectSettings
, buildSettings
, globalSettings
The following plugins work across projects and should not really be defined in a project:
Define them in ~/.sbt/0.13/plugins/plugins.sbt
on your computer.
They are project-specific so define them in project/plugins.sbt
in your project:
requires
and trigger
methodsenablePlugins
to enable plugins that are not automatically added
enablePlugins(...)
disablePlugins
to exclude plugins that are automatically enabled.
disablePlugins(...)
extends AutoPlugin
[PluginName]Plugin
is a naming convention for an auto plugin's objectprojectSettings
val to specify project settingsrequires
method to declare dependent pluginstrigger
method to declare whether or not to activate the auto plugin when requires
is satisfied.import sbt._
import Keys._
object CommonSettingsPlugin extends AutoPlugin {
// triggered when all plugins present
override def trigger = allRequirements
// no plugins required (default)
override def requires: Plugins = empty
// settings to add to projects
override def projectSettings = Seq(
organization := "io.deepsense",
version := "0.1.0",
scalaVersion := "2.11.6",
scalacOptions := Seq(
"-unchecked", "-deprecation", "-encoding", "utf8", "-feature",
"-language:existentials", "-language:implicitConversions"))
}
addSbtPlugin("org.company.plugins" % "web-service" % "1")
- Visit Jacek Laskowski's blog
- Follow @jaceklaskowski at twitter
- Use Jacek's projects at GitHub


--- title: ODF sidebar: function_reference_sidebar permalink: ODF.ODF.html folder: function_reference toc: false ---
This class is obsolet since MTEX 5.9. Use the class SO3Fun instead. Anyway the class is preserved, so that saved @ODFs can be loaded.


publications
2020
- The Genetic Architecture of DNA Replication Timing in Human Pluripotent Stem Cells2020
- Genome-wide detection of short tandem repeat expansions by long-read sequencingBMC Bioinformatics 2020
2019
- An immune-related gene pairs signature predicts overall survival in serous ovarian carcinomaOncoTargets and Therapy 2019
- An immune-related gene pairs signature predicts overall survival in serous ovarian carcinomaOncoTargets and Therapy 2019
2018
- Gene pair based prognostic signature for colorectal colon cancer:Medicine 2018


GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
| Version: | 2.3 |
| Depends: | R (≥ 2.14.0), mgcv (≥ 1.8), plotfunctions (≥ 1.3) |
| Suggests: | knitr, xtable, sp, data.table |
| Published: | 2017-08-31 |
| Author: | Jacolien van Rij [aut, cre], Martijn Wieling [aut], R. Harald Baayen [aut], Hedderik van Rijn [ctb] |
| Maintainer: | Jacolien van Rij <vanrij.jacolien at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | itsadug citation info |
| Materials: | NEWS |
| CRAN checks: | itsadug results |
| Reference manual: | itsadug.pdf |
| Vignettes: |
ACF: checking & handling autocorrelation Visual inspection of GAMM models Testing for significance |
| Package source: | itsadug_2.3.tar.gz |
| Windows binaries: | r-devel: itsadug_2.3.zip, r-release: itsadug_2.3.zip, r-oldrel: itsadug_2.3.zip |
| OS X binaries: | r-release: itsadug_2.3.tgz, r-oldrel: itsadug_2.3.tgz |
| Old sources: | itsadug archive |
| Reverse suggests: | VWPre |
Please use the canonical form https://CRAN.R-project.org/package=itsadug to link to this page.


Ocala events
-
turkey shoot soccer tournament everett washington
Welcome new members. - Free Online Library
MICHIGAN -- Susan Sundstrom-Young NEW JERSEY -- Mercedes Mendez- Herman NEW YORK -- Denise F. Boyd, Lorraine M. Grasso, Myles Kleiman .
http://www.thefreelibrary.com/Welcome+new+members.-a01318586272011 - State of Michigan
Feb 10, 2012 . Michigan Dept. of . 07/08/2011 02:30 PM WEBER, MARLENE DENISE. IMLAY CITY, MI . Indian Lake. Iosco . STERLING HEIGHTS, MI Saint Clair River -N. Channel . 08/26/2011 10:30 AM GRABOWSKI, NICHOLAS F III .
http://www.michigan.gov/documents/dnr/2011-catch-release_376400_7.pdfHONOR ROLL OF MEMBERS - Oriental Institute
Mr. Clyde Curry Smith, Ph.D. and Mrs. Ellen Marie Smith, River Falls, . Mrs. Denise G. Paul, Chicago, Illinois . Mrs. Nancy E. Turchi, Indian River, Michigan . Dr. John J. Grabowski and Mrs. Diane L. Grabowski, Cleveland Heights, Ohio .
http://oi.uchicago.edu/pdf/04-05_Members.pdf
ocean cay jacksonville beach Upcoming events in Ocala
forest suites resort lake tahoe Hottest Tickets
-
Apr 11albuquerque balloon park hotels
Amway Center -
Apr 28auburn tigers navy youth bar tshirt
Amway Center -
Jun 25tricare eligability for children
Bob Carr Performing Arts Center -
Mar 30freestone realty northbrook il
Amway Center -
Aug 27bold fresh movie tour
Amway Center -
Jun 21bag of water flys
Amway Center -
Sep 13carolyn pray bank of america
Francis Field -
Mar 17resume and vendor management
St Augustine Amphitheatre -
Oct 8pakistan china relations from saag
Amway Center -
May 4motorcycleing in myrtle beach
Bob Carr Performing Arts Center
Ocala's best venues
-
U.S.A. Pickleball Association -- Ambassador Program
James Hackenberg<PHONE_NUMBER> C Kalamazoo MI email. Yvonne Hackenberg . River Valley Debra Patty<PHONE_NUMBER> Dover AR email. River Valley Isaac Patty . Homestead-Miramar Denise Blaine<PHONE_NUMBER> Homestead FL email . Peru James Grabowski<PHONE_NUMBER> Peru IL email. Quad-Cities .
http://www.usapa.org/ambassador/index.php?u=usapa_ambassadors.php -
Delinquent Taxpayers by Name
997 CATFISH LAKE RD EAGLE RIVER WI 54521-1890 USA, I, $9,323.60 . 1119 INDIAN BLUFF DR APOPKA FL 32703-3151 USA, I, $27,667.84. GETTER . GIANNOPOULOS, DENISE . GLOBAL CONSULTING GROUP, 3509 AUBURN RD AUBURN HILLS MI 48326-6331 USA, W, $9,774.80 . GRABOWSKI, SCOTT .
http://www.revenue.wi.gov/delqlist/nmallG.htm -
BlueNose Edutainment - Bluenose Defined - Winners
Julia Dwyer (I Don't Know Which Road To Take) Wood River Middle, Hailey, ID Mrs. Donna McKelvey. Claraliz . Luke Laufenberg Dodgeville Middle, Wisconsin Mrs. Denise Hottmann . Shannon Grabowski Muir Middle, Michigan Debbie Ryan . General Smallwood Middle, Indian Head, Maryland Elizabeth Adams .
http://www.bluenoseed.com/bluenose_defined/winners/pastwinners/2008manufactured home sales washington state
98 people's favorite
-
Denise Horner Facebook, Twitter & MySpace on PeekYou
Looking for Denise Horner ? PeekYou's people search has 98 people named Denise Horner and you can find info, photos, links, family members and more.
http://www.peekyou.com/denise_horner -
West Highland White Terrier Puppies for Sale - Breeder
American Indian Dog · American Lo-Sze . Contact: Denise Ray 300 Meyers . Strawberry River Westies Contact: Tracey .<PHONE_NUMBER>. View Full Ad for AKC IN-HOME Maltese Breeders Michigan . Contact: Pawel Grabowski Krzeptow .
http://www.breederinfocenter.com/index.php?c=West+Highland+White+Terrier


This page tests whether replacing notifications removes them from the list of notifications maintained by the browser as well. You must have granted notification permission to run this test.


{% extends "base.html" %} {% block title %}Password Changed{% endblock %} {% block header %}Password Changed{% endblock %} {% block main %}
Password just has been successfully changed.
Re-enter and check
Go back


User Experience Design Product hunt list here
317.759.4940
Send an SMS
, Some systems (Android, Symbian, webOS) don’t allow a number, then you can try Send an SMS
If you want to programmingly send SMS or invoke phone call or video streaming, you can check out Twilio They also offer helper library for Node.js app Twilio node lib


sheep text
hi
fff
sheep words
sheep letters
lehleh
blehgles
sheep things
every tenth syllable in this par agraph is underlined big and bolded
sdfsdff
text
testy test


Jim Norton's |
|
Welcome! |
- Jim Norton's Coffee Shop was started in 2019 selling snow-peas with pepper, tuna casserole, and coffee. While we have received some noise complaints, we figure if our neighbours don't like it, they can be the ones to leave!
- "The tuna casserole will raise your blood pressure more than the coffee!"
- My Doctor
- So come on down! Located in the heart of Nordburg, just one block north of the police station. (Take that you noise-complainers!) If you're lucky you'll get to meet our town super-hero!
|
|


Select features, see the price updated
Scroll down to get started
{{feature.description}}
Get your quote now FREE


{% extends "simple.html" %} {% block body %}
This test determines if resources with a future Expires header date are cached by the browser. An image, a stylesheet, and an external script were downloaded in the previous page with an Expires date 30 days in the future. This page should load almost instantly if those are cached.
{% endblock %}


