In the previous chapter, we created the automated testing practices to ensure that developers get fast feedback on the quality of their work. This becomes even more important as we increase the number of developers and the number of branches they work on in version control.
The ability to “branch” in version control systems was created primarily to enable developers to work on different parts of the software system in parallel, without the risk of individual developers checking in changes that could destabilize or introduce errors into trunk (sometimes also called master or mainline).†
However, the longer developers are allowed to work in their branches in isolation, the more difficult it becomes to integrate and merge everyone’s changes back into trunk. In fact, integrating those changes becomes exponentially more difficult as we increase the number of branches and the number of changes in each code branch.
Integration problems result in a significant amount of rework to get back into a deployable state, including conflicting changes that must be manually merged or merges that break our automated or manual tests, usually requiring multiple developers to successfully resolve. And because integration has traditionally been done at the end of the project, when it takes far longer then planned, we are often forced to cut corners to make the release date.
This causes another downward spiral: when merging code is painful, we tend to do it less often, making future merges even worse. Continuous integration was designed to solve this problem by making merging into trunk a part of everyone’s daily work.
The surprising breadth of problems that continuous integration solves, as well as the solutions themselves, are exemplified in Gary Gruver’s experience as the director of engineering for HP’s LaserJet Firmware division, which builds the firmware that runs all their scanners, printers, and multifunction devices.
The team consisted of four hundred developers distributed across the US, Brazil, and India. Despite the size of their team, they were moving far too slowly. For years, they were unable to deliver new features as quickly as the business needed.
Gruver described the problem thusly: “Marketing would come to us with a million ideas to dazzle our customer, and we’d just tell them, ‘Out of your list, pick the two things you’d like to get in the next six to twelve months.’”
They were only completing two firmware releases per year, with the majority of their time spent porting code to support new products. Gruver estimated that only 5% of their time was spent creating new features—the rest of the time was spent on non-productive work associated with their technical debt, such as managing multiple code branches and manual testing, as shown below:
Gruver and his team created a goal of increasing the time spent on innovation and new functionality by a factor of ten. The team hoped this goal could be achieved through:
Before this, each product line would require a new code branch, with each model having a unique firmware build with capabilities defined at compile time.‡ The new architecture would have all developers working in a common code base, with a single firmware release supporting all LaserJet models built off of trunk, with printer capabilities being established at runtime in an XML configuration file.
Four years later, they had one codebase supporting all twenty-four HP LaserJet product lines being developed on trunk. Gruver admits trunk-based development requires a big mindset shift. Engineers thought trunk-based development would never work, but once they started, they couldn’t imagine ever going back. Over the years we’ve had several engineers leave HP, and they would call me to tell me about how backward development was in their new companies, pointing out how difficult it is to be effective and release good code when there is no feedback that continuous integration gives them.
However, trunk-based development required them to build more effective automated testing. Gruver observed, “Without automated testing, continuous integration is the fastest way to get a big pile of junk that never compiles or runs correctly.” In the beginning, a full manual testing cycle required six weeks.
In order to have all firmware builds automatically tested, they invested heavily in their printer simulators and created a testing farm in six weeks—within a few years two thousand printer simulators ran on six racks of servers that would load the firmware builds from their deployment pipeline. Their continuous integration (CI) system ran their entire set of automated unit, acceptance, and integration tests on builds from trunk, just as described in the previous chapter. Furthermore, they created a culture that halted all work anytime a developer broke the deployment pipeline, ensuring that developers quickly brought the system back into a green state.
Automated testing created fast feedback that enabled developers to quickly confirm that their committed code actually worked. Unit tests would run on their workstations in minutes, three levels of automated testing would run on every commit as well as every two and four hours. The final full regression testing would run every twenty-four hours. During this process, they:
This level of productivity could never have been supported prior to adopting continuous integration, when merely creating a green build required days of heroics. The resulting business benefits were astonishing:
What Gruver’s experience shows is that, after comprehensive use of version control, continuous integration is one of the most critical practices that enable the fast flow of work in our value stream, enabling many development teams to independently develop, test, and deliver value. Nevertheless, continuous integration remains a controversial practice. The remainder of this chapter describes the practices required to implement continuous integration, as well as how to overcome common objections.
As described in the previous chapters, whenever changes are introduced into version control that cause our deployment pipeline to fail, we quickly swarm the problem to fix it, bringing our deployment pipeline back into a green state. However, significant problems result when developers work in long-lived private branches (also known as “feature branches”), only merging back into trunk sporadically, resulting in a large batch size of changes. As described in the HP LaserJet example, what results is significant chaos and rework in order to get their code into a releasable state.
Jeff Atwood, founder of the Stack Overflow site and author of the Coding Horror blog, observes that while there are many branching strategies, they can all be put on the following spectrum:
Atwood’s observation is absolutely correct—stated more precisely, the required effort to successfully merge branches back together increases exponentially as the number of branches increase. The problem lies not only in the rework this “merge hell” creates, but also in the delayed feedback we receive from our deployment pipeline. For instance, instead of performance testing against a fully integrated system happening continuously, it will likely happen only at the end of our process.
Furthermore, as we increase the rate of code production as we add more developers, we increase the probability that any given change will impact someone else and increase the number of developers who will be impacted when someone breaks the deployment pipeline.
Here is one last troubling side effect of large batch size merges: when merging is difficult, we become less able and motivated to improve and refactor our code, because refactorings are more likely to cause rework for everyone else. When this happens, we are more reluctant to modify code that has dependencies throughout the codebase, which is (tragically) where we may have the highest payoffs.
This is how Ward Cunningham, developer of the first wiki, first described technical debt: when we do not aggressively refactor our codebase, it becomes more difficult to make changes and to maintain over time, slowing down the rate at which we can add new features. Solving this problem was one of the primary reasons behind the creation of continuous integration and trunk-based development practices, to optimize for team productivity over individual productivity.
Our countermeasure to large batch size merges is to institute continuous integration and trunk-based development practices, where all developers check in their code to trunk at least once per day. Checking code in this frequently reduces our batch size to the work performed by our entire developer team in a single day. The more frequently developers check in their code to trunk, the smaller the batch size and the closer we are to the theoretical ideal of single-piece flow.
Frequent code commits to trunk means we can run all automated tests on our software system as a whole and receive alerts when a change breaks some other part of the application or interferes with the work of another developer. And because we can detect merge problems when they are small, we can correct them faster.
We may even configure our deployment pipeline to reject any commits (e.g., code or environment changes) that take us out of a deployable state. This method is called gated commits, where the deployment pipeline first confirms that the submitted change will successfully merge, build as expected, and pass all the automated tests before actually being merged into trunk. If not, the developer will be notified, allowing corrections to be made without impacting anyone else in the value stream.
The discipline of daily code commits also forces us to break our work down into smaller chunks while still keeping trunk in a working, releasable state. And version control becomes an integral mechanism of how the team communicates with each other—everyone has a better shared understanding of the system, is aware of the state of the deployment pipeline, and can help each other when it breaks. As a result, we achieve higher quality and faster deployment lead times.
Having these practices in place, we can now again modify our definition of “done” (addition in bold text): “At the end of each development interval, we must have integrated, tested, working, and potentially shippable code, demonstrated in a production-like environment, created from trunk using a one-click process, and validated with automated tests.”
Adhering to this revised definition of done helps us further ensure the ongoing testability and deployability of the code we’re producing. By keeping our code in a deployable state, we are able to eliminate the common practice of having a separate test and stabilization phase at the end of the project.
Case Study
Continuous Integration at Bazaarvoice (2012)
Ernest Mueller, who helped engineer the DevOps transformation at National Instruments, later helped transform the development and release processes at Bazaarvoice in 2012. Bazaarvoice supplies customer generated content (e.g., reviews, ratings) for thousands of retailers, such as Best Buy, Nike, and Walmart.
At that time, Bazaarvoice had $120 million in revenue and was preparing for an IPO.§ The business was primarily driven by the Bazaarvoice Conversations application, a monolithic Java application comprised of nearly five million lines of code dating back to 2006, spanning fifteen thousand files. The service ran on 1,200 servers across four data centers and multiple cloud service providers.
Partially as a result of switching to an Agile development process and to two-week development intervals, there was a tremendous desire to increase release frequency from their current ten-week production release schedule. They had also started to decouple parts of their monolithic application, breaking it down into microservices.
Their first attempt at a two-week release schedule was in January of 2012. Mueller observed, “It didn’t go well. It caused massive chaos, with forty-four production incidents filed by our customers. The major reaction from management was basically ‘Let’s not ever do that again.’”
Mueller took over the release processes shortly afterward, with the goal of doing bi-weekly releases without causing customer downtime. The business objectives for releasing more frequently included enabling faster A/B testing (described in upcoming chapters) and increasing the flow of features into production. Mueller identified three core problems:
Mueller concluded that the monolithic Conversations application deployment process needed to be stabilized, which required continuous integration. In the six weeks that followed, developers stopped doing feature work to focus instead on writing automated testing suites, including unit tests in JUnit, regression tests in Selenium, and getting a deployment pipeline running in TeamCity. “By running these tests all the time, we felt like we could make changes with some level of safety. And most importantly, we could immediately find when someone broke something, as opposed to discovering it only after it’s in production.”
They also changed to a trunk/branch release model, where every two weeks they created a new dedicated release branch, with no new commits allowed to that branch unless there was an emergency—all changes would be worked through a sign-off process, either per-ticket or per-team through their internal wiki. That branch would go through a QA process, which would then be promoted into production.
The improvements to predictability and quality of the releases were startling:
Mueller further described how successful this effort was:
We had such success with releases every two weeks, we went to weekly releases, which required almost no changes from the engineering teams. Because releases became so routine, it was as simple as doubling the number of releases on the calendar and releasing when the calendar told us to. Seriously, it was almost a non-event. The majority of changes required were in our customer service and marketing teams, who had to change their processes, such as changing the schedule of their weekly customer emails to make sure customers knew that feature changes were coming. After that, we started working toward our next goals, which eventually led to speeding up our testing times from three plus hours to less than an hour, reducing the number of environments from four to three (Dev, Test, Production, eliminating Staging), and moving to a full continuous delivery model where we enable fast, one-click deployments.
Trunk-based development is likely the most controversial practice discussed in this book. Many engineers will not believe that it’s possible, even those that prefer working uninterrupted on a private branch without having to deal with other developers. However, the data from Puppet Labs’ 2015 State of DevOps Report is clear: trunk-based development predicts higher throughput and better stability, and even higher job satisfaction and lower rates of burnout.
While convincing developers may be difficult at first, once they see the extraordinary benefits, they will likely become lifetime converts, as the HP LaserJet and Bazaarvoice examples illustrate. Continuous integration practices set the stage for the next step, which is automating the deployment process and enabling low-risk releases.


