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Please transcribe (that is copy) the text below into the input field. Be exact and make sure to get all characters and spaces correct.
By the way we have magic link for you.
{% endif %} {% next_button %} {% if settings.DEBUG %}{% if error_rate_percent != 0 %} The text has {{ transcription | length }} characters including space. A subject can only advance to the next screen if the Levenshtein distance is less than {{ error_rate_percent }}% of the total characters. {% else %} This text is rather short ({{ transcription | length }} characters including space). A subject can only advance to the next screen if he makes no mistakes. {% endif %}


Review: SADES’s affordable one-handed keyboard is a great starter model
A cheap way to try out whether a one-handed keyboard is for you, and it just might be.
by Richard DevineSource: Windows Central
I've always been curious as to why one-handed gaming keyboards exist. After all, it's another thing to have on your desk if you do a lot of PC gaming and it's not as if you can dispense with your regular keyboard and its full suite of keys.
But I'd also never tried one for myself, so I grabbed this SADES one-handed keyboard to take one for a spin. Plenty attracted me to this particular model, not least the incredibly low price, but also it's not overstyled and, crucially, packs blue mechanical switches.
After spending a little time using it, maybe there's something to this tiny gaming keyboard thing after all...
Tiny keyboard
SADES one-handed keyboard
Bottom line: Not a bad one-handed keyboard for such a low price, though there are some annoyances.
Pros:
- Decent build quality
- Blue mechanical switches
- Included wrist rest
- Nice and compact
- Tasteful design
Cons:
- Cable could be longer
- Slides around under pressure
- No reprogrammable buttons
- Wrist rest not removable
What you'll like about the SADES one-handed keyboard
Source: Windows Central
One of the main reasons you may be considering a one-handed keyboard at all is the size. Whether it's for gaming on the road or you're tight on space and want to give your mouse more room, this is a winner in the size department. It's essentially half the size of a tenkeyless keyboard, which makes it incredibly compact.
A laptop is another great use case for something like this, and the size is such that you'll be able to stash it in your laptop bag without much trouble, something you can't say about even the smallest of regular gaming keyboards.
It's also well built, something that comes into play if you're travelling about with it, stashing it in bags with the rest of your mobile gaming arsenal. The majority of the body is a fairly solid feeling plastic, with the top plate appearing to be metal.
But what about the business end of this one-handed keyboard? What's it like to game on? Actually, very good. You get a mechanical blue switch of undetermined origin beneath each keycap, and they feel pretty good. Enthusiasts will be able to tell the difference to something like a Cherry MX, not least because of the slightly metallic sound, but when it comes to using the keyboard the keys feel excellent.
Source: Windows Central
There's a satisfying click and a nice bump, actuation distance feels low and it's pretty quick. It's not as quick as my own daily driver, the Razer Huntsman, but I've been able to game on it without feeling it's holding me back at all. The keys are fairly loud, and some might think they're too loud, but they're actually a little quieter than my Huntsman, albeit with a deeper sounding click as well. So I'm perfectly happy.
Each key is also illuminated, and each row has a different color, and there doesn't appear to be any way to change that. That's fine, personally, it looks tasteful enough and the LEDs aren't overly bright. There's also an included wrist rest which, while only plastic with no cushioning, does a good enough job of providing support. Some soft trim would have been nice, but it's not uncomfortable to use at least, though it really should also be detachable for those who simply don't want it to be there.
The design is very tasteful as a whole, too. There are plenty of aggressive looking competitors out there with ridiculous fonts and unnecessary angles. This is just a fairly unassuming rectangle, with a splash of color and some chamfered edges to add a bit of class.
 The first is that under pressure, it can slide.
Whether the keyboard is too light at just under 1lb or whether the rubber grips on the bottom just aren't up to scratch, it's something to bear in mind before you click the buy button if you're a heavy-handed gamer like I am.
The cable is also, in my opinion, too short. I use this with my left hand and my PC is over on the right hand side of my, not overly large desk, and the cable barely reaches. Gaming peripherals should have long cables.
Perhaps more of an issue for some is that since there's no companion app, you can't reprogram any of the keys to either change their function or add macros. They are just single purpose. In my case, it meant changing some of the keybindings in Destiny 2 to match keys actually on the SADES, but as I don't use macros that's as far as I had to go. But if you're looking for something more advanced, then this isn't the keyboard for you.
Should you buy the SADES one-handed keyboard?
Source: Windows Central
If you're looking for something to use with a laptop in particular, or you're just interested in trying out a one-handed keyboard to see if it's something you'll enjoy, you can't really go wrong with this.
Sure, you'll need to make sure you don't force it to slide across the desk and you'll quite possibly need a USB extension cable to plug it in, but for the price it's hard to truly complain.
3.5 out of 5
If you're a more advanced user who's looking for reprogrammable keys and macro support, then it's not for you, but it is a very good beginner product. I've never used one before and there's certainly an appeal, particularly to use gaming on a laptop.