Like all well-formed XML, the CQ/XML format contains one root element. The name of that element is 'ClearQuest'. The ClearQuest element is also responsible for passing login information to the ClearQuest server via its attributes. The ClearQuest element has four required and one optional attribute.
| Required Attribute | Description | Notes |
|---|---|---|
| login | the ClearQuest user login | |
| password
- or - encrypted |
the (encrypted) ClearQuest user password |
encryption keys must be syncronized with a CQ admin |
| db | the ClearQuest database to login to | valid values: pract or prodd |
| repo | the ClearQuest database repository | valid values: practice or prodr |
| Optional Attribute | Description | Notes |
| email-fail | email address to send errors to on 'queued actions' |
Although the CQ/XML Interface can prohibit actions against defects based on its permissions model, it is still possible to corrupt a large number of defects rather quickly if you are not careful. Therefore, before deploying into production you should do all development and testing against the practice database (db='pract' repo='practice'). If you corrupt data in the production database we will be unable to back out the changes you have introduced.
Since you can only have one root element, to send multiple commands create multiple defect/query/info elements within one root element or make multiple connections to the server. See also: Common Issues, Mistakes & Caveats.
Standard login:
<ClearQuest login='cq_user' password='password' db='pract' repo='practice'>
Login with encrypted password:
<ClearQuest login='cq_user' encrypted='&±¿þA' db='pract' repo='practice'>
Login with email failure notification:
<ClearQuest login='cq_user' password='password' db='pract' repo='practice'<EMAIL_ADDRESS>Now that you can login, the next chapter Defect Elements will show how to work with specific defects.
| Connecting to the CQ/XML Server | Table of Contents | Defect Elements |


WITNESS LIST Law Enforcement Committee April 7, 2003-2:30P HB 57 For: Alajo, Javier (Consulate General of Mexico in Austin, Texas) Alvarado, Crescencia (El Buen Samaritano Episcopal Mission) Anchondo, Leo (Catholic Charities Office of Immigrant Concerns; Catholic Diocese of Austin) Armendariz, Hoptencia (United Farm Workers South Texas; La Union del Pueblo Entero) Arroyo, Maria (Austin Interfaith and Texas Interfaith Network) Berra, Joseph (Self and MALDEF) Flores, Nancy (Texas Council on Family Violence) Garcia, Gus (Self and Austin City Council) Harrell, Will (American Civil Liberties Union) Hernandez, Juan Carlos (Self) Juarez, Benito (Mayor's Office of Immigrant and Refugee Affairs) Krause, Wayne (Self and Texas Civil Rights Project) Landeros, Rudy (Austin Police Department) Lopez, Lorena (Austin Interfaith and Texas Interfaith Network) Ortiz, Jose C. (Centro por los Derechos Humanos) Parsons, Paul (Self) Plascencia, Luis (Austin Commission on Immigrant Affairs) Pytel, Elsa Reyna (Self) Rodas, Jasmine (Immigrantes Latinos en Accion) Salamanca-Speer, Nidia (Self) Villareal, Armando (United Farm Workers of America; AFL-CIO) Yanez-Correa, Ana (Texas LULAC) Against: Toney, Bill (Self) On: Michalak, Theresa (Wells Fargo Bank) HB 287 For: Freeman, Gary (Justice of the Peace & Constables Assn. of Texas) HB 353 For: Coffey, Dr. EC (Self) Harrell, Will (American Civil Liberties Union; NAACP; LULAC) On: Eisenberg, Arthur (University of North Texas Health Science Center) HB 481 For: Harrell, Will (American Civil Liberties Union; NAACP; LULAC) Krause, Wayne (Self and Texas Civil Rights Project) HB 819 For: Tripp, Alice (Texas State Rifle) On: Edmonds, Shannon (TDCAA) HB 875 For: Morgan, Gary D. (Dental Laboratory Association of Texas) HB 1663 For: Love, Ed (Bexar County Criminal Investigation Laboratory) HB 1845 Against: Anchondo, Leo (Catholic Charities Office of Immigrant Concerns and Catholic Diocese of Austin) Berra, Joseph (Mexican American Legal Defense and Educational Fund) Codena, Adriana (Service Employees International Union) Fegueroa, Mayra (Houston Coalition for Amnesty and Dignity) Harrell, Will (American Civil Liberties Union) Jimenez, Maria (National Movement for Legalization and Immigrant Rights) Perez, Amelia (Cualicion de Tejas por la Dinida y al Mistia) Plascencia, Luis (Austin Commission on Immigrant Affairs) Villareal, Armando (United Farm Workers of America; AFL-CIO) Yanez-Correa, Ana (Texas LULAC) On: Elder, Frank (Texas DPS) HB 2079 For: Kessler, Ron (TX Burglar and Fire Alarm Association) HB 2237 For: Johnson, Helen (Self and State Firemen's & Fire Marhals' Association) HB 2619 For: Tripp, Alice (Texas State Rifle) HB 2773 For: Neeley, Karen (Independent Bankers Association ofTexas) HB 2829 For: Garcia, Gus (City of Austin, Texas) Harrell, Will (American Civil Liberties Union) HB 2881 For: Beukenkamp, Michael (Self) Blackburn, Audley (Guide Dog Users of Texas) Finley, Cynthia (Self and Community Relations Comm.- Lubbock; Amer. Council & South Plains Council of the Blind of Texas) Soltes, Sheri (Self and Texas Hearing and Sevice Dogs) Sparks, Dewayne (Self) Whitehurst, Shane (Self) SB 165 For: Lindsey, Gary (Sheriff Jim Bowles, Dallas County) Pope, Robert (Dallas Area Rapid Transit)


Using Node.js/Express server using Mongoose to organize models and query the database
The Angular CLI is a tool to initialize, develop, scaffold and maintain Angular applications
JSON Web Token (JWT) is a compact URL-safe means of representing claims to be transferred between two parties
Coming soon! Get Ready!


Patching
Edge Cases
Patching at runtime is very flexible. But it comes with its downsides. This section describes edge cases that need workarounds, are hard to solve or sometimes impossible.
Inlining
This Article describes the details pretty good. An inlined method is no longer a method and is not called in the normal way. As a result, Harmony cannot patch these methods and your patches will simply be non-functional.
The solution is highly dependent on your situation. If you have control over the host application, you could run it in debug mode, but that would come with a large speed penalty. Besides that, you can only resort to some clever redesign of your patch and find a spot higher up in the call chain that is not inlined. This sometimes requires mass-patching all occurances of all methods that call the inlined method and patching there (TargetMethods()
is your friend).
Calling Base Methods
When the class you want to patch overrides a method in its base class, calling the base implementation with base.SomeMethod()
does not work as you expected, when you call it from your patch code.
using HarmonyLib;
using System;
using System.Runtime.CompilerServices;
[HarmonyPatch]
class Patch
{
[HarmonyReversePatch]
[HarmonyPatch(typeof(BaseClass), nameof(BaseClass.Method))]
[MethodImpl(MethodImplOptions.NoInlining)]
static string BaseMethodDummy(SubClass instance) { return null; }
[HarmonyPatch(typeof(SubClass), nameof(SubClass.Method))]
static void Prefix(SubClass __instance)
{
var str = BaseMethodDummy(__instance);
Console.WriteLine(str);
}
}
public class BaseClass
{
public virtual string Method()
{
return "base";
}
}
public class SubClass : BaseClass
{
public override string Method()
{
return "subclass";
}
}
The reason for this is that the resolution of base.SomeMethod()
happens in your compiler. It will create IL code that targets that specific method. At runtime however, you can't simply use reflections or delegates to call it. They all will be resolved to the overwriting method. The only solution that is known to solve this is to use a Reverse Patch
, that copies the original to a stub of your own that you then can call. See this gist for an example and a comparison.
Generics
Generics can be difficult to patch. In general, expect generic methods and methods of generic classes to be shared between different types of T
during runtime. This means that by patching one method, the method will be patched for all types of T
. Depending on the type of generic and your .NET runtime, this can be worked around in a few ways:
- If the generic includes a value type, such as
int
, in most (but not all) cases, the method will not be shared. Patching a method which uses a value type parameter will patch only that specific method. Conversely, patching a generic with an object type will not patch the value type method, so both may have to be patched. - If the method is a non-generic non-static method of a generic class, you can check the generic type using
__instance
(such as__instance.GetType().DeclaringType.GenericTypeArguments
), and adjust your code's behavior depending on the type. - If the method is a generic method of a non-generic class, you may be able to examine the method's arguments, if any argument contains
T
. Also, generic type data will be lost (ifMethod<T>
is patched usingMethod<string>
,Method<object>
will becomeMethod<string>
) - If the method is a static non-generic method of a generic class, generic type data will be lost (see above).
Changing the type returned by a constructor
It seems to be easy to make a constructor return a different type. Unfortunately, C# and the intermediate bytecode (CIL) doesn’t work like that. A constructor in C# is compiled into the following IL code:
newobj instance void Test::.ctor();
And the newobj IL code is described by Microsoft as
The newobj instruction allocates a new instance of the class associated with ctor and initializes all the fields in the new instance to 0 (of the proper type) or null references as appropriate. It then calls the constructor ctor with the given arguments along with the newly created instance. After the constructor has been called, the now initialized object reference (type O) is pushed on the stack.
So a constructor is just an initialiser method that gets the newly empty obj as an argument to set the values of fields. All the "create object of type T" logic is in the IL code and the internal logic of the C# runtime. Which means you cannot change the type from within the constructor method. All you can do is to manipulate the place where the constructor is called (the operand of newobj or some extra IL after it that changes the value on the stack).
Static Constructors
Static constructors of a class will run as soon as you touch or instantiate that class. That unfortunately means that when Harmony asks for some basic required information for that class, it will trigger the static constructor before the patch happens.
As a result, you cannot patch static constructors unless you plan to run them again (which often defeats the purpose). It also has the side effect that your patches to other methods in such a class will run the constructor at the wrong moment - causing errors. In that case, you need to time the patching so it happens when it's ok to run the static constructor or when it already has been triggered.
Native (External) Methods
A method that has only an external implementation (like a native Unity method) can normally not be patched. Harmony requires access to the original IL code to build the replacement. Thus adding Prefix or Postfix to it does not work. This leaves only one possibility: using a transpiler to create your own implementation.
As a result, you can patch native methods with a transpiler-only patch that ignores the (empty) input and returns a new implementation that will replace the original. Beware: after patching, the original implemenation is lost and you cannot call it anymore, making this approach less useful.
MarshalByRefObject
Methods inheriting from MarshalByRefObject
are kind of special and patching them and information about how the .NET runtime implements the glue code between managed methods and their jitted assembler code does not exist. Thus special methods like certain types of generics and methods inheriting from MarshalByRefObject are difficult or impossible to patch.
Special Classes
Related to the problem with marshalled classes, .NET contains classes like HttpRequest that exhibit strange side effects when patching methods in them. Sometimes, its necessary to patch some methods with identity patches (no prefix, postfix or transpiler, but still patched) to make patches on other methods in the same class work. Details are sparse and it really depends on your architecture, your .NET version, the runtime environment and the class you are patching. There is no simple solution but sometimes, experimenting gives results.
Methods With Dead Code
In some environments (like Mono) the runtime poses strict rules about creating methods that should not contain dead code. This becomes problematic, when patching methods like the following results in a InvalidProgramException
:
public SomeType MyMethod()
{
throw new NotImplementedException()
}
That method has no RET
IL code in its body and if you try to patch it, Harmony will generate illegal IL. The only solution to this is to create a Transpiler
that transpiles the method to a correct version by creating valid IL. This is also true for adding a Prefix or Postfix to that method. The way Harmony works, the replacement method needs to be valid to add calls to your patches to it.
Patching too early: MissingMethodException in Unity
When patching too early, for example on the injected assemblys entry point, Unity will throw a MissingMethodException: Attempted to access a missing method
.
This situation occurs when the original method directly or indirectly calls an external
UnityEngine method.
In the following example code, patching either SomeMethod()
or SomeOtherMethod()
will cause the exception:
class SomeGameObject
{
GameObject gameObject;
void SomeMethod()
{
UnityEngine.Object.DontDestroyOnLoad(gameObject);
}
void SomeOtherMethod()
{
SomeMethod();
}
}
UnityEngine.Object.DontDestroyOnLoad()
is an external UnityEngine method:
[MethodImpl(MethodImplOptions.InternalCall)]
[GeneratedByOldBindingsGenerator]
public static extern void DontDestroyOnLoad(Object target);
To prevent this issue, make sure UnityEngine has finished its startup phase (dynamically linking external methods to actual binary) before patching such methods.
One way to do so is to execute patching only after Unity has loaded the first scene, for example by using the SceneManager.sceneLoaded
event:
public static class Patcher
{
private static bool patched = false;
public static void Main()
{
//DoPatch(); <-- Do not execute patching on assembly entry point
SceneManager.sceneLoaded += SceneLoaded;
}
private static void DoPatch()
{
var harmony = new Harmony("test");
harmony.PatchAll();
patched = true;
}
private static void SceneLoaded(UnityEngine.SceneManagement.Scene scene, UnityEngine.SceneManagement.LoadSceneMode loadSceneMode)
{
// Execute patching after unity has finished it's startup and loaded at least the first game scene
if (!patched)
DoPatch();
}
}