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--- layout: post authors: ["Greg Wilson"] title: "Instructor Training Checkout Procedure" date: 2015-12-18 time: "01:30:00" tags: ["Instructor Training"] ---
After a lot of discussion, we have come up with a new procedure for completing instructor training for Data Carpentry and Software Carpentry. Its goals are to ensure that people are familiar with the lesson material while introducing them to our community. The steps are:
After completing the instructor training course, the trainee picks one of the core lessons (described below) to focus on.
She then submits a new exercise for that lesson (also described below).
The trainee then takes part in an hour-long group discussion of that lesson led by an experienced instructor. The trainee is expected to have gone through the lesson before this session so that she can ask lots of pointed questions during that hour. If the mentor leading the session feels that the trainee is unprepared, she may be asked to do some more work and attend a future discussion session before moving on.
The trainee then signs up for a demonstration lesson session (described below). In that session, she is asked to teach a five-minute segment of her lesson chosen by the person running the session. Since she does not know in advance which five-minute segment she will be asked to teach, she must be ready to teach any part of the lesson. She may also then be asked some questions about the material covered in the instructor training course.
If all goes well, she gets her badge. If the examiner feels that she needs to do more work, she will be given feedback and asked to try again.
Notes:
The core lessons for Data Carpentry are those listed under Biology Workshop Lessons: spreadsheets, OpenRefine, SQL, programming with R, and programming with Python. The core lessons for Software Carpentry are the ones on the Unix shell, version control with Git, and programming with Python or R.
Exercises for Software Carpentry lessons must be submitted as pull requests on GitHub. Exercises for Data Carpentry lessons must be added to a Google Doc.
Demonstration sessions will be scheduled at regular times throughout the week, rather than on demand. Each session will be one hour long, so four or five people will be able to do their demonstrations and get feedback from the trainer and their peers.
This process won't be the last word in instructor training—we learn something new every time we run a class—but we believe it improves on what we're doing now, and that we can implement it with the resources we have. We will start running mentoring and checkout sessions in the second week of January; until then, best wishes from all of us for a happy New Year.


How can I get data refunds using Datagenie?
We will refund a part of data used on your favorite apps
installed in your phone via Datagenie. From this refunded data, you can
data recharge your prepaid mobile number by selecting your operator and
circle.
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Click on 'Get free paytm cash' button and invite your friends
via whatsapp/facebook/email or any other channel. On every friend who
joins through your invite gives you an opportunity to earn some
predefined paytm cash which will be credited to your Datagenie wallet.
You can transfer this paytm cash to your paytm account.
Can I use this to recharge my family/friends Mobile Number?
Yes, you can data recharge your family/friends mobile number if they are also prepaid connection users.
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Mobile data recharges are dependent on your operator and
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IT Open House on Monday
by Staff ReporterMADURAI
The Chief Commissioner of Income Tax, Rajiv Vijay Nabar, will hold an Open House for taxpayers and their representatives, professionals and general public at the Income Tax Office on V.P.Rathinasamy Road, B.B.Kulam in Madurai between 3 p.m. and 5 p.m. on Monday.
This would be the third Open House for 2019-2020. Representations and grievances submitted at the Open House would be redressed on priority. A majority of grievances received during the Open House held on October 29 were resolved, said the release.


raw surf and turf
Story
Beef Carpaccio: raw beef tenderloin flattened and served with salt pepper and olive oil. There's something about gnawing on raw beef that just feels so satisfyingly primal. It reminds me of one winter when pops brought home a buck and my brother, LJ, and I helped him break it down.  LJ and I decided to take advantage of the heat and started roasting and eating some of the venison we were cutting. It was delicious -- I ended up passing out ten minutes later from the combination of the smell of blood and the history lesson my brother was giving about Mayan sacrifices -- but that's beside the point. Red meat, as close to nature as possible, is always the best. Period. Hands down.
The same can be said for fish. The most delicious fish I've tasted has tended to be raw or close to it. The flavors and smells of really fresh fish are the antithesis of that rotting "fishy" smell that can drive so many people away from fish. Tuna tartare is simple: some fresh raw tuna, diced with acid, oil, salt, and any other desired flavors.
We decided to take these two simple interpretations of surf and turf and see if we could get them to harmonize. As "fancy" as this might look it's really simple to prepare. Enjoy.
Recipe
Raw Surf and Turf
February 9, 2014
Tuna Tartare and Beef Carpaccio with crispy shallots.
Prep time: (including 2 hours of freezer time)
Cook time: (everything's raw but the shallots!)
Total time:
Yield: 2 servings
Ingredients:
Beef Tenderloin (as fresh as possible): 1/3 lbAlbacore or Yellowfin Tuna (as fresh as possible): 1/4 lb
Kosher Salt: to taste
Sesame Seeds: 1 tsp
Serrano or Jalapeño Pepper: 1/8 pepper
Sesame Oil: 1/2 tsp
Lime: 1/4
Shallot: 1/2 large
Canola Oil: 2 T
Chives: small bunch
Mayo: 1 T
Sriracha: 1 tsp
Directions:
2. Combine mayo and sriracha, reserve for later.
3. Remove beef from freezer. Slice as thin as possible to create large flat disks.
4. Place beef disks between plastic wrap and pound until paper thin but not separating at the fat seams (these will be pretty delicate so be careful). Place beef sheets back in freezer between plastic wrap.
5. Finely chop shallots and fry in canola oil on high heat. Remove as soon as they reach a dark brown and place on paper towel to absorb oil.
6. Carefully chop tuna into quarter inch cubes - try to handle these as little as possible, you are looking for cubes not mush and they break down pretty easily.
7. Chop chile pepper and half of chives very finely. Combine these with tuna, shallots, juice from the lime, sesame oil, salt, sesame seeds. Taste this and adjust flavors to your liking.
8. Remove beef sheets from freezer. Place a chive onto your working area. Top this with a sheet of beef. Spoon about a tablespoon of the tuna mixture into the center of the beef.
9. You have two options here, you can garnish with spicy mayo and sesame seeds and serve open like the photo in the recipe, or you can roll and tie with the chive like those seen at the top of the page.


Stack comments in Retro are a compact form, using short codes in place of actual words. These codes are listed in the next section.
A typical comment for a function that takes two arguments and leaves one will look like:
( xy-z )
In a few cases, functions may consume or leave a variable number of arguments. In this case, we denote it like:
( n-n || n- )
There are two other modifiers in use. Some functions have different compile-time and run-time stack use. We prefix the comment with C: for compile-time, and R: for run-time actions.
If not specified, the stack comments are for runtime effects. Functions with no C: are assumed to have no stack impact during compilation.
| Code | Used for |
|---|---|
| x, y, z, n | Generic numbers |
| q, r | Quotient, Remainder (for division) |
| " | Function parses for a string |
| q | Quote |
| a | Address |
| $ | Zero-terminated string |
| c | ASCII character |
| f | Flag |
| t | Type constant |
| ... | Variable number of values on stack |
============== Document Title ============== ------------------------------------------ Document Subtitle, or Major Division Title ------------------------------------------ Section ======= Subsection ---------- Sub-Subsection `````````````` Sub-Sub-Subsection ..................


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@* createAccount Template File *@ @(userForm :Form[NewUser])(implicit request: MessagesRequestHeader, flash:Flash)
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| Modular Application Framework Components | |
Overview |
Top Previous Next |
|
The TmafHookManager manages all published code with a MAF-Application. To do that it uses a Dynamic Function Table (DFT) to store, which function is in which module and how are they linked together.
The DFT contains a list a Dynamic Functions (Hooks), which contain 1-n code fragments (SubHooks). Every SubHook represents published code in a module. Published code can be real code written inside a unit in a module or class information connected to a SubHook that can be created automatically by the TmafModuleController.
SubHooks contain the following information :
Code can be executed only by a TmafHookClient and can call a Hook (with all its SubHooks) or a specific function (UniqueID) within a Hook. Therefore one can create Hooks that should be called as a whole, but also Hooks, that just collect single functions called only through their UniqueID. The latter is great to build Tool-Hooks, which contain a number of functions where the programmer is sure that they never need a second function added. It also improves the readability of the DFT in the DFT Editor, as the amount of Hooks decrease. |