Great starter
SADES one-handed keyboard
A good place to start with a one-handed keyboard
Nothing flash, just the gaming half of a keyboard with mechanical switches, tasteful RGB and a decent wrist rest.
We may earn a commission for purchases using our links. Learn more.


Upcoming Events
Central Park Fundraiser
7th December – 21st December
Centra Shop | 175 Witton Road, Cork
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Food Drive in Cork
1st November – 31st December
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Mon-Sat (11am – 7pm), Sun (12pm- 5pm)
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Clare: 02321 708021 &<PHONE_NUMBER>89 | Cork: 07843 55875290 & 07478 7877392
Charity Food Sale
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Glonmore GAA Pitch, Savile Road, Donegal
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Charity runs 2
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Bottomless brunch
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C++ Support
The Clang frontend now supports the majority of C++. Support in the frontend for C++ language features, however, does not automatically translate into support for those features in the static analyzer. Language features need to be specifically modeled in the static analyzer so their semantics can be properly analyzed. Support for analyzing C++ and Objective-C++ files is currently extremely limited, and we are only encouraging those who are interested in contributing to the development of the analyzer to try this functionality out at this time.
Listed here are a set of open tasks that are prerequisites for decent analysis of C++. This list is also not complete; new tasks will be added as deemed necessary.
- Control-Flow Graph Enhancements:
- Model C++ destructors
- Model C++ initializers (in constructors)
- Path-Sensitive Analysis Engine (GRExprEngine):
- Model C++ casts
- Model C++ constructors
- Model C++ destructors
- Model new and delete


We’ll help you fill out the application and send it off to your school. If you have questions or need help, look for the icon or use the FAQ at the top of the screen.
Here’s what you’ll need to know to apply:
Income is money paid to a person from a job, Social Security, annuities, child support, and more. Both children and adults can earn income. See Help for a list of more types of income.
This application has to be signed by a member of the household who is an adult.


Priest Shegfield:
People of the just Einhasad may express their loyalty to their goddess in many ways.
Prove your loyalty by assisting the Temple and you will be given the title "Hands of the Temple" as a reward -- and to inspire others.
"I want to prove my loyalty to my goddess."


Hello there! This is a little about me, I like all things related to computers. Like at first sight with a computers is during 5th grade. At that time I played the Counter Strike 1.6 game with minimum specifications, Pentium 3. Maybe you can judge me as a gamers too, so that's why I put my nickname on the middle of my name. From there I started learning something about Networking, Programming & I like analyzing a system.
"Hack The World!"
Develop projects in the BizOps team as a Platform and sometimes a Product
Lead the engagement team
Develop B2B projects in co-branding teams
Develop an integrated internal system
Install, Maintenance & Troubleshoot: Software, Hardware & Networking
GPA: 3.07 / 4.00
Playing as Sniper
Playing as Midlaner or Offlaner
Playing as Midlaner or Offlaner
Present the application that I made
Help or answer student questions when experiencing difficulties
Make sure the event runs as planned and all teams get what they need
Make sure the event runs as planned and all teams get what they need