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/names.html ---
In the realm of computer algebra it seems that there are commonly accepted names that are used throughout mathematics which we implement. For instance, the sin, integral, subscript, coefficient, etc. In the data structures we find common names like first, last, map, rotate, etc. Sometimes these are not comon but still "reasonably obvious", such as in a DeRham Complex you find a function named "lieDerivative", which is a lie derivative.
So far, in lean and logic I haven't found "universal" naming to be the norm. Even in areas like group theory the naming convention seems non-universal. Given that some of these basic theorems exist in all of the systems, would it be a useful topic to try to converge on a "common core" naming of theorems?
An alternative might be to create a "rosetta stone" document, similar to one in computer algebra:
http://axiom-developer.org/axiom-website/rosetta.pdf
My (admit
My admitedly limited understanding is that "proof irrelevance" implies that if something is proved in one system then it is true. So such a rosetta collection turns into both a cross-reference and a catalog of proven statements.
And if a theorem is proven true in one system, couldn't it be stated as an axiom in other systems? So the rosetta stone becomes a catalog of axioms.
Of course you can add axioms... but since there will be a human translation step involved when going from one system to the other, the trust level goes down.
Concerning "common core" names. I think it is very hard to come up with a practical naming system that everyone is happy with. I for one think that it would already be very helpful if we just add those "common core" names in the comments above theorems. For most applications that should also be sufficient.
Good point. On the other hand, with a list of proofs (say, from group theory) it would be possible to claim which ones were proven in Lean and which ones are axioms.
Assume a rosetta document (perhaps one similar to the Digital Library of Mathematical Functions (DLMF) https://dlmf.nist.gov/ so that one could look up a theorem (because the "reference name" cites chapter/section/theorem. Along with the theorem from "the first prover" would be the actual proof. Theorems proven in other systems could be named in the same section. Theorems not listed are just axioms.
The "Digital Library of Mathematical Theorems" (DLMT) would be a useful collection, especially since it would show how to prove a theorem, despite syntactic / semantic mismatch. Indeed, if there was an natural language explanation of the proof it would be even more enlightening.
Such an effort would be an interesting sub-forum at conferences.
IF a system came up with a shorter or more obvious proof that replace the existing entry.
Attaching the author's name would give professional credit
William Stein (http://www.sagemath.org/) built a "front end" system that allows users of many systems to have a common way of communicating. This approach might help with the translation problem.
Of course, there is the issue of "compatible logics" since, as I understand it, a theorem proven in a classical logic is not necessarily a theorem in constructive logic (Bishop?)
If libraries were organized "by the book", it would make it a lot easier to search for theorems. All of the group theory theorems are in the chapter on group theory, for example. That would make it possible to have "library compliance" tests and cross-system proofs.
I don't understand... is the current folder hierarchy in mathlib unclear?
As to the issue of "trust" for a theorem from another system... I believe that Coq and Lean share
No... you have to trust the human translation.
a common logic. If the proof in one system were output in a common format it seems to me that "proof checking" a Lean proof in Coq or the reverse is possible
Sure, but that requires writing a lot of glue.
Folders are a really bad semantic organization. I call it a POS (Pile of Sand) organization... It all sorta-works to sorta-provide some ad hoc map to a tree structure.
Mathematics seems more graph-like than tree-like, but maybe that's just me.
How is it different from "by the book" organization with chapters? (Books are linearly ordered, which is probably even worse than POS trees.)
Books provide several mechanisms for search that everyone is familiar with. Chapters, sections, indexes, tables, hyperlinks, and surrounding text
We have all that (if you map folders to chapters etc).
Every POS organized system I've ever worked on was "obvious" after the first year or so of use.
Re: Coq <-> Lean, they don't actually have the exact same logic. I've wanted this for a long time, but I think that a wholesale conversion will not work in either direction because DTT is very sensitive to defeq
A DLMT website could provide "proof checkable" sources in a common format so systems could proof-check their axioms to increase trust. Sharing such efforts means that systems could specialize (e.g. HoTT axioms)
This exists in some other languages already
I don't think Lean is a very good interchange language for proofs coming from many systems
I believe that Lean can output a "primitive proof" (don't remember the name) that gets fed to the lowest level proof system. I believe that Coq also has that ability. I will check later today. Given a common core logic they ought to be able to cross-validate proofs.
Those primitive proofs are not in a common format
and they are not in a common logic
so you can't feed a lean proof to Coq, it will complain it is not well typed even if you fix the syntax
and vice versa
So to build Axiom as an Oracle system I have to re-prove every algorithm in every system from scratch? That hardly seems like mathematics to me.
Like I said, Lean is not an interchange format. That doesn't mean they don't exist
I don't need a common interchange for proofs, just for proof checking
OpenTheory is a reasonably successful interchange format for HOL like systems, and I have heard Dedukti is an interchange format for DTT systems, although I haven't used it. Metamath is also a generic prover that can be used to check any logic including HOL and DTT systems, and I have successfully translated to and from Metamath for this sort of thing
Excellent. I'll look up metamath. Thanks.
There is also my own language MM0, which I recently demonstrated here to translate a large proof from Metamath to Lean (https://leanprover.zulipchat.com/#narrow/stream/113488-general/topic/Prime.20number.20theorem.20in.20lean/near/168472336)
There aren't a lot of people working on proof translations, but I think it is very important for the theorem proving community to have a more rigorous sense of equivalence of logics, so that it doesn't become some battle royale between the various prover communities.
Well I've wandered off into Linear Logic (Pfenning / Balzer) for a bit and that is a somewhat incompatible logic.
not for metamath or MM0 it's not
I think Isabelle is also a logical framework, so it might work for this purpose too, I haven't tried
The idea is that you state your axioms up front, and define the system you are proving theorems in
rather than baking everything into the foundation like lean
Since I'm proving source code algorithms I'm essentially taking that path. The group theory superstructure provides axioms (proven elsewhere) that can be used in the proof of code.
Btw, book organization and POS organization are quite compatible. Axiom uses literate programming with all of the source code in book form. The compiler extracts the source code into POS form at compile time. John Kitchin (CMU/Chemical Engineering) (http://kitchingroup.cheme.cmu.edu) is a strong advocate of literate programming.
Btw, book organization and POS organization are quite compatible. Axiom uses literate programming with all of the source code in book form. The compiler extracts the source code into POS form at compile time. John Kitchin (CMU/Chemical Engineering) (http://kitchingroup.cheme.cmu.edu) is a strong advocate of literate programming.
(deleted)
Re: POS (Pile of Sand) and directory organization: Ted Nelson's Computers for Cynics "The Nightmare of Files and Directories" https://www.youtube.com/watch?v=Qfai5reVrck&list=PLTI2Kz0V2OFlgbkROVmzkfQRW2FrX2KfR&index=2
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