--- layout: post status: publish published: true title: Android > Muti Project with library in Android Studio author: display_name: hojunbaek login: hojunbaek email<EMAIL_ADDRESS>url: '' author_login: hojunbaek author_email<EMAIL_ADDRESS>wordpress_id: 1882 wordpress_url: http://ezcocoa.com/?p=1882 date: '2013-09-12 14:51:52 +0900' date_gmt: '2013-09-12 14:51:52 +0900' categories: - Android tags: [] comments: [] ---
Configure
Version : Android Studio 0.2.8
Situation : 2 More Projects and 2 More Librarys (Jar file and Java lib Project)
Problem : Eclipse에서 gradle 파일을 생성 후 Android Studio 에서 Import. (정상 과정) 하지만 컴파일 시 java.lang.NoClassDefFoundError 에러 발생.
Why the problem happened?
문제는 Java SDK 기본으로 1.7 Version이 선택됨. (1.6 Version으로 변환 후 정상 작동)
How to change JDK Version to 1.6?
/System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Home
주의사항
가끔 씩 SDK Android 4.2.2 Platform' is invalid. android studio 에러가 발생한다 이 에러가 발생하면 프로젝트를 아래와 같이 재구성해야 한다.
Try this:
Close IDE
Remove .idea folder and all .iml files in the project.
Restart the IDE and re-import the project.
Original post: https://code.google.com/p/android/issues/detail?id=59018
Next Problem
프로젝트 안에 so 파일 즉 NDK를 사용하였는데 옴기고 난 후 문제가 발생하였다.
Couldn't load lib-file: findLibrary returned null
I'm not particularly familiar with IntelliJ or Gradle but I have figured it out. I used ZBarAndroidSDK-0.2.
Copy the contents of the ZBar SDK libs/ folder into your project's libs/ folder.
Modify your build.gradle (see below) to make sure the jar and native libs are included in your APK.
To make IntelliJ aware of ZBar, add zbar.jar in your project structure. To do this, go to File > Project Structure > Libraries > + Sign > Java and find zbar.jar with the file picker. Add it to your project.
Add the following to your build.gradle (making sure to keep whatever other dependencies you've got):
dependencies {
compile files('libs/android-support-v4.jar')
compile files('libs/zbar.jar')
}
task copyNativeLibs(type: Copy) {
from(new File('libs')) { include '**' }
into new File(buildDir, 'native-libs')
}
tasks.withType(Compile) { compileTask -> compileTask.dependsOn copyNativeLibs }
clean.dependsOn 'cleanCopyNativeLibs'
tasks.withType(com.android.build.gradle.tasks.PackageApplication) { pkgTask ->
pkgTask.jniDir new File(buildDir, 'native-libs')
}
My build.gradle is based on this gist: https://gist.github.com/khernyo/4226923.


Taken at The Lakes in Las Vegas by Rob. This photo shoot was for a calendar called Tailwaggers.
Taken in Phoenix, Arizona. You can see me getting photographed by Andy Hartmark.
Photograph was taken in Goodyear, Arizona by Chad Hatfield. It's really an amazing work of art and I love it!
Taken March 4th, 2010, this photo shoot is one of my favorites. It was taken inside of The Congress Plaza Hotel in Chicago, Illinois. I will never forget how beautiful the windy city is.
Taken at Red Rock in Las Vegas by David Rice. This photo shoot is special to me because it marks the beginning of my modeling career.


Uber ban: London business groups mourn 'dark day'
by Stefan BosciaBusiness groups have slammed the decision to not grant Uber a new operating licence in London, with one think tank calling it a “total disgrace”.
Transport for London (TfL) announced this morning it had not renewed Uber’s licence, after a long-running battle over the company’s safety record.
Read more: TfL bans Uber from London after ‘pattern of failures” leaves customers at risk
The transport body found a number of “safety breaches”, which made it “not fit and proper to operate”.
TfL said Uber had unlicensed operators upload photos on other people’s accounts, leading to at least 14,000 trips involving these fraudulent drivers.
Uber’s UK general manager Jamie Heywood said the company will appeal the decision.
The company can continue to operate in the meantime.
The Institute of Economic Affairs’ (IEA) director of trade Shanker Singham said the decision marked a “dark day for competition”.
“Uber – and other platforms like it – give consumers real alternatives to the monopoly enjoyed by London’s black cab industry,” he said.
“As with any regulatory crackdown on new entrants, this action will harm London’s consumers, particularly damaging the least-well-off who cannot afford the high cost of black cabs.”
The licensing decision was supported by mayor of London Sadiq Khan, who said: “TfL have identified a pattern of failure by Uber that has directly put passengers’ safety at risk.”
Matt Lesh, director of research at the Adam Smith Institute, said Khan had “sided with the vested interests of black cabs”.
“Khan’s decision to strip Uber of their operating licence on spurious grounds is a total disgrace,” he said.
Uber was originally banned from operating in London in 2017, sparking a legal challenge.
It was granted a 15-month licence in 2018 with a set of regulatory conditions by the chief magistrate.
The company’s licence was renewed for just two months in September, after TfL said it needed to see more evidence that Uber could “ensure passenger safety”.
The transport body implemented a number of restrictions on the tech giant during the two-month probationary period, including more stringent driver and licence checks.
The Entrepreneurs Network said Uber had done enough to improve safety measures and should have been granted a new licence.
A spokesperson for the group said: “The violations uncovered by TfL, while troubling, have been addressed through stricter checks on drivers.
“Furthermore, if you take into account Uber’s other safety features, it’s likely they compare favourably to other modes of transport in London and definitely favourably compared to transport in London ten years ago before Uber.”
Read more: Uber rivals sharpen knives after the app’s London ban
The CBI was more diplomatic in its response.
CBI chief policy director Matthew Fell said: “TfL of course have to be confident around safety issues, so we’d encourage both sides to continue the dialogue to determine what changes are required in order that Uber’s customers can continue to enjoy the service in the long term.”


This example shows how a legacy checkout form can be replaced by a Payment Request
- only for browsers that support it - using:
if (window.PaymentRequest) { ... }
.
For the purposes of the demo, imagine you have chosen an item and now you need to
check out. Please note that no payments will be taken, this is just a front-end
demo. If you would like to try entering card details, you can use dummy data,
for example the card number 4111 1111 1111 1111
.
Payment Request success. Demo complete. No payment has been taken.
The Payment Request API is unsupported or was cancelled or failed, so here we can proceed with our legacy web form (not implemented for this demo).


Diógenes Ballester: Manifesto of an Arteologist
Introduction
Any artist, in the process of creating art, is also a student and scholar of art, history and philosophy. An artist who takes this multiplicity of roles seriously might be called an “arteologist.” In this process, the arteologist is like an egregor or a griot, helping to ferment and mature the connections which in contemporary art are linked to cultural identity, historical meaning and spiritual transformation.
I like to call myself an “arteologist”.
I am an immigrant from Puerto Rico to the United States who has lived in Paris and other parts of the post-industrial metropolises of the Western world and developing industrial metropolises of the East. The stages of globalization I have encountered through these migrations have reconfigured my artistic language. It has become a language that speaks of fragmentation and the displacement of the emigrant, about a multicultural diaspora that is re-lived daily by millions of exiles, refugees and migrant workers, constantly mutating, multipolar individuals.
My work examines trans-Caribbean and transnational history in order to help locate and rebuild bridges to our past that can anchor us in our collective future. This process has shown me the need for a new mythology for the 21st century, and even suggests some possible outlines for that new mythology.
A globalized 21st century, multipolar mythology must be built on the collective mind of cross-cultural communication. The flows of energy and cultural memory that circulate in the world of the Caribbean, those syncretic processes of struggle and reaffirmation that connect all of us, the stream of sounds that we can derive from listening to ancient codes, could provide a model for the development of this new mythology.
I want to speak here of globalization, as the deployment of a system of production of symbols and material goods. The powers of symbolic production, religious, artistic, political and intellectual, continue to be subjected and integrated into the economic apparatus according to its own industrial and commercial criteria. One key way this interaction takes place is via the new media, which are predominantly used for economic power.
However, new media lets other voices co-create and transmit experiences to an audience beyond the physical space inhabited. The resulting cross-cultural fertilization afforded to those of us who function as arteologist allows us to not only create and deploy symbols of meaning, creativity, and imagination, but also challenges exclusions to the current dominant production of knowledge and reality.
An Arteologist’s Manifesto
The arteologist is like the new-born phoenix bird that saved the remains of the ancestors in a sacred place before beginning the journey into a new era. The arteologist meditates on reality by watching and listening to truth rooted in the past, present and future.
Artists always research and investigate their art forms. They formally or informally study their artistic concepts and aesthetic processes, including the content and the message they wish to convey.
The investigation of cultural remains can take various forms: experienced, observed or empirical. And while some of the less empirical methods are related to subjectivity, we know that empiricism itself has assumed or imposed a worldview.
Alongside the skills of an investigator, there is the creative and artistic aspect of the work. The conception of art and its product needs to enhance the stories told and be a part of its meaning.
The installation is not just an object. It includes all the energy that flows around the construction. It mobilizes, articulates, and takes different forms. Part of my creative process is experiencing feelings and sensing the energy passing through my mind, body, and spirit. While I am creating these installations, I connect with the fluidity and dynamics involved in the gesture of telling a story, the story of intercultural meaning related to the fundamental elements of life. I feel like my being is flowing; I perceive in and with the whole space. This kind of perception relates to the senses and is part of a vision that involves theory and praxis.
The objective is to translate into an aesthetic perception a dialectical vision that follows the breathing of our history.
Conclusion
One function of the arteologist is to discover, investigate, extrapolate meaning, and offer a reconfigured perspective on what has been left behind as artifacts. I hope that this presentation has given you a sense of my work as an arteologist.
I have been blessed as a Puerto Rican with a rich religious, cultural, political, and familial heritage. This heritage invites curiosity and investigation. These are the factors that interest me and through which I express my creativity. These are the memories embedded in these installation art projects.
My work attempts to apprehend, reconfigure, and re-contextualize my/our histories. It manifests as integrated altarpieces that depict layers of experiences and culture. These installations, like my quest, are offered not as a product but rather as a process. Deconstructing the threads of religion, history, art, culture, politics, and oral histories strengthens us as part of the chain of history. Becoming a link in that chain is as important as cutting the chains of slavery.