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We describe the design and implementation of Julia in our first paper - Julia: A Fast Dynamic Language for Technical Computing. This is work in progress and comments are appreciated.
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In the typical sequence of an individual’s emotional development, friendships come after parental bonding and before pair bonding. In the intervening period between the end of early childhood and the onset of full adulthood, friendships are often the most important relationships in the emotional life of the adolescent, and are often more intense than relationships later in life. The absence of friends can be emotionally damaging.
Evolution
The evolutionary psychology approach to human development has led to the theory of Dunbar’s number, proposed by British anthropologist Robin Dunbar. He theorized that there is a limit of approximately 150 people with whom a human can maintain stable social relationships.
Childhood
In childhood, friendships are often based on the sharing of toys, and the enjoyment received from performing activities together. These friendships are maintained through affection, sharing, and creative playtime. While sharing is difficult for children at this age, they are more likely to share with someone they consider to be a friend. As children mature, they become less individualized and are more aware of others.
They begin to see their friends’ points of view, and enjoy playing in groups. They also experience peer rejection as they move through the middle childhood years. Establishing good friendships at a young age helps a child to be better acclimated in society later on in their life. In a 1975 study, Bigelow and La Gaipa found that expectations for a “best friend” become increasingly complex as a child gets older. The study investigated such criteria in a sample of 480 children between the ages of six and fourteen.
Their findings highlighted three stages of development in friendship expectations. In the first stage, children emphasized shared activities and the importance of geographical closeness. In the second, they emphasized sharing, loyalty, and commitment. In the final stage, they increasingly desired similar attitudes, values, and interests.
Prize friendships
According to Berndt, children prize friendships that are high in pro-social behavior, intimacy, and other positive features; they are troubled by friendships that are high in conflict, dominance, rivalry, and other negative features. High-quality friendships have often been assumed to have positive effects on many aspects of children’s social development. Perceived benefits from such friendships include enhanced social success, but they apparently do not include an effect on children’s general self-esteem.
Numerous studies with adults suggest that friendships and other supportive relationships do enhance self-esteem. Other potential benefits of friendship include the opportunity to learn about empathy and problem solving. Coaching from parents can be useful in helping children to make friends. Eileen Kennedy-Moore describes three key ingredients of children’s friendship formation: (1) openness, (2) similarity, and (3) shared fun. Parents can also help children understand social guidelines they haven’t learned on their own.
Drawing from research by Robert Selman and others, Kennedy-Moore outlines developmental stages in children’s friendship, reflecting an increasing capacity to understand others’ perspectives: “I Want It My Way”, “What’s In It For Me?”, “By the Rules”, “Caring and Sharing”, and “Friends Through Thick and Thin.”


The saxon:while
element is used to iterate while some condition is true.
The condition is given as a boolean expression in the mandatory test attribute. Because
this expression must change its value if the loop is to terminate, the condition will generally
reference a variable that is updated somewhere in the loop using an saxon:assign
element.
Alternatively, it may test a condition that is changed by means of a call on an extension function
that has side-effects.
Example:
<xsl:variable name="i" expr="0"/>
<saxon:while test="$i < 10">
The value of i is <xsl:value-of select="$i"/>
<saxon:assign name="i" expr="$i+1"/>
</saxon:while>