--- layout: post title: Stopwatch "python" date: 2014-10-22 05:17:34.000000000 +02:00 type: post parent_id: '0' published: true password: '' status: publish categories: - Linux - Python tags: - python meta: _edit_last: '49460244' geo_public: '0' publicize_facebook_url: https://facebook.com/10203695101753021 _wpas_done_3508581: '1' _publicize_done_external: a:1:{s:8:"facebook";a:1:{i:1081800551;b:1;}} _wpas_skip_3508581: '1' author: login: mpho112 email<EMAIL_ADDRESS>display_name: Mpho first_name: '' last_name: '' ---
Well as for this week, we were to create some sort of stopwatch game.
Player gets a point when, they hit "stop" button if it stops on seconds.
A bit even more trickier, but challenge was accepted and completed...Hell Yeah.
[sourcecode language="python" wraplines="false" collapse="false"]
#http://www.codeskulptor.org/#user38_TSo5mURks3_2.py
#http://www.codeskulptor.org/#user38_TSo5mURks3_3.py
#or http://www.codeskulptor.org/#user38_nJvMd8w50P_0.py
# Mini-project #3 - "Stopwatch: The Game"
# Created by Mpho "!nerd" Mphego - Marion Island SANSA Engineer 2014-2015
#
# 'Introduction to Interactive Programming in Python' Course
# RICE University - https://class.coursera.org/interactivepython-005
# by Joe Warren, John Greiner, Stephen Wong, Scott Rixner
# Our mini-project for this week will focus on combining text drawing in
# the canvas with timers to build a simple digital stopwatch that keeps
# track of the time in tenths of a second. The stopwatch should contain
# "Start", "Stop" and "Reset" buttons.
try:
import simplegui
except:
import SimpleGUICS2Pygame.simpleguics2pygame as simplegui
# To run simplegui in idle python, install SimpleGUICS2Pygame module
# download module : https://pypi.python.org/pypi/SimpleGUICS2Pygame
# define global variables
A_string, B_string, C_string, D_string, count, score, over_all_score = 0, 0, 0, 0, 0, 0, 0
started = "No"
clear = "\n" * 100
# define helper function format that converts time
# in tenths of seconds into formatted string A:BC.D
def format(t):
global started, count, D_string, B_string, C_string, A_string
t = count
if started == "Yes":
t = D_string
D_string += 1
if D_string >= 10: #.1 seconds
if D_string == 10:
C_string += 1
if C_string == 10: # seconds
B_string += 1
if B_string >= 6:
A_string += 1
B_string = 0
C_string = 0
D_string = 0
return str(A_string) + ":" + str(B_string) + str(C_string) + "." + str(D_string)
# define event handlers for buttons; "Start", "Stop", "Reset"
def start_button():
global started#, count
started = "No"
#print "Started Button Pressed",started
if started:
timer.start()
started = "Yes"
print "Start Button Pressed"
#format(count)
#print ".10s Timer is Started"#,timer.is_running()
def score_board():
global score, over_all_score
if C_string > 0 and D_string == 0:
score += 1
# print "Good"
#else:
#if score > 0:
# score -= 1
# print "You are Loosing"
def stop_button():
global started, A_string, B_string, C_string, D_string, count, score, over_all_score
if started == "Yes":
timer.stop()
started = "No"
over_all_score += 1
score_board()
print "Stop Button Pressed"
#print ".10s Timer is Stopped"#,timer.is_running()
def reset_button():
global started, count, D_string, B_string, C_string , A_string, score, over_all_score
A_string, B_string, C_string, D_string, count, score, over_all_score = 0, 0, 0, 0, 0, 0, 0
timer.stop()
print "Timer Restarted"#,timer.is_running()
def exit_button():
frame.stop()
timer.stop()
print clear
# define event handler for timer with 0.1 sec interval
def timer_handler():
global started, count
#print "counter running",started
if started == "Yes":
count += 1
format(count)
# define draw handler
def draw_handler(canvas):
global score, over_all_score
canvas.draw_text(str(A_string) + ":" + str(B_string) +
str(C_string) + "." + str(D_string) ,
(100, 130), 50, 'White')
canvas.draw_text("Score: " + str(score) + "/" + str(over_all_score), (210, 20), 20, 'Red')
# create frame
frame = simplegui.create_frame('StopWatch Game!', 300, 200)
timer = simplegui.create_timer(100, timer_handler)
# register event handlers
frame.set_draw_handler(draw_handler)
frame.add_button('Start', start_button,70)
frame.add_button('Stop', stop_button,70)
frame.add_button('Reset', reset_button, 70)
frame.add_button('Exit', exit_button, 50)
# start frame
frame.start()
timer.start()
print "Global Timer is running:",timer.is_running()
# Please remember to review the grading rubric
[/sourcecode]


-
Challenge/ Problem
NGO Distro is a Content Management System (CMS) targeted towards the NGOs (Non Government Organizations) of Nepal. Most of the NGOs in Nepal don’t have capacity and resources to create and maintain website. To fill this gap, YoungInnovations has created a tool that is customizable, easy to setup and easy to use. Y&C took on the challenge to design the visual identity and themes for this tool.
-
Our Approach
The idea started with playing around the letters N, G and O. Fiddling around this idea gave birth to a mark that looked like a fingerprint. This also matched with our mission of building a tool that would help NGOs to create a website with simple taps of their fingertips. For the landing page, we wanted to look different than other CMSs out there. So we chose to go with a bit of retro-look.
Published Date
2019-06-12T06:17:52.572ZServices Provided
- Illustration
- Brand Identity
- Videography


Running into some unpredictable and not consistently replicable memory errors in the tgp function calls. Also rather troublesome that return object provides only posterior means for the process mean and covariance matrix.
Generic GP methods
- Implemented MCMC sampler. Excerpt below based on my gaussian-process-basics.md script, as updated to include this case.
Define some example priors
lpriors <- function(pars){
d.p <- c(5, 5)
s2.p <- c(5, 5)
prior <- unname(
dgamma(exp(pars[1]), d.p[1], scale = d.p[2]) *
dgamma(exp(pars[2]), s2.p[1], s2.p[2])
)
log(prior)
}
Define the posterior function for our metropolis sampler
posterior <- function(pars, x, y){
l <- exp(pars[1])
sigma.n <- exp(pars[2])
cov <- function(X, Y) outer(X, Y, SE, l)
I <- diag(1, length(x))
K <- cov(x, x)
loglik <- - 0.5 * t(y) %*% solve(K + sigma.n^2 * I) %*% y -
log(det(K + sigma.n^2*I)) -
length(y) * log(2 * pi) / 2
loglik + lpriors(pars)
}
I do not think we have good conjugate priors we can use in this situation to accomplish Gibbs sampling. Instead we simply MCMC by the Metropolis-Hastings alogrithm,
require(mcmc)
n <- 1e4
out <- metrop(posterior, log(pars), n, x = obs$x, y = obs$y)
out$accept
[1] 0.1509
Very good, we want an acceptance rate around 20%, if the literature can be trusted on this account. We then plot transformed and untransformed traces.
postdist <- cbind(index=1:n, as.data.frame(exp(out$batch)))
names(postdist) <- c("index", names(pars))
df <- melt(postdist, id="index")
# TRACES
ggplot(df) + geom_line(aes(index, value)) + facet_wrap(~ variable, scale="free", ncol=1)
ggplot(df) + geom_line(aes(index, log(value))) + facet_wrap(~ variable, scale="free", ncol=1)
Generate a plot to compare priors and posteriors
d.p <- c(5, 5)
s2.p <- c(5, 5)
s2_prior <- function(x) dgamma(x, s2.p[1], s2.p[2])
d_prior <- function(x) dgamma(x, d.p[1], scale = d.p[2])
prior_fns <- list(l = d_prior, sigma.n = s2_prior)
require(plyr)
prior_curves <- ddply(df, "variable", function(dd){
grid <- seq(min(dd$value), max(dd$value), length = 100)
data.frame(value = grid, density = prior_fns[[dd$variable[1]]](grid))
})
# Posteriors (easier to read than histograms)
ggplot(df, aes(value)) +
stat_density(geom="path", position="identity", alpha=0.7) +
geom_line(data=prior_curves, aes(x=value, y=density), col="red") +
facet_wrap(~ variable, scale="free", ncol=2)
Steve chat
- integrate over posteriors in GP case
- Ignore measurement uncertainty for the moment
- General status updates, etc.
Marc meeting
- Ricker case, but with constraint (e.g. maintain above n%)
- Consider Ricker model with external management constraint $ (Y / Y_opt) + (1 - ) Pr( )$
- Consider time horizon of learning vs implementing; see diagram
Simone talk
General interests: adaptive potential of small pops, demography of small pops, extinction processes in communities, evol life histories & enviroment, ML for species distribution models.
Marble trout system under the influence of regular catastrophic floods
Misc
- 6 hours on trains, 2 hours on buses, 1.25 hours on bicycle, 6 hours in lab.
- Interesting reading in The Secret History of OpenStack, the Free Cloud Software That’s Changing Everything. Sounds just like the unlikely collaboration we need to emerge for a more robust and sustainable underlying scientific infrastructure.


HTMLDocumentType
internal protocol HTMLDocumentType : XMLDocumentType
-
The contents of the tag
title
in the document.nil
if no such tag exist.Declaration
Swift
public final var title: String?
-
The node representing the tag
head
.nil
if no such tag exist.Declaration
Swift
public final var head: XMLElement?
-
The node representing the tag
body
.nil
if no such tag exist.Declaration
Swift
public final var body: XMLElement?