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The dialog Factoring-with-a-Hint offers
two different modes, which can be chosen in the upper part of the window:
1. Enter the RSA-modulus N,
the bit length of p
and the known part P
can be entered manually.
2. Enter the bit length of N and of p
and let the program generate a random example.
Steps to proceed:
Depending on the above mentioned choice either
N itself or the bit length of
N can be entered.
The group "Base of numbers" allows to change the representation
of N,
p and P.
In both cases the bit length of p
has to be entered (for this attack the bit length of
p has to be exactly known!).
Consequently it is selected, whether the upper most significant or least
significant bits of p
are known.
Depending on the chosen mode either the known
part P
of p is
entered manually or P
is generated by entering the number of bits known and clicking "Generate
example". Note:
When entering P manually
it has to be identical with
p in its binary representation in e.g. 40 bits. If you choose
option 1 you may want to change to the binary or hexadecimal representation
every once in a while to check, whether the bits are indentical!
The attack is started by clicking the "Start" button.
The section "Attack" provides information about the elapsed time
of the running attack and how many lattice reductions have been completed. After
the attack is finished, the found factors p
and q are
displayed. Furthermore the number of required bits (n
/ 4+1), which is calculated depending on N,
is shown. From the number of known bits also follows the size of the latticebase:
the more bits of one of the factors are known, the smaller the lattice can be.
Example:
Proceed according to option 1:
Attack on a RSA modulus N
with a length of 200 bits:
The attacker enters the publicly known value for N: N =<PHONE_NUMBER>568180272388944445745883306779673638843521865435523
In most of the cases both factors have exactly half the length of N.
The same holds here:
Bit length of p:
100
The attacker selects, that the upper most significant bits of p
are known.
He enters P =<PHONE_NUMBER>01422049670156.
The attack is started by clicking "Start".
Dialog corresponding to this example:
Annotations:
How much of the factor p
of N has
to be known does not depend on the size of
p but on the size of N.
To be able to determine N
of a bit length of n, at least
(n/4) +1 bit have to be known, but this results in a lattice
the size of n,
which is hard to solve from a size of n=128.
When P
and p match
in either their decimal or hexadecimal representation they rarely match exactly
in their binary representation. An attack, however, can only succeed if they
exactly match binary.


Smoke shrouds Sydney's skyline as wildfires burn nearby
by The Associated Press, StaffSYDNEY, Australia -- Thick smoke from wildfires has shrouded Sydney and its surrounding areas with health experts warning residents with medical conditions to remain indoors.
The Sydney skyline was barely visible with air quality in some parts of the city reaching over hazardous levels early Tuesday.
Shane Fitzsimmons, the state's rural fire commissioner, says firefighters would be challenged by high temperatures and wind conditions.
Most of the coastal areas of the New South Wales are under very high fire danger with 48 fires burning across the state.
Fires have destroyed 577 homes in New South Wales during the wildfire season, which peaks during the Southern Hemisphere summer but has started early after an unusually warm and dry winter.


This document contains the results of running metrics for codepro.metrics at 10/26/14 3:08 PM.
This document contains the results of running metrics for codepro.metrics at 10/26/14 3:08 PM.
| Metric Name | Value |
|---|---|
| Abstractness | 10% |
| Average Block Depth | 1.06 |
| Average Cyclomatic Complexity | 1.32 |
| Average Lines Of Code Per Method | 8.46 |
| Average Number of Constructors Per Type | 0.68 |
| Average Number of Fields Per Type | 0.58 |
| Average Number of Methods Per Type | 2.20 |
| Average Number of Parameters | 0.73 |
| Comments Ratio | 3.6% |
| Efferent Couplings | 235 |
| Lines of Code | 8,809 |
| Number of Characters | 443,169 |
| Number of Comments | 320 |
| Number of Constructors | 178 |
| Number of Fields | 365 |
| Number of Lines | 12,317 |
| Number of Methods | 572 |
| Number of Packages | 63 |
| Number of Semicolons | 5,456 |
| Number of Types | 260 |
| Weighted Methods | 995 |


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These examples have moved to storybook
https://solid.github.io/solid-ui/examples/storybook/?path=/docs/log--simple-message


It's built on JavaScript (ES6) and WebGL 2.0.
Client side API. No server required.
The programmer only needs to provide a set of options to create the thematic map.


‘It’s a wind up’: Mark Bosnich sees red over claims NBL is overtaking the A-League
by Jack AustinMark Bosnich has fired back at suggestions the NBL is overtaking the A-League in terms of interest from the Australian public and expressed his bemusement as to why the two are even being compared.
The dig at the A-League came on Sunday evening when an article was published in the Australian by sports reporter Will Swanton, titled: ‘NBL slam-dunks crowd with Sydney Kings as A-League scores own goal’.
The article was referring to the record-breaking crowd of 17,514 spectators to watch the Kings defeat the Illawarra Hawks at the Qudos Bank Arena.
Stream every game of the 2019/20 Hyundai A-League season LIVE & On-Demand with Kayo Sports. Get your 14 day free trial & start streaming instantly>
Swanton claimed the NBL was “changing the Australian sporting landscape” while “the A-League is floundering from poor crowd and television figures, no real superstars and a standard of play far below the rest of the world’s domestic leagues.”
He also pointed out NBL owner Larry Kestelman rating his product as the second-best league in the world behind the NBA, while the A-League is nowhere near that standard.
But Bosnich hit back on Monday’s edition of Bill & Boz, calling out Swanton for his lack of knowledge about the A-League and expressing his disappointment that one Australian sport is being put down to make another look better.
“If he’s such an expert then tell us about the standard of play, then Will,” Bosnich said. “Because obviously you’re so qualified, you know about the standard of play.
“So, number one, you’re completely wrong about that.
“In terms of the crowds, the crowds are fine – and this is still without the A-League clubs having their hands on the steering wheel, it’s still yet to be put over.
 So don’t talk about television viewers.
“The bottom line is that the NBL’s average crowd last year was 6,000 people. Do you ever actually hear people from football bringing that up?!
“I would say it’s a great league, we’re really, really happy, we think Andrew Bogut is fantastic for the Australian team, the loan system they’ve implemented is absolutely fantastic with the likes of LaMelo Ball and even more American players who will be coming down here, we should be doing something similar to that…
“So for them to come out with that, I can only see it as one thing: I’ve seen it for the last 10 years I’ve been back, it’s a wind-up.”


Over the summer of 2016, I attended an REU at NC State University. Over the summer, we partnered up with a company, ABB, to conduct a pre-study about addressing the issue with spreadsheets in the company. You can download the poster here.
The goal of the project was to make a full interpreter build off of the grammar described in the section below. The interpreter should be able to parse and evaluate mathematical expressions.
<bexpr> ::= <expr> ;
<expr> ::= <term> <ttail>
<ttail> ::= <add_sub_tok> <term> <ttail> | e
<term> ::= <stmt> <stail>
<stail> ::= <mult_div_tok> <stmt> <stail> | e
<stmt> ::= <factor> <ftail>
<ftail> ::= <compare_tok> <factor> <ftail> | e
<factor> ::= <expp> ^ <factor> | <expp>
<expp> ::= ( <expr> ) | <num>
<add_sub_tok> ::= + | -
<mul_div_tok> ::= * | /
<compare_tok> ::= < | > | &= | >= | ! = | ==
<num> ::= {0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9}+
This project was written in Java and was made in order to learn more about networking as well as the DNS protocol as specified in RFC 1035. The program was command line based but also came with a GUI created in Java Swing. Recently, I ported this over to C which you can find on my GitHub.
If you aren't familiar with the game of S.O.S., here is a link to the rules. This project was a networked, command line version of the game that supported a server where an N amount of players can play. This project was also written in Java.
Juke Meister was a year long project for my 2nd year software engineering class. We worked in a team of 4 and went through the entire process following the development lifecycle. We had 4 milestones in all, where the 4th and final one was implementation. Juke Meister is a touch-compatible juke box software running in Java that uses Java Swing as the GUI. It has two views, the user and the administrator view, where, with a password, administrators can log in to manage the machine. User's had to pay money to play music, but to emulate that, we attached the coin insert function to keyboard input.
Route Recorder is an Android application that tracks where the user has traveled while the app is running and traces the user's route with a line. Route's can be saved and loaded as well as tracking distance travelled. This app is requires GPS or a cell signal for location tracking.
If you'd like to know more about any of these projects, let me know!