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| Beloved, believe not every spirit, but try the spirits whether they are of God: because many false prophets are gone out into the world.
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| ᎢᏨᎨᏳᎢ, ᏞᏍᏗ ᎾᏂᎥ ᏗᏓᏅᏙ ᏱᏗᏦᎯᏳᎲᏍᎨᏍᏗ, ᏕᏥᎪᎵᏰᏍᎨᏍᏗᏍᎩᏂ ᏗᏓᏅᏙ, ᎤᏁᎳᏅᎯ ᏅᏓᏳᏓᎴᏅᎯ ᎨᏒ, ᎠᎴ ᎤᏁᎳᏅᎯ ᏅᏓᏳ ᏓᎴᏅᎯ ᏂᎨᏒᎾ ᎨᏒᎢ. ᎤᏂᏣᏔᏰᏃ ᎤᎾᏠᎾᏍᏗ ᎠᎾᏙᎴᎰᏍᎩ ᎠᏁᏙᎭ ᎡᎶᎯ.
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I-tsv-ge-yu-i, tle-s-di na-ni-v di-da-nv-do yi-di-tso-hi-yu-hv-s-ge-s-di, de-tsi-go-li-ye-s-ge-s-di-s-gi-ni di-da-nv-do, U-ne-la-nv-hi nv-da-yu-da-le-nv-hi ge-sv, a-le U-ne-la-nv-hi nv-da-yu da-le-nv-hi ni-ge-sv-na ge-sv-i. U-ni-tsa-ta-ye-no u-na-tlo-na-s-di a-na-do-le-ho-s-gi a-ne-do-ha e-lo-hi.
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| Hereby know ye the Spirit of God: Every spirit that confesseth that Jesus Christ is come in the flesh is of God:
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| ᎯᎠ ᎾᏍᎩ ᏄᏍᏗ ᎢᏦᎵᏍᏙᏗ ᎤᏁᎳᏅᎯ ᎤᏤᎵ ᎠᏓᏅᏙ; ᎾᏍᎩ ᎠᏓᏅᏙ ᎯᎠ ᎢᎦᏪᏍᎩ, ᏥᏌ ᎦᎶᏁᏛ ᎤᏇᏓᎵ ᎤᎾᏄᎪᏥᎴᎢ, ᎾᏍᎩ ᎤᏁᎳᏅᎯ ᏅᏓᏳᏓᎴᏅᎯ.
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Hi-a na-s-gi nu-s-di i-tso-li-s-do-di U-ne-la-nv-hi u-tse-li A-da-nv-do; na-s-gi a-da-nv-do hi-a i-ga-we-s-gi, Tsi-sa Ga-lo-ne-dv u-que-da-li u-na-nu-go-tsi-le-i, na-s-gi U-ne-la-nv-hi nv-da-yu-da-le-nv-hi.
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| And every spirit that confesseth not that Jesus Christ is come in the flesh is not of God: and this is that spirit of antichrist, whereof ye have heard that it should come; and even now already is it in the world.
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| ᎾᏍᎩᏃ ᎠᏓᏅᏙ ᎯᎠ ᎢᎦᏪᏍᎩ ᏂᎨᏒᎾ, ᏥᏌ ᎦᎶᏁᏛ ᎤᏇᏓᎵ ᎤᎾᏄᎪᏥᎴᎢ, ᎥᏝ ᎤᏁᎳᏅᎯ ᏅᏓᏳᏓᎴᏅᎯ ᏱᎩ. ᎾᏍᎩᏍᎩᏂ ᎯᎠ ᎦᎶᏁᏛ-ᎠᏡᏗᏍᎩ ᎤᏤᎵᎦ ᎠᏓᏅᏙ, ᎾᏍᎩ ᎤᎷᎯᏍᏗᏱ ᎢᏣᏛᎦᏅᎯ ᏥᎩ, ᎠᎴ ᎿᎭᏉ ᎦᏳᎳ ᎡᎶᎯ ᎡᎭ.
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Na-s-gi-no a-da-nv-do hi-a i-ga-we-s-gi ni-ge-sv-na, Tsi-sa Ga-lo-ne-dv u-que-da-li u-na-nu-go-tsi-le-i, v-tla U-ne-la-nv-hi nv-da-yu-da-le-nv-hi yi-gi. Na-s-gi-s-gi-ni hi-a ga-lo-ne-dv--a-tlu-di-s-gi u-tse-li-ga a-da-nv-do, na-s-gi u-lu-hi-s-di-yi i-tsa-dv-ga-nv-hi tsi-gi, a-le hna-quo ga-yu-la e-lo-hi e-ha.
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| Ye are of God, little children, and have overcome them: because greater is he that is in you, than he that is in the world.
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| ᏂᎯ ᎤᏁᎳᏅᎯ ᏅᏓᏣᏓᎴᏅᎯ, ᏗᏥᏲᎵ, ᎠᎴ ᏕᏥᏎᎪᎩᏒ ᎾᏍᎩ; ᏅᏗᎦᎵᏍᏙᏗᎭ ᎤᏟ ᎤᎵᏂᏗᎩᏗᏳ ᎨᏒ ᎢᏥᏯᎢ ᎡᏍᎦᏉ ᎡᎶᎯ ᎠᏯᎢ.