{% extends "base.html" %} {% block nav %} {% include "nav_for_login.html" %} {% endblock %} {% block content %}
To add user, send email invitation. Email should be associated with Google account
| User Name | User Email | Role | Created |
|---|---|---|---|
| {{ user.user_name }} | {{ user.email }} |
{% if current_user.role in ('primary_owner', 'admin') %}
|
{{ user.created_at }} |
| Token | Created by | Created | |
|---|---|---|---|
| {{ token['token'] }} | {{ token['created_by'] }} | {{ token['created_at'] }} | {% if current_user.role in ('primary_owner', 'admin') %} {% else %} - {% endif %} |


Hurry! Get Your Free FASTags Before December 1
A FASTag is tag embedded with a unique identification number mounted on the windscreen of a vehicle. As the vehicle reaches the toll plaza, the number is read by the RFID reader. The toll amount is deducted from the linked account.
New Delhi: The National Highway Authorities of India has started giving FAStags free of cost even without the security deposit of Rs 150. “Between Thursday (November 21) and December 1, you can get the FASTags for free,” Union minister for road transport and highways Nitin Gadkari said.
These tags are RFID stickers that can be used to make electronic payment for toll charges. This would cost the government Rs 150-200 crore, approximately. But owners will have to link their bank account to pay the toll amount. From December 1, all but one lane will become FASTag lanes.
A FASTag is tag embedded with a unique identification number mounted on the windscreen of a vehicle. As the vehicle reaches the toll plaza, the number is read by the RFID reader. The toll amount is deducted from the linked account.
From 1.4 lakh km of National Highways, toll charges were being collected from almost 25,000 km, as on March-end 2019. Between April-September this year, 2000 km of new National Highways are being brought under toll. Some 535-536 toll plazas have electronic toll collection systems. Barring newly installed 16-17 toll plazas, almost all the lanes have electronic toll collection systems.
Owners of vehicles without FASTags will have to pay twice the normal rate at toll gates.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on India Latest News on India.com.
Story Timeline
- 16 Nov 2019 'Our Vision Not Just Limited to Forming Govt, But Rebuilding Nation': Nitin Gadkari
- 14 Oct 2019 Jammu-Srinagar National Highway on Terror Alert; Government Vehicles Might be Targets, Claims Report
- 20 Feb 2019 New RFID tags help robots to track moving objects
Comments - Join the Discussion


At cafe jecrc we provide world class variety of cuisines & a place you would like to visit again and agian.


DREAMPLANR is a mobile app to help you keep in touch with your life goals. You can make some plans to reach your goals and set due dates.
Check out what you can do with this life goal tracker app!
Your life goal will be tracked so you can be motivated everytime. Set your plans inside bucket list regarding to your life goals.
Check how your plan progresses goes. DREAMPLANR will show you the stats of your plans you've set and done.
Stay inspired to do and set more plan from other users. You can befriend with them.
DREAMPLANR will keep in touch and remind you about your plans on your busy life.


{% block content %}
Ww2 Mania App
Please choose a menu option and proceed
Start Game
How To Play
{% endblock %}


| Status: | {{data.status}} |
|---|---|
| Order placed at: | {{data.createdDate}} |
| Shipping Type: | {{data.shippingType}} |
| Shipping Price: | $ {{data.shippingPrice}} |
| Products: | {{data.detail.length}} |
| Total Products: | $ {{data.total}} |
| Total: | $ {{data.sumTotal}} |
| Ship Address: |
|---|
| {{data.deliveryAddress}} |


Today, enterprises must be able to consistently market, advertise, sell, and support online across smartphones, tablets, social networks, and a growing number of Internet of things (IoTs). This is why innovations in translation technology that deliver multichannel and agile translation services when and where customers need them are critical factors for global business success. Professionals rely on networks such as websites, social media, call centers, and live on-site interpretation to perform a growing number of business related tasks. Modern translation solutions that are dynamic, real time, and rendered on all devices are not only possible—they are increasingly a must.
test
Designed for the digital economy, Stepes disrupts traditional static localization models so multinational enterprises can penetrate into global markets more quickly and efficiently.


The atmosphere module¶
We’ve done the hard work of creating all the atmospheric arrays for the individual constituents. Now we just need to put everything together.
Atmosphere¶
We can construct the arrays of the optical depth, single scattering
albedo, and phase function with Atmosphere
. It requires
tuples of each of the 3 arrays for each atmospheric
constituent. I’ll go ahead and make these tuples for Rayleigh scattering and
dust
rayleigh_info = (rayleigh_od, rayleigh_ssa, rayleigh_pf)
dust_info = (dust_od, dust_ssa, dust_pf)
We can now add these to Atmosphere
, which will go ahead and construct the
composite arrays.
from pyRT_DISORT.atmosphere import Atmosphere
model = Atmosphere(rayleigh_info, dust_info)
DTAUC = model.optical_depth
SSALB = model.single_scattering_albedo
PMOM = model.legendre_moments
That’s all there is to it. We now have our atmospheric arrays. The remaning modules are generally small and simply help to construct some of the switches required by DISORT.


Chelsea FC interested in signing 17-year-old English youngster – report
Chelsea FC are keen on a deal to sign Wigan starlet Joe Gelhardt, according to reports
by Transfer AgentChelsea FC are one of a number of clubs keeping a close eye on Wigan starlet Joe Gelhardt, according to reports in the British media.
The Daily Star is reporting that the Blues are keeping tabs on the 17-year-old striker in the lead-up to the January transfer window.
The same story says that Manchester United and City, Liverpool FC, Everton and Tottenham Hotspur have also been keeping a close eye on the teenage forward’s development in recent weeks.
Gelhardt has made seven Championship appearances for Wigan so far this season, all of which have come from the bench.
Despite the reported interest from Chelsea FC, the story says that the Blues and other suitors only have a “slim” chance of being able to sign him in January.
That’s because Wigan’s finances are thought to be in order and the club are under no pressure to sell as things stand.
Gelhardt made his first team debut for Wigan at the age of 16 after having joined the club when he was 10.
Wigan are said to value the young forward at around £5m, according to the same story.
It remains to be seen whether Chelsea FC will be able to make any new signings in January as their Fifa transfer ban is still in place as things stand.


§580-6 Guardian ad litem for incompetent defendant. In any case where the court has reason to believe that the defendant in a matrimonial action is not fully competent to conduct the defendant's defense or to comprehend the nature of the proceedings, the court may appoint a guardian ad litem to represent the interests of the defendant. The court may assess the reasonable fees and expenses of the guardian ad litem as costs of the action, payable in whole or in part by either or both parties as the circumstances may justify. [L 1919, c 10, pt of §2; RL 1925, §2972; RL 1935, §4467; RL 1945, §12217; am L 1951, c 287, §1(2); RL 1955, §324-27; am L 1966, c 22, §6; am L 1967, c 76, §3; HRS §580-6; gen ch 1985]
Cross References
Maximum fees for appointed counsel and guardian ad litem, see §571-87.
Rules of Court
Guardians ad litem, see HFCR rule 17(c).


1. What is Lextor?
Lextor is a connected system where robots talk. It is artificially intelligent and can perform wide range of tasks.
2. How does it work?
It works in 5 steps:
1. Identification
The robots will be designed to work over an area and identify everything it can using a camera mounted on it.
2. Processing
A Raspberry Pi Pocket Sized PC along with Windows IOT Core/Linux Distro will be used for sending the images to the Cloud and running algorithms
3. Cloud
Azure will be used along with many Open Source libraries to run different algorithms.
4. Algorithms
The algorithms azure will be running will be license plate recognition system, face recognition system, speed detection, fire detection, etc.
5. Output
The robots will be equipped to perform several tasks like informing nearby medical support or changing traffic lights, etc.
3. Why is this platform required?
This platform is required to fulfil the following objectives:
· To provide a platform wherein users can avail multiple land, air or water facilities, such as surveillance and maintenance, to boost efficiency of their sectors, all using robots.
· To make available these services using latest technology- i.e., by a swarm of robots in air (swarm of drones), on land (swarm of land robots) or under water (swarm of aqua-robots), all controlled by an Artificial Intelligence.
· Through this, to make as many people and sectors as we can, ultimately a part of our broad vision goal to accomplish UN’s Sustainable Development Goals before 2030.
· To provide a great connected healthcare to people
· A create a decentralized system in order to do tasks faster
· Provide a system for control and management of traffic using UAV
4. What type of robots will be performing the services?
The robots will be designed and pre-programmed by us, and therefore will operate autonomously in all the 3 domains- air, land and water. Drones (UAVs) will work in air, the wheeled robots will cover the land, and the aqua robots will be in action under waters.
5. How are the robots controlled and monitored?
Monitoring and evaluation is done by various algorithms that provide easy functionality of the system, like the examples below. Azure is the cloud platform we used in order to deploy our code.
· Speed and accidents. Various sensors shall be used so that the drone detects speed of the vehicles and also find places where accidents can occur or have occurred.
· License Plate Recognition. By using OpenALPR, the drone will be able to identify any license plate, making it very easy to detect the owner of the vehicle involved in accidents or any illegal activity.
· Facial Recognition. OpenCV and Python Libraries will be used to detect faces which can be run in databases like Aadhar in India that stores face of every citizen.
· Artificial Intelligence. The received inputs need to be worked upon and since Humans cannot be always there to take actions we will need an artificial intelligence that takes specific actions on the basis of received inputs.
6. How is it better than the existing platforms doing similar business?
· Through our platform, we'll be creating swarm of robots of each domain (air, land and water) which will be controlled by AI.
· Our platform will be allowing 'interconnection between the robots of the 3 domains' so that they work with coordination.
· Single platform will be able to contribute to UN's sustainable goals by AI in multiple ways.
· Lextor may work autonomously in future.
7. Who all can avail the services from this platform?
Lookup our services section for this information. Here
8. What makes lextor worth the investment?
Multiple Possibilities. One Platform. Many services can be carried out by this single platform, making it cost effective. The services are not limited to our current targets but can be expanded wherever required.
Latest Technology. Robots are the NOW main trend, and in our platform, they are controlled by Artificial Intelligence. The design of our platform is simple, as it is taken directly from the nature, hence allowing us to make our platform even more efficient with network designs like Artificial Neural Network (ANN) (design taken from the human brain).
Better World. Delivered. With our platform, we assure that we will contribute to make this world a better place, and the consumers too will become a part of the process, as our broad vision goal is to work towards sustainable development by targeting on U.N. Sustainable Development Goals.
SHARE THIS PAGE!