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| I l@ve RuBoard |
B.2 Book Examples DistributionThis section briefly discusses the book's example source code distribution, and covers example usage details. B.2.1 The Book Examples PackageThe Examples\PP2E CD directory is a Python module package that contains source code files for all examples presented in this book (and more). The PP2E package in turn contains nested module packages that partition the example files into subdirectories by topic. You can either run files straight off the CD, or copy the PP2E directory onto your machine's hard drive (copying over allows you to change the files, and lets Python store their compiled bytecode for faster startups). Either way, the directory that contains the PP2E root must generally be listed on the Python module search path (normally, the PYTHONPATH environment variable). This is the only entry that you must add to the Python path, though; import statements in book examples are always package import paths relative to the PP2E root directory unless the imported module lives in the same directory as the importer. Also in the examples package, you'll find scripts for converting example files' line-feeds to and from Unix format (they are in DOS format on the CD--see http://examples.oreilly.com/python2), making files writable (useful after a drag-and-drop on Windows), and more. See the README files at the top of the Examples and PP2E directory trees for more details on package tree usage and utilities. B.2.2 Running the Demo Launcher ScriptsThe top level of the CD's Examples\PP2E package (see http://examples.oreilly.com/python2) includes Python self-configuring scripts that can be run to launch major book examples, even if you do not configure your environment. That is, they should work even of you don't set your PATH or PYTHONPATH shell variables. These two scripts, PyDemos and PyGadgets, are presented in Chapter 8, and described more fully in both this book's Preface and the CD's README files (see http://examples.oreilly.com/python2). In most cases, you should be able to run these scripts right off the book's CD by double-clicking on them in a file explorer GUI (assuming Python has been installed, of course). |
| I l@ve RuBoard |


Model N Ant Tasks
DbProgress
DbReport
DiffReport
FileInfo
Hostname
ListDep
Apache Ant Tasks
Overview of Ant Tasks
Core Tasks
Optional Tasks
Library Dependencies
Third Party Ant Tasks
AntContrib


--- title: "June Gardening Tips" layout: "default" ---
Conserve water by watering lawns only before 10:00am or after 6:00pm.
Set out all warm season vegetable and annual flower seedlings or plant seeds now.
Watch for slug and snail damage on plants and use prepared bait or other techniques to control the pests.
Continue to apply mulch to garden and vegetable beds. The mulch will help to conserve water and improve soil texture as the mulch decomposes.
Trim dead and dying leaves of spring flowering bulbs.
Grasshopper damage will begin to show on tender young plants. If insecticides are used for control, make sure that directions are closely followed to prevent harm to bees and other beneficial insects.
As summer temperatures climb, don't over-fertilize cool season turf grasses. These grasses perform best during cooler weather, and applying fertilizer in mid summer heat does not benefit turf development.
Control annual weeds before they mature to prevent them from setting and dispersing seed.
Visit Red Butte Garden to see the variety of plants that are in flower during this time of year and take advantage of the many classes we have to offer.
Want to learn about native plants for water conservation, perennial and herb care, or how to use stone in the landscape? Check out our CALENDAR for useful classes!


Hey, I'm Hiram!
▸ I'm a software engineer working on the Design System team at
Compass!
▸ I'm passionate about the combination of computer science & design to create interactive experiences.
Currently:
▸ Learning 中文
▸ Exploring New York City
▸ Hype for Animal Crossing


| [ Team LiB ] |
4.10 FunctionsMost statements in a typical Python program are organized into functions. A function is a group of statements that executes upon request. Python provides many built-in functions and allows programmers to define their own functions. A request to execute a function is known as a function call. When a function is called, it may be passed arguments that specify data upon which the function performs its computation. In Python, a function always returns a result value, either None or a value that represents the results of its computation. Functions defined within class statements are also called methods. Issues specific to methods are covered in Chapter 5; the general coverage of functions in this section, however, also applies to methods. In Python, functions are objects (values) and are handled like other objects. Thus, you can pass a function as an argument in a call to another function. Similarly, a function can return another function as the result of a call. A function, just like any other object, can be bound to a variable, an item in a container, or an attribute of an object. Functions can also be keys into a dictionary. For example, if you need to quickly find a function's inverse given the function, you could define a dictionary whose keys and values are functions and then make the dictionary bidirectional (using some functions from module math, covered in Chapter 15): inverse = {sin:asin, cos:acos, tan:atan, log:exp} for f in inverse.keys( ): inverse[inverse[f]] = f The fact that functions are objects in Python is often expressed by saying that functions are first-class objects. 4.10.1 The def StatementThe def statement is the most common way to define a function. def is a single-clause compound statement with the following syntax: def function-name(parameters): statement(s) function-name is an identifier. It is a variable that gets bound (or rebound) to the function object when def executes. parameters is an optional list of identifiers, called formal parameters or just parameters, that are used to represent values that are supplied as arguments when the function is called. In the simplest case, a function doesn't have any formal parameters, which means the function doesn't take any arguments when it is called. In this case, the function definition has empty parentheses following function-name. When a function does take arguments, parameters contains one or more identifiers, separated by commas (,). In this case, each call to the function supplies values, known as arguments, that correspond to the parameters specified in the function definition. The parameters are local variables of the function, as we'll discuss later in this section, and each call to the function binds these local variables to the corresponding values that the caller supplies as arguments. The non-empty sequence of statements, known as the function body, does not execute when the def statement executes. Rather, the function body executes later, each time the function is called. The function body can contain zero or more occurrences of the return statement, as we'll discuss shortly. Here's an example of a simple function that returns a value that is double the value passed to it: def double(x): return x*2 4.10.2 ParametersFormal parameters that are simple identifiers indicate mandatory parameters. Each call to the function must supply a corresponding value (argument) for each mandatory parameter. In the comma-separated list of parameters, zero or more mandatory parameters may be followed by zero or more optional parameters, where each optional parameter has the syntax: identifier=expression The def statement evaluates the expression and saves a reference to the value returned by the expression, called the default value for the parameter, among the attributes of the function object. When a function call does not supply an argument corresponding to an optional parameter, the call binds the parameter's identifier to its default value for that execution of the function. Note that the same object, the default value, gets bound to the optional parameter whenever the caller does not supply a corresponding argument. This can be tricky when the default value is a mutable object and the function body alters the parameter. For example: def f(x, y=[ ]): y.append(x) return y print f(23) # prints: [23] prinf f(42) # prints: [23,42] The second print statement prints [23,42] because the first call to f altered the default value of y, originally an empty list [ ], by appending 23 to it. If you want y to be bound to a new empty list object each time f is called with a single argument, use the following: def f(x, y=None): if y is None: y = [ ] y.append(x) return y print f(23) # prints: [23] prinf f(42) # prints: [42] At the end of the formal parameters, you may optionally use either or both of the special forms *identifier1 and **identifier2. If both are present, the one with two asterisks must be last. *identifier1 indicates that any call to the function may supply extra positional arguments, while **identifier2 specifies that any call to the function may supply extra named arguments (positional and named arguments are covered later in this chapter). Every call to the function binds identifier1 to a tuple whose items are the extra positional arguments (or the empty tuple, if there are none). identifier2 is bound to a dictionary whose items are the names and values of the extra named arguments (or the empty dictionary, if there are none). Here's how to write a function that accepts any number of arguments and returns their sum: def sum(*numbers): result = 0 for number in numbers: result += number return result print sum(23,42) # prints: 65 The ** form also lets you construct a dictionary with string keys in a more readable fashion than with the standard dictionary creation syntax: def adict(**kwds): return kwds print adict(a=23, b=42) # prints: {'a':23, 'b':42} Note that the body of function adict is just one simple statement, and therefore we can exercise the option to put it on the same line as the def statement. Of course, it would be just as correct (and arguably more readable) to code function adict using two lines instead of one: def adict(**kwds): return kwds 4.10.3 Attributes of Function ObjectsThe def statement defines some attributes of a function object. The attribute func_name, also accessible as _ _name_ _, is a read-only attribute (trying to rebind or unbind it raises a runtime exception) that refers to the identifier used as the function name in the def statement. The attribute func_defaults, which you may rebind or unbind, refers to the tuple of default values for the optional parameters (or the empty tuple, if the function has no optional parameters). Another function attribute is the documentation string, also known as a docstring. You may use or rebind a function's docstring attribute as either func_doc or _ _doc_ _. If the first statement in the function body is a string literal, the compiler binds that string as the function's docstring attribute. A similar rule applies to classes (see Chapter 5) and modules (see Chapter 7). Docstrings most often span multiple physical lines, and are therefore normally specified in triple-quoted string literal form. For example: def sum(*numbers): '''Accept arbitrary numerical arguments and return their sum. The arguments are zero or more numbers. The result is their sum.''' result = 0 for number in numbers: result += number return result Documentation strings should be part of any Python code you write. They play a role similar to that of comments in any programming language, but their applicability is wider since they are available at runtime. Development environments and other tools may use docstrings from function, class, and module objects to remind the programmer how to use those objects. The doctest module (covered in Chapter 17) makes it easy to check that the sample code in docstrings is accurate and correct. To make your docstrings as useful as possible, you should respect a few simple conventions. The first line of a docstring should be a concise summary of the function's purpose, starting with an uppercase letter and ending with a period. It should not mention the function's name, unless the name happens to be a natural-language word that comes naturally as part of a good, concise summary of the function's operation. If the docstring is multiline, the second line should be empty, and the following lines should form one or more paragraphs, separated by empty lines, describing the function's expected arguments, preconditions, return value, and side effects (if any). Further explanations, bibliographical references, and usage examples (to be checked with doctest) can optionally follow toward the end of the docstring. In addition to its predefined attributes, a function object may be given arbitrary attributes. To create an attribute of a function object, bind a value to the appropriate attribute references in an assignment statement after the def statement has executed. For example, a function could count how many times it is called: def counter( ): counter.count += 1 return counter.count counter.count = 0 Note that this is not common usage. More often, when you want to group together some state (data) and some behavior (code), you should use the object-oriented mechanisms covered in Chapter 5. However, the ability to associate arbitrary attributes with a function can sometimes come in handy. 4.10.4 The return StatementThe return statement in Python is allowed only inside a function body, and it can optionally be followed by an expression. When return executes, the function terminates and the value of the expression is returned. A function returns None if it terminates by reaching the end of its body or by executing a return statement that has no expression. As a matter of style, you should not write a return statement without an expression at the end of a function body. If some return statements in a function have an expression, all return statements should have an expression. return None should only be written explicitly to meet this style requirement. Python does not enforce these stylistic conventions, but your code will be clearer and more readable if you follow them. 4.10.5 Calling FunctionsA function call is an expression with the following syntax: function-object(arguments) function-object may be any reference to a function object; it is most often the function's name. The parentheses denote the function-call operation itself. arguments, in the simplest case, is a series of zero or more expressions separated by commas (,), giving values for the function's corresponding formal parameters. When a function is called, the parameters are bound to these values, the function body executes, and the value of the function-call expression is whatever the function returns. <IP_ADDRESS> The semantics of argument passingIn traditional terms, all argument passing in Python is by value. For example, if a variable is passed as an argument, Python passes to the function the object (value) to which the variable currently refers, not the variable itself. Thus, a function cannot rebind the caller's variables. However, if a mutable object is passed as an argument, the function may request changes to that object since Python passes the object itself, not a copy. Rebinding a variable and mutating an object are totally different concepts in Python. For example: def f(x, y): x = 23 y.append(42) a = 77 b = [99] f(a, b) print a, b # prints: 77 [99, 42] The print statement shows that a is still bound to 77. Function f's rebinding of its parameter x to 23 has no effect on f's caller, and in particular on the binding of the caller's variable, which happened to be used to pass 77 as the parameter's value. However, the print statement also shows that b is now bound to [99,42]. b is still bound to the same list object as before the call, but that object has mutated, as f has appended 42 to that list object. In either case, f has not altered the caller's bindings, nor can f alter the number 77, as numbers are immutable. However, f can alter a list object, as list objects are mutable. In this example, f does mutate the list object that the caller passes to f as the second argument by calling the object's append method. <IP_ADDRESS> Kinds of argumentsArguments that are just expressions are called positional arguments. Each positional argument supplies the value for the formal parameter that corresponds to it by position (order) in the function definition. In a function call, zero or more positional arguments may be followed by zero or more named arguments with the following syntax: identifier=expression The identifier must be one of the formal parameter names used in the def statement for the function. The expression supplies the value for the formal parameter of that name. A function call must supply, via either a positional or a named argument, exactly one value for each mandatory parameter, and zero or one value for each optional parameter. For example: def divide(divisor, dividend): return dividend // divisor print divide(12,94) # prints: 7 print divide(dividend=94, divisor=12) # prints: 7 As you can see, the two calls to divide are equivalent. You can pass named arguments for readability purposes when you think that identifying the role of each argument and controlling the order of arguments enhances your code's clarity. A more common use of named arguments is to bind some optional parameters to specific values, while letting other optional parameters take their default values: def f(middle, begin='init', end='finis'): return begin+middle+end print f('tini', end='') # prints: inittini Thanks to named argument end='', the caller can specify a value, the empty string '', for f's third parameter, end, and still let f's second parameter, begin, use its default value, the string 'init'. At the end of the arguments in a function call, you may optionally use either or both of the special forms *seq and **dict. If both are present, the one with two asterisks must be last. *seq passes the items of seq to the function as positional arguments (after the normal positional arguments, if any, that the call gives with the usual simple syntax). seq may be any sequence or iterable. **dict passes the items of dict to the function as named arguments, where dict must be a dictionary whose keys are all strings. Each item's key is a parameter name, and the item's value is the argument's value. Sometimes you want to pass an argument of the form *seq or **dict when the formal parameters use similar forms, as described earlier under Section 4.10.2. For example, using the function sum defined in that section (and shown again here), you may want to print the sum of all the values in dictionary d. This is easy with *seq: def sum(*numbers): result = 0 for number in numbers: result += number return result print sum(*d.values( )) However, you may also pass arguments of the form *seq or **dict when calling a function that does not use similar forms in its formal parameters. 4.10.6 NamespacesA function's formal parameters, plus any variables that are bound (by assignment or by other binding statements) in the function body, comprise the function's local namespace, also known as local scope. Each of these variables is called a local variable of the function. Variables that are not local are known as global variables (in the absence of nested definitions, which we'll discuss shortly). Global variables are attributes of the module object, as covered in Chapter 7. If a local variable in a function has the same name as a global variable, whenever that name is mentioned in the function body, the local variable, not the global variable, is used. This idea is expressed by saying that the local variable hides the global variable of the same name throughout the function body. <IP_ADDRESS> The global statementBy default, any variable that is bound within a function body is a local variable of the function. If a function needs to rebind some global variables, the first statement of the function must be: global identifiers where identifiers is one or more identifiers separated by commas (,). The identifiers listed in a global statement refer to the global variables (i.e., attributes of the module object) that the function needs to rebind. For example, the function counter that we saw in Section 4.10.3 could be implemented using global and a global variable rather than an attribute of the function object as follows: _count = 0 def counter( ): global _count _count += 1 return _count Without the global statement, the counter function would raise an UnboundLocalError exception because _count would be an uninitialized (unbound) local variable. Note also that while the global statement does enable this kind of programming, it is neither elegant nor advisable. As I mentioned earlier, when you want to group together some state and some behavior, the object-oriented mechanisms covered in Chapter 5 are typically the best approach. You don't need global if the function body simply uses a global variable, including changing the object bound to that variable if the object is mutable. You need to use a global statement only if the function body rebinds a global variable. As a matter of style, you should not use global unless it's strictly necessary, as its presence will cause readers of your program to assume the statement is there for some useful purpose. <IP_ADDRESS> Nested functions and nested scopesA def statement within a function body defines a nested function, and the function whose body includes the def is known as an outer function to the nested one. Code in a nested function's body may access (but not rebind) local variables of an outer function, also known as free variables of the nested function. This nested-scope access is automatic in Python 2.2 and later. To request nested-scope access in Python 2.1, the first statement of the module must be: from _ _future_ _ import nested_scopes The simplest way to let a nested function access a value is often not to rely on nested scopes, but rather to explicitly pass that value as one of the function's arguments. The argument's value can be bound when the nested function is defined by using the value as the default for an optional argument. For example: def percent1(a, b, c): # works with any version def pc(x, total=a+b+c): return (x*100.0) / total print "Percentages are ", pc(a), pc(b), pc(c) Here's the same functionality using nested scopes: def percent2(a, b, c): # needs 2.2 or "from future import" def pc(x): return (x*100.0) / (a+b+c) print "Percentages are", pc(a), pc(b), pc(c) In this specific case, percent1 has a slight advantage: the computation of a+b+c happens only once, while percent2's inner function pc repeats the computation three times. However, if the outer function were rebinding its local variables between calls to the nested function, repeating this computation might be an advantage. It's therefore advisable to be aware of both approaches, and choose the most appropriate one case by case. A nested function that accesses values from outer local variables is known as a closure. The following example shows how to build a closure without nested scopes (using a default value): def make_adder_1(augend): # works with any version def add(addend, _augend=augend): return addend+_augend return add Here's the same closure functionality using nested scopes: def make_adder_2(augend): # needs 2.2 or "from future import" def add(addend): return addend+augend return add Closures are an exception to the general rule that the object-oriented mechanisms covered in Chapter 5 are the best way to bundle together data and code. When you need to construct callable objects, with some parameters fixed at object construction time, closures can be simpler and more effective than classes. For example, the result of make_adder_1(7) is a function that accepts a single argument and adds 7 to that argument (the result of make_adder_2(7) behaves in just the same way). You can also express the same idea as lambda x: x+7, using the lambda form covered in the next section. A closure is a "factory" for any member of a family of functions distinguished by some parameters, such as the value of argument augend in the previous examples, and this may often help you avoid code duplication. 4.10.7 lambda ExpressionsIf a function body contains a single return expression statement, you may choose to replace the function with the special lambda expression form: lambda parameters: expression A lambda expression is the anonymous equivalent of a normal function whose body is a single return statement. Note that the lambda syntax does not use the return keyword. You can use a lambda expression wherever you would use a reference to a function. lambda can sometimes be handy when you want to use a simple function as an argument or return value. Here's an example that uses a lambda expression as an argument to the built-in filter function: aList = [1,2,3,4,5,6,7,8,9] low = 3 high = 7 filter(lambda x,l=low,h=high: h>x>l, aList) # returns: [4, 5, 6] As an alternative, you can always use a local def statement that gives the function object a name. You can then use this name as the argument or return value. Here's the same filter example using a local def statement: aList = [1,2,3,4,5,6,7,8,9] low = 3 high = 7 def test(value, l=low, h=high): return h>value>l filter(test, aList) # returns: [4, 5, 6] 4.10.8 GeneratorsWhen the body of a function contains one or more occurrences of the keyword yield, the function is called a generator. When a generator is called, the function body does not execute. Instead, calling the generator returns a special iterator object that wraps the function body, the set of its local variables (including its parameters), and the current point of execution, which is initially the start of the function. When the next method of this iterator object is called, the function body executes up to the next yield statement, which takes the form: yield expression When a yield statement executes, the function is frozen with its execution state and local variables intact, and the expression following yield is returned as the result of the next method. On the next call to next, execution of the function body resumes where it left off, again up to the next yield statement. If the function body ends or executes a return statement, the iterator raises a StopException to indicate that the iterator is finished. Note that return statements in a generator cannot contain expressions, as that is a syntax error. yield is always a keyword in Python 2.3 and later. In Python 2.2, to make yield a keyword in a source file, use the following line as the first statement in the file: from _ _future_ _ import generators In Python 2.1 and earlier, you cannot define generators. Generators are often handy ways to build iterators. Since the most common way to use an iterator is to loop on it with a for statement, you typically call a generator like this: for avariable in somegenerator(arguments): For example, say that you want a sequence of numbers counting up from 1 to N and then down to 1 again. A generator helps: def updown(N): for x in xrange(1,N): yield x for x in xrange(N,0,-1): yield x for i in updown(3): print i # prints: 1 2 3 2 1 Here is a generator that works somewhat like the built-in xrange function, but returns a sequence of floating-point values instead of a sequence of integers: def frange(start, stop, step=1.0): while start < stop: yield start start += step frange is only somewhat like xrange, because, for simplicity, it makes arguments start and stop mandatory, and silently assumes step is positive (by default, like xrange, frange makes step equal to 1). Generators are more flexible than functions that return lists. A generator may build an iterator that returns an infinite stream of results that is usable only in loops that terminate by other means (e.g., via a break statement). Further, the generator-built iterator performs lazy evaluation: the iterator computes each successive item only when and if needed, just in time, while the equivalent function does all computations in advance and may require large amounts of memory to hold the results list. Therefore, in Python 2.2 and later, if all you need is the ability to iterate on a computed sequence, it is often best to compute the sequence in a generator, rather than in a function that returns a list. If the caller needs a list that contains all the items produced by a generator G(arguments), the caller can use the following code: resulting_list = list(G(arguments)) 4.10.9 RecursionPython supports recursion (i.e., a Python function can call itself), but there is a limit to how deep the recursion can be. By default, Python interrupts recursion and raises a RecursionLimitExceeded exception (covered in Chapter 6) when it detects that the stack of recursive calls has gone over a depth of 1,000. You can change the recursion limit with function setrecursionlimit of module sys, covered in Chapter 8. However, changing this limit will still not give you unlimited recursion; the absolute maximum limit depends on the platform, particularly on the underlying operating system and C runtime library, but it's typically a few thousand. When recursive calls get too deep, your program will crash. Runaway recursion after a call to setrecursionlimit that exceeds the platform's capabilities is one of the very few ways a Python program can crash—really crash, hard, without the usual safety net of Python's exception mechanisms. Therefore, be wary of trying to fix a program that is getting RecursionLimitExceeded exceptions by raising the recursion limit too high with setrecursionlimit. Most often, you'd be better advised to look for ways to remove the recursion or, at least, to limit the depth of recursion that your program needs. |
| [ Team LiB ] |