Home
Portfolio
About Me
THINGS I KNOW
JavaScript
CSS/HTML
C#/.Net
Python
React
SQL
Things i've made so far
Quiz
Neighbor
Cupcake


MyUni
Sample Change a Major or Minor Request Form
Student Information
First Name
{{ first_name }}
Last Name
{{ last_name }}
Email address
**email**/
Major Field
{{ major }}
Minor Field
{{ minor }}
Signature
**signature_1**/
Initials
**initials_1**/


{% extends "gamecraft/gamecraft/base.html" %} {% block title %}GameCraft Privacy and Legal Statements{% endblock %} {% block content %}
GameCraft respects the privacy of sponsors, helpers, event participants and visitors to this site. We make every attempt to avoid keeping personal identifying information. We may use tools such as ticket sales tools which keep some identifying information but access to these is restricted to core GameCraft staff.
As part of some monitoring and measurement tools this website may set cookies in your browser. The underlying web framework (Django) may also set some cookies. Expect to see some cookies from TypeKit and CloudFlare. GameCraft doesn't track users for advertising purposes or any other reason than performance monitoring and security. If these tools had the option to turn off cookies and remain useful we'd do it :)
See the site colophon for more details on how this site is put together and tools used.
All games and content produced during GameCraft events remains the full property of their creators. GameCraft makes no claim to anything participants create. When you take part in GameCraft events you own everything you create.
All trademarks remain property of their respective owners.


It Works! What Next?
- Replace the HTML at app/index.html and create your own HTML files.
- Write some Sass. Update the files in the app/scss directory.
- Update or delete the app/js/scripts.js file.
- Change out the favicon and other icon images. You can automate this process at http://realfavicongenerator.net/.
- Run
grunt
by itself to build optimized files in a dist directory.


Jim Goodwin hails super subs for helping Saints unlock Ross County defence late for vital win
Substitutes Tony Andreu and Kyle McAllister helped to set up Sam Foley's late wonderstrike.
by Ben RamageDelighted St Mirren manager Jim Goodwin paid tribute to his super subs after they helped carve out a telling winner.
With the Buddies struggling to break down a stubborn Ross County defence, the gaffer threw on attacking talents Tony Andreu and Kyle McAllister.
And they helped set up Sam Foley's superb strike that sealed an important win for St Mirren .

"What a fantastic goal to win it. Tony Andreu plays a big part in it, does really well. Kyle McAllister puts a good ball around the corner as well.
"The two boys were involved and obviously Sam Foley gets the plaudits because it's a hell of a strike."
While Goodwin wishes his team hadn't left it to the last minute to make their dominance pay, he was pleased with the character his side showed to stick at it to the death.
He added: "I'm always happy when we win. I was a little bit frustrated to be honest because I felt we could have made it a lot more comfortable than what it was.
"I'm really pleased for the players. It was fantastic character that they showed to go behind and come back and win it like that.
"It was a great result and a really important result. A draw wouldn't have been enough today, there's no doubt about that.
"It was great scenes at the end and great to see the the fans at the end happy and enjoying that moment.
"We were maybe five minutes from being booed off the park so thankfully we managed to get a nice ovation at the end instead."


In an unsafe situation, please call your nearest crisis hotline. You are nearest to Hennepin County: +1<PHONE_NUMBER>


Files CCore/inc/Scanf.h CCore/src/Scanf.cpp
Files CCore/inc/DetectProxy.h CCore/src/DetectProxy.cpp
Subfolders CCore/inc/scanf CCore/src/scanf
Scanning is a process, inverse to the printing. I.e. scanning is a creation of an object state from a sequence of characters. There are three actors in a scanning process: the object, the input device and scanning options. The input device provides a character sequence, scanning options determines how the object state is encoded. The type of the object is responsible for the scanning implementation. There are default implementations for integral and string types.
Scanning is performed with the following scanning functions:
template <class S,ScanableType ... TT> void Scanf(S &&inp,const char *format,TT && ... tt); template <class S,ScanableType ... TT> void Scanobj(S &&inp,TT && ... tt);
Scanf() uses the given format string to scan. Format string may specify options for each object to scan.
Scanobj() scans the input to the given set of objects with default scan options.
Format string contains embedded format specifiers. Each character not in a format specifier causes Scanf() to read a single character from the input and match it with the format character. If there is no input characters or characters are different then the scanning is failed. The character ' '(space) is treated especially: it extracts from the input device space-like characters up to the end or a non-space character. Format specifier has a form "#<options>;", where <options> is the option string. It is converted to the correspondent object scan options. To scan the character '#' use "##" as a format specifier.
There are three ways to make a type scanable.
The most direct way: define in the class definition the method template scan() like this:
class SomeClass { public: void scan(ScannerType &inp) { .... } };
If you need scanning options do like this:
class SomeClass { public: struct ScanOptType { .... ScanOptType(); // default options ScanOptType(const char *ptr,const char *lim); // [ptr,lim) -- options from the format string // if the format string has a format specifier #XXX;, // then [ptr,lim) points to the string XXX }; void scan(ScannerType &inp,const ScanOptType &opt) { .... } };
The second way is to specify a scan proxy type:
class SomeClass { public: using ScanProxyType = .... ; };
In this case the SomeClass::ScanProxyType & must be castable from the type SomeClass. For example:
struct S { int val = 0 ; using SomeClass = int ; operator int & () { return val; } };
You can also specialize the template CCore::ScanProxy<T> to define the scan proxy type and optionally the scan option type.
namespace App { struct S { .... }; } // namespace App namespace CCore { template <> struct ScanProxy<App::S> { using ProxyType = .... ; using OptType = .... ; }; } // namespace CCore
You can determine the scan option type using the ScanOptAdapter<T>. If the type T has a scan option type (even through proxy), then ScanOptAdapter<T>::ScanOptType is that type.
Sometimes you need to specify scan options using the object, not the format string. In such case use the class BindScanOpt():
template <class OptType,class T> struct BindScanOpt;
T obj;
Opt opt;
Scanf(out,"#;",BindScanOpt(opt,obj));
An input device for scanning provides a sequential access to a character sequence. An input device type must be either an actual input device type or redirect the input functionality to another type using a specialization of the ScanInpAdapter. By default, the actual input device type is declared as the inner type ScanInpType.
/* struct ScanInpAdapter<S> */
template <class S>
struct ScanInpAdapter
{
using ScanInpType = typename S::ScanInpType ;
};
The input device must provide the following methods:
class IDev { public: using ScanInpType = IDev & ; ScanInpType scanRef() { return *this; } // cursor ulen operator + () const; bool operator ! () const; char operator * () const; void operator ++ (); // error bool isOk() const; bool isFailed() const; void fail(); };
It behaves like a character cursor plus internal fail flag is implemented to signal about scanning errors or input errors.
The simplest way of the building input object classes is to derive them from the ScanBase class.
class ScanBase : NoCopy
{
....
private:
virtual PtrLen<const char> underflow()=0;
protected:
void pump(); // must be called in constructor of derived class
void reset();
public:
// constructors
ScanBase();
~ScanBase();
// cursor
ulen operator + () const;
bool operator ! () const;
char operator * () const;
void operator ++ ();
// position
TextPos getTextPos() const;
// error
bool isOk() const;
bool isFailed() const;
void fail();
};
This class counts the current text position. You can retrieve it with the method getTextPos(). A derived class must implement the virtual method underflow(). This method is called when the previous input character frame is consumed. It must return a character range. This range can be empty, if the end of input is reached. It must remains valid until the next call of underflow(). In case of error the method fail() must be called and an exception may be thrown. There are two protected methods to be used in a derived class. pump() performs the character pumping, it must be called in the derived class constructor as the last action. reset() resets the base to the initial state. It can be used in such derived class methods like open() or close() when the input is changed.
To scan a given string the class ScanString exists:
class ScanString : public ScanBase
{
PtrLen<const char> str;
private:
virtual PtrLen<const char> underflow() { return Replace_null(str); }
public:
explicit ScanString(StrLen str_) : str(str_) { pump(); }
~ScanString() {}
};
There is a default implementation of the string scanning. It is applied to objects of the type String. The following scan options are supported:
/* enum StringScanType */ enum StringScanType { StringScanQuote, StringScanToSpace, StringScanToPunct, StringScanDefault = StringScanToPunct }; template <class Dev> bool Parse_try(Dev &dev,StringScanType &ret); /* struct StringScanOpt */ struct StringScanOpt { StringScanType type; void setDefault() { type=StringScanDefault; } StringScanOpt() { setDefault(); } StringScanOpt(const char *ptr,const char *lim); // // [.q|.Q|.s|.S|.p|.P] // };
There are three variants of the string scanning, they are selected by options. Options ".q" and ".Q" means scan a quoted string (StringScanQuote), ".s" and ".S" means scan up to a space character (StringScanToSpace), and ".p" and ".P" means scan up to a space or punctuation character (StringScanToPunct). The last mode is default, punctuation characters are: ! " # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ ` { | } ~.
When scanning a quoted string the input is scanned for a string of characters, enclosed in double quotes. Quotes are not included in the result. If the input does not begin with a quoted string, it is failed.
When scanning up to a some kind of character the input is scanned for a string of characters up to the first character of that kind.
The class StringSetScan scans the input for one of the given strings and returns the string index. Index is 1-based. If the input does not begin with one of the expected strings, the input is failed and the index is set to null.
class StringSetScan : NoCopy
{
....
public:
StringSetScan(std::initializer_list<const char *> zstr_list);
StringSetScan(std::initializer_list<StrLen> str_list);
~StringSetScan();
using PrintProxyType = ulen ;
operator ulen() const { return index; }
void scan(ScannerType &inp);
};
Constructor takes a braced list of zero-terminated strings. These strings must persist over the object life-time. Usually, they are string literals.
The cast operator returns the 1-based index of the found string or null if the scan has failed.
There is a default implementation of the integral types scanning. It is applied to objects of integral type. The following scan options are supported:
/* enum IntScanBase */ enum IntScanBase { IntScanNone, IntScanHex, IntScanBin, IntScan0X, IntScanDefault = IntScanNone }; template <class Dev> bool Parse_try(Dev &dev,IntScanBase &ret); /* struct IntScanOpt */ struct IntScanOpt { unsigned base; // 2..16, 0 for any IntScanBase scan_base; void setDefault() { base=0; scan_base=IntScanDefault; } IntScanOpt() { setDefault(); } IntScanOpt(const char *ptr,const char *lim); // // [.base|.x|.X|.b|.B|.h|.H] // };
There are four forms of the integer representation.
Binary form:
[+|-]D1..(b|B) , where D is a binary digit
Hex form:
[+|-]D1..(h|H) , where D is a hex digit
0x form:
[+|-]0xD1.. , where D is a hex digit
And base form, where base is the representation base from 2 to 16:
[+|-]D1.. , where D is a base digit
By default the integer scanner accepts all form of the integer representation, base is assumed 10. But using options you may specify a desired form. Use .b or .B to specify the binary form, .h or .H to specify the hex form, .x or .X to specify the 0x form and .base to specify the base form.
There is a family of scan utility functions.
/* concept CharStreamType<S> */ template <class S> // ref extended concept bool CharStreamType = requires(S &obj,Meta::ToConst<S> &cobj) { { +cobj } -> bool ; { !cobj } -> bool ; { *cobj } -> char ; ++obj; } ; /* concept CharFailStreamType<S> */ template <class S> // ref extended concept bool CharFailStreamType = CharStreamType<S> && requires(S &obj) { obj.fail(); } ;
CharStreamType is a character stream type, it is a cursor over char.
CharFailStreamType is a CharStreamType with the method fail().
int CharBaseValue(char ch,unsigned base);
This function returns the digit value of the given character or -1, if the character is not a digit, where base is a representation base. It must be in the range [2,16].
void SkipSpace(CharStreamType &inp);
This function extracts space-like characters from the input up to the end or a first non-space character. Can be applied to any char cursor.
void SkipNonSpace(CharStreamType &inp);
This function extracts non-space characters from the input up to the end or a first space-like character. Can be applied to any char cursor.
bool ProbeChar(CharStreamType &inp,char ch);
This function tries to move over the specified char. On success it returns true, otherwise the return value is false and the cursor remains at the current position. Can be applied to any char cursor.
template <CharFailStreamType S,class ... CC>
void PassChars(S &inp,CC ... cc);
This function extracts the given sequence of characters or fail the input.
void PassOneOfChar(CharFailStreamType &inp,FuncType<bool,char> func);
This function extracts one character, which satisfies the given condition, or fail the input. func is a functor with the signature boolable (char ch). It is used to specify the condition.
void SkipAllOfChar(CharStreamType &inp,FuncType<bool,char> func);
This function extracts characters, which satisfies the given condition, up to the end or a first character, which does not satisfy the condition. Can be applied to any char cursor. func is a functor with the signature boolable (char ch). It is used to specify the condition.
enum EndOfScanType
{
EndOfScan
};
This word can be scanned. It fails the scanner if it is not empty.
template <class T,class ProxySet>
struct ScanProxySet : ScanOptAdapter<ProxySet>
{
struct ProxyType
{
T &ret;
explicit ProxyType(T &ret_) : ret(ret_) {}
template <class S>
void scan(S &inp)
{
ProxySet set;
Scanobj(inp,set);
set.map(ret);
}
template <class S,class OptType>
void scan(S &inp,const OptType &opt)
{
ProxySet set;
Scanobj(inp,BindScanOpt(opt,set));
set.map(ret);
}
};
};
This class can be used to specialize the template ScanProxy<T> .
template <> struct ScanProxy<SomeType> : ScanProxySet<SomeType,SetType> {};
The class SetType will be used to scan instead the type SomeType. Then the result is returned using the method map().
class SetType { public: SetType(); void map(SomeType &ret) const; void scan(ScannerType &inp); }; OR class SetType { public: SetType(); void map(SomeType &ret) const; using ScanOptType = .... ; void scan(ScannerType &inp,ScanOptType opt); };


statsmodels.tsa.statespace.sarimax.SARIMAX¶
-
class
statsmodels.tsa.statespace.sarimax.
SARIMAX
(endog, exog=None, order=(1, 0, 0), seasonal_order=(0, 0, 0, 0), trend=None, measurement_error=False, time_varying_regression=False, mle_regression=True, simple_differencing=False, enforce_stationarity=True, enforce_invertibility=True, hamilton_representation=False, **kwargs)[source]¶ Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors model
Parameters: endog : array_like
The observed time-series process
exog : array_like, optional
Array of exogenous regressors, shaped nobs x k.
order : iterable or iterable of iterables, optional
The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters. d must be an integer indicating the integration order of the process, while p and q may either be an integers indicating the AR and MA orders (so that all lags up to those orders are included) or else iterables giving specific AR and / or MA lags to include. Default is an AR(1) model: (1,0,0).
seasonal_order : iterable, optional
The (P,D,Q,s) order of the seasonal component of the model for the AR parameters, differences, MA parameters, and periodicity. d must be an integer indicating the integration order of the process, while p and q may either be an integers indicating the AR and MA orders (so that all lags up to those orders are included) or else iterables giving specific AR and / or MA lags to include. s is an integer giving the periodicity (number of periods in season), often it is 4 for quarterly data or 12 for monthly data. Default is no seasonal effect.
trend : str{‘n’,’c’,’t’,’ct’} or iterable, optional
Parameter controlling the deterministic trend polynomial . Can be specified as a string where ‘c’ indicates a constant (i.e. a degree zero component of the trend polynomial), ‘t’ indicates a linear trend with time, and ‘ct’ is both. Can also be specified as an iterable defining the polynomial as in numpy.poly1d, where [1,1,0,1] would denote . Default is to not include a trend component.
measurement_error : boolean, optional
Whether or not to assume the endogenous observations endog were measured with error. Default is False.
time_varying_regression : boolean, optional
Used when an explanatory variables, exog, are provided provided to select whether or not coefficients on the exogenous regressors are allowed to vary over time. Default is False.
mle_regression : boolean, optional
Whether or not to use estimate the regression coefficients for the exogenous variables as part of maximum likelihood estimation or through the Kalman filter (i.e. recursive least squares). If time_varying_regression is True, this must be set to False. Default is True.
simple_differencing : boolean, optional
Whether or not to use partially conditional maximum likelihood estimation. If True, differencing is performed prior to estimation, which discards the first initial rows but reuslts in a smaller state-space formulation. If False, the full SARIMAX model is put in state-space form so that all datapoints can be used in estimation. Default is False.
enforce_stationarity : boolean, optional
Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. Default is True.
enforce_invertibility : boolean, optional
Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. Default is True.
hamilton_representation : boolean, optional
Whether or not to use the Hamilton representation of an ARMA process (if True) or the Harvey representation (if False). Default is False.
**kwargs
Keyword arguments may be used to provide default values for state space matrices or for Kalman filtering options. See Representation, and KalmanFilter for more details.
Notes
The SARIMA model is specified .
In terms of a univariate structural model, this can be represented as
where is only applicable in the case of measurement error (although it is also used in the case of a pure regression model, i.e. if p=q=0).
In terms of this model, regression with SARIMA errors can be represented easily as
this model is the one used when exogenous regressors are provided.
Note that the reduced form lag polynomials will be written as:
If mle_regression is True, regression coefficients are treated as additional parameters to be estimated via maximum likelihood. Otherwise they are included as part of the state with a diffuse initialization. In this case, however, with approximate diffuse initialization, results can be sensitive to the initial variance.
This class allows two different underlying representations of ARMA models as state space models: that of Hamilton and that of Harvey. Both are equivalent in the sense that they are analytical representations of the ARMA model, but the state vectors of each have different meanings. For this reason, maximum likelihood does not result in identical parameter estimates and even the same set of parameters will result in different loglikelihoods.
The Harvey representation is convenient because it allows integrating differencing into the state vector to allow using all observations for estimation.
In this implementation of differenced models, the Hamilton representation is not able to accomodate differencing in the state vector, so simple_differencing (which performs differencing prior to estimation so that the first d + sD observations are lost) must be used.
Many other packages use the Hamilton representation, so that tests against Stata and R require using it along with simple differencing (as Stata does).
Detailed information about state space models can be found in [R80]. Some specific references are:
- Chapter 3.4 describes ARMA and ARIMA models in state space form (using the Harvey representation), and gives references for basic seasonal models and models with a multiplicative form (for example the airline model). It also shows a state space model for a full ARIMA process (this is what is done here if simple_differencing=False).
- Chapter 3.6 describes estimating regression effects via the Kalman filter (this is performed if mle_regression is False), regression with time-varying coefficients, and regression with ARMA errors (recall from above that if regression effects are present, the model estimated by this class is regression with SARIMA errors).
- Chapter 8.4 describes the application of an ARMA model to an example dataset. A replication of this section is available in an example IPython notebook in the documentation.
References
[R80] (1, 2) Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. Attributes
measurement_error (boolean) Whether or not to assume the endogenous observations endog were measured with error. state_error (boolean) Whether or not the transition equation has an error component. mle_regression (boolean) Whether or not the regression coefficients for the exogenous variables were estimated via maximum likelihood estimation. state_regression (boolean) Whether or not the regression coefficients for the exogenous variables are included as elements of the state space and estimated via the Kalman filter. time_varying_regression (boolean) Whether or not coefficients on the exogenous regressors are allowed to vary over time. simple_differencing (boolean) Whether or not to use partially conditional maximum likelihood estimation. enforce_stationarity (boolean) Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. enforce_invertibility (boolean) Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. hamilton_representation (boolean) Whether or not to use the Hamilton representation of an ARMA process. trend (str{‘n’,’c’,’t’,’ct’} or iterable) Parameter controlling the deterministic trend polynomial . See the class parameter documentation for more information. polynomial_ar (array) Array containing autoregressive lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero). polynomial_ma (array) Array containing moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero). polynomial_seasonal_ar (array) Array containing seasonal moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero). polynomial_seasonal_ma (array) Array containing seasonal moving average lag polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero). polynomial_trend (array) Array containing trend polynomial coefficients, ordered from lowest degree to highest. Initialized with ones, unless a coefficient is constrained to be zero (in which case it is zero). k_ar (int) Highest autoregressive order in the model, zero-indexed. k_ar_params (int) Number of autoregressive parameters to be estimated. k_diff (int) Order of intergration. k_ma (int) Highest moving average order in the model, zero-indexed. k_ma_params (int) Number of moving average parameters to be estimated. seasonal_periods (int) Number of periods in a season. k_seasonal_ar (int) Highest seasonal autoregressive order in the model, zero-indexed. k_seasonal_ar_params (int) Number of seasonal autoregressive parameters to be estimated. k_seasonal_diff (int) Order of seasonal intergration. k_seasonal_ma (int) Highest seasonal moving average order in the model, zero-indexed. k_seasonal_ma_params (int) Number of seasonal moving average parameters to be estimated. k_trend (int) Order of the trend polynomial plus one (i.e. the constant polynomial would have k_trend=1). k_exog (int) Number of exogenous regressors. Methods
filter
(params, **kwargs)fit
([start_params, transformed, cov_type, ...])Fits the model by maximum likelihood via Kalman filter. from_formula
(formula, data[, subset])Not implemented for state space models hessian
(params, *args, **kwargs)Hessian matrix of the likelihood function, evaluated at the given impulse_responses
(params[, steps, impulse, ...])Impulse response function information
(params)Fisher information matrix of model initialize
()Initialize the SARIMAX model. initialize_approximate_diffuse
([variance])Initialize the statespace model with approximate diffuse values. initialize_known
(initial_state, ...)Initialize the statespace model with known distribution for initial state. initialize_state
([variance, complex_step])Initialize state and state covariance arrays in preparation for the Kalman filter. initialize_statespace
(**kwargs)Initialize the state space representation initialize_stationary
()Initialize the statespace model as stationary. loglike
(params, *args, **kwargs)Loglikelihood evaluation loglikeobs
(params[, transformed, complex_step])Loglikelihood evaluation observed_information_matrix
(params[, ...])Observed information matrix opg_information_matrix
(params[, ...])Outer product of gradients information matrix predict
(params[, exog])After a model has been fit predict returns the fitted values. prepare_data
()score
(params, *args, **kwargs)Compute the score function at params. score_obs
(params[, method, transformed, ...])Compute the score per observation, evaluated at params set_conserve_memory
([conserve_memory])Set the memory conservation method set_filter_method
([filter_method])Set the filtering method set_inversion_method
([inversion_method])Set the inversion method set_smoother_output
([smoother_output])Set the smoother output set_stability_method
([stability_method])Set the numerical stability method simulate
(params, nsimulations[, ...])Simulate a new time series following the state space model smooth
(params, **kwargs)transform_jacobian
(unconstrained[, ...])Jacobian matrix for the parameter transformation function transform_params
(unconstrained)Transform unconstrained parameters used by the optimizer to constrained parameters used in likelihood evaluation. untransform_params
(constrained)Transform constrained parameters used in likelihood evaluation update
(params[, transformed, complex_step])Update the parameters of the model Methods
filter
(params, **kwargs)fit
([start_params, transformed, cov_type, ...])Fits the model by maximum likelihood via Kalman filter. from_formula
(formula, data[, subset])Not implemented for state space models hessian
(params, *args, **kwargs)Hessian matrix of the likelihood function, evaluated at the given impulse_responses
(params[, steps, impulse, ...])Impulse response function information
(params)Fisher information matrix of model initialize
()Initialize the SARIMAX model. initialize_approximate_diffuse
([variance])Initialize the statespace model with approximate diffuse values. initialize_known
(initial_state, ...)Initialize the statespace model with known distribution for initial state. initialize_state
([variance, complex_step])Initialize state and state covariance arrays in preparation for the Kalman filter. initialize_statespace
(**kwargs)Initialize the state space representation initialize_stationary
()Initialize the statespace model as stationary. loglike
(params, *args, **kwargs)Loglikelihood evaluation loglikeobs
(params[, transformed, complex_step])Loglikelihood evaluation observed_information_matrix
(params[, ...])Observed information matrix opg_information_matrix
(params[, ...])Outer product of gradients information matrix predict
(params[, exog])After a model has been fit predict returns the fitted values. prepare_data
()score
(params, *args, **kwargs)Compute the score function at params. score_obs
(params[, method, transformed, ...])Compute the score per observation, evaluated at params set_conserve_memory
([conserve_memory])Set the memory conservation method set_filter_method
([filter_method])Set the filtering method set_inversion_method
([inversion_method])Set the inversion method set_smoother_output
([smoother_output])Set the smoother output set_stability_method
([stability_method])Set the numerical stability method simulate
(params, nsimulations[, ...])Simulate a new time series following the state space model smooth
(params, **kwargs)transform_jacobian
(unconstrained[, ...])Jacobian matrix for the parameter transformation function transform_params
(unconstrained)Transform unconstrained parameters used by the optimizer to constrained parameters used in likelihood evaluation. untransform_params
(constrained)Transform constrained parameters used in likelihood evaluation update
(params[, transformed, complex_step])Update the parameters of the model Attributes
endog_names
Names of endogenous variables exog_names
initial_design
Initial design matrix initial_selection
Initial selection matrix initial_state_intercept
Initial state intercept vector initial_transition
Initial transition matrix initial_variance
initialization
loglikelihood_burn
model_latex_names
The latex names of all possible model parameters. model_names
The plain text names of all possible model parameters. model_orders
The orders of each of the polynomials in the model. param_names
List of human readable parameter names (for parameters actually included in the model). param_terms
List of parameters actually included in the model, in sorted order. params_complete
start_params
Starting parameters for maximum likelihood estimation tolerance