Most people can work safely. These good practice reminders can help:
Water and Shade:
- Drinking water must be on site.
- Drink plenty of water, even if you’re not thirsty.
Emergency planning and response:
- Make sure medical help (clinic, hospital, emergency services) is nearby – if not, first aid must be on site.
- Know where you are in case you need to call 911.
Work and rest:
- Gradually build up workload and allow more breaks over the first week for new or returning workers doing heavy work (acclimatize workers).
- Use more precautions for layers of protective clothing and work in direct sun.
- Use sunscreen and wear light-colored clothing.
Training:
- Train on the signs and symptoms of heat illness and what to do in an emergency.
- Plan ahead for hotter weather.


SKINCARE MISCONCEPTIONS
- Vitamin E minimizes scarring.
-
Vitamin E is an antioxidant and helps build skin, but there is no evidence that it does anything to help with scarring. There is even some research that suggests it may have a negative effect on scarring.
- Cucumbers help reduce puffiness around the eyes.
-
The principle ingredient in cucumbers is 90% water with the balance being inert fiber. Although cucumbers are mostly used in spas for facial treatments and can be soothing and hydrate skin temporarily. The same results can be obtained from a cold compress.
- Skin pores open and close.
-
Pores are openings in the skin that allow oils to reach the surface. It is not possible for them to open and close on their own. If pores are larger, this can be due to dead cells, genetics or scarring from squeezing blemishes.
- The higher the skin protection factor (SPF) rating, the better.
-
SPF ratings only refer to protection from UVB rays. A person needs sun protection that has chemical and physical blockers, plus antioxidants. A higher SPF also gives a false sense of security and introduces more potentially harmful chemicals to the body.
- Layering several products with SPF ratings increases protection.
-
You are only protected to the extent of the higher rating of one product. A foundation with an SPF of 10, moisturizer with an SPF of 15 and a sunscreen with an SPF of 20 does not yield an SPF rating of 45.
- Topical creams containing collagen can replace collagen.
-
There is no evidence that the topical application of collagen or elastin can penetrate the dermis, even when using technology. They can provide moisturization to the epidermis, but only injections are effective.
- Mineral oil is bad for your skin.
-
Mineral oil is a far from the industrial type that was used on skin back in the day. It's a very effective ingredient in helping remove excess oil from the skin. Vitamin A is the best ingredient to normalize skin.
- Mineral oil is comedogenic.
-
Cosmeceutical-grade mineral oil is not comedogenic. The myth is that industrial-grade mineral oil and lubricants are the same as those used in cosmetic ingredients. So-called medicinal white mineral oil has met with stringent safety standards. To remove sebum plugs in the follicles, an oil-based substance is needed. Water-based products cannot melt or remove sebum plugs.
- Preservatives in skin care products are bad.
-
Preservatives help prevent the growth of bacteria, fungi and other organisms that can not only deteriorate a product’s effectiveness and spoil the product itself, but also allow harmful bacteria to get on or in the skin. Parabens are found throughout nature; for example, many fruits and vegetables are naturally full of parabens
- Packaging is not important.
-
Packaging in skin care is vitally important—not for aesthetic reasons—but to protect the efficacy of the ingredients. Wide-mouth jars, transparent containers and pumps that are not airless all pose problems in keeping ingredients safe and potent.
- Chocolate and greasy foods cause acne.
-
Eating chocolate does not cause acne. Hormonal factors, bacteria and skin cells are at the root of problematic skin, and stress can exacerbate flareups. Some individuals may have allergic reactions to foods that can cause inflammation, as well.
- Natural and organic products are always better.
-
Many natural and organic products are not as they claim. Plus, many times, active ingredients have to be synthesized to be bioavailable and efficacious. Synthetic compounds can actually be identical to those found in nature and be more effective. Natural vs. laboratory-processed should not lead to an up or down decision about whether a product is good or bad. Not all chemicals are bad, and not all natural or organic ingredients are good.
- Using larger quantities of a product will yield better results.
-
Less is more. Normally, a pea-sized amount of facial product will do the trick. Excessive amounts can cause skin problems and waste money.
- Blackheads are a caused by improperly cleansed skin.
-
Blackheads or comedones are caused by clogged pores, and excessive scrubbing can irritate and further inflame skin. Blackheads often contain dirt, oil, and dry and dead skin cells that need to be removed. Products that help dissolve sebum are the most effective.
- Drying problematic or oily skin clears up acne.
-
The opposite is true. When skin becomes overly dry, an environment is created in which the skin is signaled that is too dry and produces more oil. Use drying products sparingly, and look to lightly moisture oily skin. The goal is to keep skin balanced.
- Get a base tan to prevent burning before going on vacation.
-
Any tan is a scar, and there is no such thing as a healthy, safe tan. Self-tanners are the safest way to obtain tanned skin.
- All sun damage to skin occurs before 18 years of age.
-
Sun damage continues to occur throughout life, although recent information suggests less than 50% of sun damage happens before a person is 18.2 It is never too late to protect your skin.
- Indoor tanning is safe.
-
The argument that tanning beds and booths do not cause skin mutations that may cause cancerous lesions to develop is patently false. UVA rays found in indoor tanning lead to deeper, more harmful skin damage. You do not have to have a sunburn to create damage to skin cells.
- Antioxidants reverse wrinkles.
-
Antioxidants are essential in fighting free radical formation and are important in helping prevent skin damage, but they cannot make wrinkles go away.


Helping a new dev setup their machine today with rbenv
when trying to
bundle
I ran into this error:
ERROR: While executing gem ... (Gem::FilePermissionError)
You don't have write permissions for the /Library/Ruby/Gems/2.0.0 directory.
Thankfully I already dealt with this before and referenced it as a comment to a question on StackOverflow. For my own future reference I will list the solution here as well.
You need to correct your paths
To determine if this fix will work run the following:
which gem
This should output a directory you do not have permissions to:
/usr/bin/gem
To fix this perform the following steps.
- Determine the path you need to copy to your profile:
rbenv init -
The first line of the output is the line you need to copy over to your profile:
export PATH="/Users/justin/.rbenv/shims:${PATH}" #path that needs to be copied source "/usr/local/Cellar/rbenv/0.4.0/libexec/../completions/rbenv.zsh" rbenv rehash 2>/dev/null rbenv() { typeset command command="$1" if [ "$#" -gt 0 ]; then shift fi case "$command" in rehash|shell) eval `rbenv "sh-$command" "$@"`;; *) command rbenv "$command" "$@";; esac }
- Copy the path to your profile and save it
- Reload your profile (source ~/.zshenv for me)
- Run
rbenv rehash
Now when you run which gem
you should get a local path that you have permissions to:
/Users/justin/.rbenv/shims/gem


ScreenOutput()
语法
OutputID = ScreenOutput()概要
Returns the OutputID of the currently used screen to perform 2D rendering operations on it. It will use the PureBasic 2DDrawing library and can only be used within a StartDrawing() / StopDrawing() block. The memory allocated in ScreenOutput() is released on StopDrawing().
参数
无.
返回值
The OutputID of the currently used screen to perform 2D rendering operations on it.
备注
On Linux and OS X, ScreenOutput() copies the whole screen buffer back to main memory to do 2D drawing operations (OpenGL doesn't allow direct buffer access). Therefore drawing on a screen is very slow and should be avoided. ScreenOutput() has to be called in the same thread where OpenScreen() was called.
示例
StartDrawing(ScreenOutput()) ; do some drawing stuff here... StopDrawing()
已支持操作系统
所有


404 - Not Found
Oops! We're not able to find the page you're looking for. Here are some options to help you get back on the right track:
If all else has failed, try emailing our friendly customer service staff.
Oops! We're not able to find the page you're looking for. Here are some options to help you get back on the right track:
If all else has failed, try emailing our friendly customer service staff.


Located 35 NM off the east coast of the Malaysian peninsula, Tioman Island has been voted one of the world's most beautiful islands. Scenic, tranquil, with miles of white sandy beaches and crystal clear waters teeming with spectacular marine life and colourful coral gardens. The twin peaks of Gunung Nenek Semukut provide a prominent landmark to this island.
This is still very much the case today and cruising yacht SV Totem reported in September 2013 "What a pleasant surprise to find that Tioman is still a sleepy place of quiet beauty. Yes, it got busy on the weekend with the flocks from Singapore and KL, but most of the time it was just heavenly peace".
For full details on clearance into and out of Malaysia see Malaysia Formalities.
Clear in with Customs and Immigration at the ferry pier building. If the building is locked up, just cross the street to the airport and they'll call the officials for you.
The harbormaster (Jabatan Laut) is now in the marina.
When leaving here to visit Sarawak, it is necessary to clear out.
Last updated September 2013.
The new (controversial) marina at the village of Teluk Tekek on the western side of the island is now open, but it has limited berths for visitors. Photo taken by SY Totem September 2013.
The Marine Park Authority has laid orange buoys all round the island.
Most of the villages have jetties.
Juara Beach is a fair-weather anchorage on the eastern side of the island with exposure to the E. It is a surf beach in the NE monsoon routinely exposed. All sand, no reef, with good holding in ~20' close in. Juara has a collection of small beach-front restaurants, with generally lower prices than in Tektek. Laundry services also available for less than Tektek. There's minimal provisioning available here.
Last updated September 2013.


If you can see the running "hello world" in the addressfield/tab/tab-switcher of your browser then there is support for svg favicons, however if you see a sad smileyface icon or any other icon then there's no support for svg favicons in your browser.


Enum SYSTEM_ERROR_MODE
Namespace: THNETII.WinApi.Native.WinBase
Assembly: THNETII.WinApi.Headers.WinBase.dll
Syntax
[Flags]
public enum SYSTEM_ERROR_MODE
Fields
| Name | Description |
|---|---|
| SEM_FAILCRITICALERRORS | The system does not display the critical-error-handler message box. Instead, the system sends the error to the calling thread. Best practice is that all applications call the process-wide |
| SEM_NOALIGNMENTFAULTEXCEPT | The system automatically fixes memory alignment faults and makes them invisible to the application.
It does this for the calling process and any descendant processes.
This feature is only supported by certain processor architectures. For more information, see |
| SEM_NOGPFAULTERRORBOX | The system does not display the Windows Error Reporting dialog. |
| SEM_NOOPENFILEERRORBOX | The THNETII.WinApiNative.FileManagement.FileManagementFunctions.OpenFile function does not display a message box when it fails to find a file. Instead, the error is returned to the caller. This error mode overrides the |


Get a file's content.
Returns the content of a file.
Optional query parameters
The API supports an optional query parameter, version with the following values:
Accepts and returns JSON as Content-Type.
Request:
GET /api/v2/admin/sites/current/storage/images/cat.jpg HTTPS/1.1
Connection: keep-alive
Accept: application/json
Authorization: c50f6e6be0d1481ca0d8eb0c63642fdd171c17846af04cdd95676a0888141f73
Response:
HTTP/1.1 200 OK
Content-Type: application/octet-stream
Content-Length: 1024
Last-Modified: Mon, 26 Jul 2010 12:45:26 GMT
Body: file contents as binary
Get a file's content
var request = $.ajax({
url: "/api/v2/admin/sites/current/storage/images/description2.txt",
type: "GET",
connection: "keep-alive",
accept: "application/json",
headers: {
"Authorization": $.cookie('access_token')
}
});
request.done(function (msg) {
console.log(msg);
})
request.fail(function (jqXHR) {
console.log("Request failed.");
console.log("Error code: " + jqXHR.status);
console.log("Error text: " + jqXHR.statusText);
console.log("Response text: " + jqXHR.responseText);
})
This method will return the following error codes:
200
- OK400
- Bad request:
104000
- Generic FileAPI error104001
- File not found104002
- Not a file104003
- Not enough privileges104004
- Invalid login multiple sites104009
- Path not found104014
- Name required104019
- Can't find folder104023
- Unauthorized access104024
- Folder not empty104004
- File or folder name too long 401
- when authentication token is incorrect404
- the file does not exist


How does it work?
Interestingly, it was quite easy to find out, so here is how to do it with mimikatz (you’ll need local admin):
sekurlsa::pth /user:<user name> /domain:<domain name> /ntlm:<the user's ntlm hash> /run:"mstsc.exe /restrictedadmin"
This will open a new RDP window. If it still shows the user you are currently logged on with, just ignore it - everything will just work ;-)
Why does it work?
RDP Restricted Admin Mode builds upon Kerberos. Taking a look at the network traffic, one can see that the RDP client requests a ticket on behalf of the impersonated user which is no problem since the hash is all we need to authenticate against Kerberos.
Restricted Admin Mode is disabled, what can I do?
A registry key controls if a server accepts Restricted Admin sessions. If you have the NTLM hash of a user that has privileges to set registry keys, you can use for example Powershell to enable it and log in via RDP afterwards:
mimikatz.exe "sekurlsa::pth /user:<user name> /domain:<domain name> /ntlm:<the user's ntlm hash> /run:powershell.exe"
A new Powershell window will pop up:
Enter-PSSession -Computer <Target>
New-ItemProperty -Path "HKLM:\System\CurrentControlSet\Control\Lsa" -Name "DisableRestrictedAdmin" -Value "0" -PropertyType DWORD -Force
Now, your RDP should work fine.


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Leaps and Bounds: The Game
Level
This text is displayed if your browser does not support HTML5 Canvas.
Controls
face left/right = left/right arrow | leap = d | bound = f


|
Livox SDK API
V2.2.0
|
#include <livox_def.h>
Public Attributes | |
| char | broadcast_code [16] |
| uint8_t | handle |
| uint8_t | slot |
| uint8_t | id |
| uint8_t | type |
| uint16_t | data_port |
| uint16_t | cmd_port |
| uint16_t | sensor_port |
| char | ip [16] |
| LidarState | state |
| LidarFeature | feature |
| StatusUnion | status |
| uint8_t | firmware_version [4] |
Information of the connected LiDAR or hub.
Definition at line 336 of file livox_def.h.
| char DeviceInfo::broadcast_code[16] |
Device broadcast code, null-terminated string, 15 characters at most.
Definition at line 337 of file livox_def.h.
| uint16_t DeviceInfo::cmd_port |
Control command UDP port.
Definition at line 343 of file livox_def.h.
| uint16_t DeviceInfo::data_port |
Point cloud data UDP port.
Definition at line 342 of file livox_def.h.
| LidarFeature DeviceInfo::feature |
LiDAR feature.
Definition at line 347 of file livox_def.h.
| uint8_t DeviceInfo::firmware_version[4] |
Firmware version.
Definition at line 349 of file livox_def.h.
| uint8_t DeviceInfo::handle |
Device handle.
Definition at line 338 of file livox_def.h.
| uint8_t DeviceInfo::id |
LiDAR id.
Definition at line 340 of file livox_def.h.
| char DeviceInfo::ip[16] |
IP address.
Definition at line 345 of file livox_def.h.
| uint16_t DeviceInfo::sensor_port |
IMU data UDP port.
Definition at line 344 of file livox_def.h.
| uint8_t DeviceInfo::slot |
Slot number used for connecting LiDAR.
Definition at line 339 of file livox_def.h.
| LidarState DeviceInfo::state |
LiDAR state.
Definition at line 346 of file livox_def.h.
| StatusUnion DeviceInfo::status |
LiDAR work state status.
Definition at line 348 of file livox_def.h.
| uint8_t DeviceInfo::type |
Device type, refer to DeviceType.
Definition at line 341 of file livox_def.h.


The problem with having many condition types is that all intermediaries in a certain path would need to support the same algorithms in order for them to be usable. That meant that the only algorithms you could rely upon having support for would be the least common denominator. Most likely, this would come down to simple SHA256 hash-locks. Less than 20% of the functionality could serve more than 80% of the use cases, and so the Interledger standard parted ways with Crypto Conditions.


| carWeb {car} | R Documentation |
This function will access the website for An R Companion to Applied Regression, or setup files or data.
carWeb(page = c("webpage", "errata", "taskviews"), script, data, setup)
page |
A character string indicating what page to open. The default
|
script |
The quoted name of a chapter in An R Companion to Applied
Regression, like |
data |
The quoted name of a data file in An R Companion to Applied
Regression, like |
setup |
If |
Either displays a web page or a PDF document or downloads files to your working directory.
Sanford Weisberg, based on the function UsingR
in the UsingR
package by John Verzani
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
## Not run: carWeb() carWeb(setup=TRUE) ## End(Not run)


Welcome to
azk
docs!
azk
is an open-source orchestrator for local development environments.
Please choose a language
English (en-US)
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Killed Mutations:
- changed conditional boundary
(org.pitest.mutationtest.engine.gregor.mutators.ConditionalsBoundaryMutator)
- negated conditional (org.pitest.mutationtest.engine.gregor.mutators.NegateConditionalsMutator)
Survived Mutations:
- Replaced integer addition with subtraction
(org.pitest.mutationtest.engine.gregor.mutators.MathMutator)


This example demonstrates the most simple usage of the Noise.UniformNoise
block:
- globalSeed is the Noise.GlobalSeed
block with default options (just dragged from sublibrary
Noise).
- uniformNoise1 is an instance of Noise.UniformNoise
with samplePeriod = 0.02 s and a Uniform distribution with limits
y_min=-1, y_max=3.
- uniformNoise2 is identical to uniformNoise1
with the exception that useAutomaticLocalSeed=false and
fixedLocalSeed=10.
At every 0.02 seconds a time event occurs and a uniform random
number in the band between -1 ... 3 is drawn. This random number is
held constant until the next sample instant. The result of a
simulation is shown in the next diagram:
| Date |
Description |
| June 22, 2015 |
|
Generated at 2019-01-25T16:44:03Z by OpenModelica 1.14.0~dev-130-gefa44fb