-
Smuggling HTTP headers through reverse proxies
Under some conditions, it is possible to smuggle HTTP headers through a reverse proxy, even if it was explicitly unset before. This is possible in some cases due to HTTP header normalization and parser differentials. Because HTTP headers are commonly used as way to pass authentication data to the backend (for example in mutual TLS scenarios), this can lead to critical vulnerabilities.
-
Unquoted Service Path exploit in Fortinet FortiClient
FortiClient for Windows prior to 6.2.3 is vulnerable to an unquoted service path vulnerability (CVE-2019-17658). That may allow an attacker to gain elevated privileges via the FortiClientConsole executable service path.
-
Juniper Junos Space prior to 19.4R1 Local File Inclusion Vulnerability
A Local File Inclusion vulnerability (CVE-2020-1611) in Juniper Networks Junos Space allows an attacker to view all files on the target when the device receives malicious HTTP packets.
-
wolfSSL TLSv1.3 Remote Buffer Overflow
A new critical remote buffer overflow vulnerability (CVE-2019-11873) was discovered in the wolfSSL library (version 4.0.0-stable, http://www.wolfssl.com) by Security Evaluators of Telekom Security with modern fuzzing methods. The vulnerability allows an attacker to overwrite a large part of the RAM of a wolfSSL server with hisdata over the network.
-
axTLS Remote Buffer Overflow
A new critical remote buffer overflow vulnerability (CVE-2019-8981) in the axTLS library for embedded devices (version 2.1.4, http://axtls.scourceforge.net was discovered on 2019 February 20 with modern fuzzing methods, which possibly allows remote code execution. A new fixed version (2.1.5) countering this is now available for download.
-
WeBid Directory Traversal, Blind SQL Injection and XSS
Multiple vulnerabilities were identified in version 1.2.2 of the popular “WeBid” open source auction system. Patches for all three vulnerabilities are available in their GitHub, and will be included in the next release.
-
ServiceNow Glide Scripting injection leading to privilege escalation
ServiceNow, an enterprise IT service management solution, is vulnerable to an template injection vulnerability, leading to a full privilege escalation.
-
Variant of Satori/Mirai detected attacking public available ADB shells
On the 10th of July at 23:30 UTC we noticed an increased traffic on our blackhole monitoring on TCP port 5555. Upon further analysis, we saw a big chunk of this traffic coming from China, USA and the Dominican Republic. In total we gathered 246.434 packets from 68.361 unique IPs. Based on the packet details we gathered, we can assume that the packets were generated by a lot of different devices. In addition, the traffic behavior on port 5555 matches the typicall scan behavior of botnets.


PATh is committed to openness in the science and institutions we support, in innovation of technologies and methodologies, and in interfaces to the broader ecosystem of NSF-funded CI services, especially as the fabric of coordination services evolves. To facilitate ingestion of ideas into HTCSS, and services into FoCaS, the Global Infrastructure Lab (GIL) tests and evaluate infrastructure software from inside and outside the partnership.
GIL maintains several testbeds, allowing the team to rapidly test configurations. These dedicated testbeds are composed of volunteer partners in the OSG Consortium providing service orchestration features. Virtual testbeds are formed by overlaying the software to be evaluated onto production resources and by using short-lived cloud bursts. These techniques allow GIL to serve as a laboratory for services prior to introduction within FoCaS. Within scope are major new versions of existing services as well as new services slated to either expand or replace existing functionality. GIL is thus positioned in PATh to act as a Technology Translation Service. GIL also helps the PATh Software Development team identify software worth interfacing with and ideas worth implementing and provide a platform for scalability and usability evaluation of HTCSS components.
GIL focuses on features and products where it can make the most impact. On top of software to adopt, GIL also explores ideas for implementation in HTCSS and pointers to products with which HTCSS should interoperate.
GIL rapidly deploys new and updated services using a lightweight and highly automated approach. The GIL team is committed to collaborate with the XD program, IRIS-HEP, other national services like PRP and SLATE, and public cloud providers. By leveraging these connections and other NSF-funded national and global projects, PATh is positioned to innovate on the infrastructure layer with benefits across the NSF CI ecosystem.
GIL has proposed and led the discussion of the following ideas:


Analog_clock
Try Now
Auto_write_text
Try Now
AC
DEL
÷
1
2
3
*
4
5
6
+
7
8
9
-
.
0
=
Calculator
Try now
Color_flipper_1
Click Try button to explore
Color_flipper_1
Try now
Colorflipper_2
Click Try button to explore
Color_flipper_2
Try now
Digital_clock
Try now
Log in form
Try now
Purple_heart_animation
Try now
Question Text
Question
Question
Question
Question
Submit
Try now
Music
Try now
Click Me Pretty Please
Notification
Try now
Check_weather
Try now


Whoa it's Carlos Ray "Chuck" Norris
Choose a category:
Get Your Chuck Fact
Get a New Fact
Chuck says ...
x