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Ni-hi U-ne-la-nv-hi nv-da-tsa-da-le-nv-hi, di-tsi-yo-li, a-le de-tsi-se-go-gi-sv na-s-gi; nv-di-ga-li-s-do-di-ha u-tli u-li-ni-di-gi-di-yu ge-sv i-tsi-ya-i e-s-ga-quo e-lo-hi a-ya-i.
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| They are of the world: therefore speak they of the world, and the world heareth them.
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| ᎡᎶᎯ ᏅᏓᏳᎾᏓᎴᏅᎯ ᎾᏍᎩ, ᎾᏍᎩ ᎢᏳᏍᏗ ᎡᎶᎯ ᎡᎯ ᎠᏂᏬᏂᎭ, ᎠᎴ ᎡᎶᎯ ᎤᎾᏛᎦᏁᎰᎢ.
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E-lo-hi nv-da-yu-na-da-le-nv-hi na-s-gi, na-s-gi i-yu-s-di e-lo-hi e-hi a-ni-wo-ni-ha, a-le e-lo-hi u-na-dv-ga-ne-ho-i.
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| We are of God: he that knoweth God heareth us; he that is not of God heareth not us. Hereby know we the spirit of truth, and the spirit of error.
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| ᎠᏴ ᎤᏁᎳᏅᎯ ᏅᏓᏲᎦᏓᎴᏅᎯ; ᎾᏍᎩ Ꮎ ᎤᏁᎳᏅᎯ ᎠᎦᏔᎯ ᎣᎦᏛᎦᏁᎰᎢ. ᎾᏃ ᎤᏁᎳᏅᎯ ᏅᏓᏳᏓᎴᏅᎯ ᏂᎨᏒᎾ ᎥᏝ ᏲᎦᏛᎦᏁᎰᎢ. ᎾᏍᎩ ᏅᏗᎦᎵᏍᏙᏗᎭ ᏕᏗᎦᏔᎭ ᏚᏳᎪᏛ ᎢᏯᎾᏛᏁᎯ ᎠᏓᏅᏙ, ᎠᎴ ᏚᏳᎪᏛ ᎢᏯᏌᏛᏁᎯ ᏂᎨᏒᎾ ᏗᏓᏅᏙ.
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A-yv U-ne-la-nv-hi nv-da-yo-ga-da-le-nv-hi; na-s-gi na U-ne-la-nv-hi a-ga-ta-hi o-ga-dv-ga-ne-ho-i. Na-no U-ne-la-nv-hi nv-da-yu-da-le-nv-hi ni-ge-sv-na v-tla yo-ga-dv-ga-ne-ho-i. Na-s-gi nv-di-ga-li-s-do-di-ha de-di-ga-ta-ha du-yu-go-dv i-ya-na-dv-ne-hi a-da-nv-do, a-le du-yu-go-dv i-ya-sa-dv-ne-hi ni-ge-sv-na di-da-nv-do.
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| Beloved, let us love one another: for love is of God; and every one that loveth is born of God, and knoweth God.
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| ᎢᏨᎨᏳᎢ, ᏕᎦᏓᎨᏳᏎᏍᏗ; ᎠᏓᎨᏳᏗᏰᏃ ᎨᏒ ᎤᏁᎳᏅᎯ ᎤᏓᏳᏓᎴᏅᎯ, ᎩᎶᏃ ᏧᏓᎨᏳᏐ ᎤᏁᎳᏅᎯ ᎤᎾᏄᎪᏫᏒᎯ ᎨᏐᎢ, ᎠᎴ ᎠᎦᏔᎰ ᎤᏁᎳᏅᎯ.
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I-tsv-ge-yu-i, de-ga-da-ge-yu-se-s-di; a-da-ge-yu-di-ye-no ge-sv U-ne-la-nv-hi u-da-yu-da-le-nv-hi, gi-lo-no tsu-da-ge-yu-so U-ne-la-nv-hi u-na-nu-go-wi-sv-hi ge-so-i, a-le a-ga-ta-ho U-ne-la-nv-hi.
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| He that loveth not knoweth not God; for God is love.
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| ᎤᏓᎨᏳᎯᏃ ᏂᎨᏒᎾ, ᎥᏝ ᏯᎦᏔᎰ ᎤᏁᎳᏅᎯ; ᎤᏁᎳᏅᎯᏰᏃ ᎤᏓᎨᏳᏗ ᎨᏒ ᏚᏙᎥ.
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U-da-ge-yu-hi-no ni-ge-sv-na, v-tla ya-ga-ta-ho U-ne-la-nv-hi; U-ne-la-nv-hi-ye-no u-da-ge-yu-di ge-sv du-do-v.
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| In this was manifested the love of God toward us, because that God sent his only begotten Son into the world, that we might live through him.
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| ᎠᏂ ᎯᎠ ᎬᏂᎨᏒ ᏄᎵᏍᏔᏅᎩ ᎤᏁᎳᏅᎯ ᎢᎩᎨᏳᏒᎢ, ᎤᏁᎳᏅᎯᏰᏃ ᏧᏅᏎ ᎤᏩᏒᎯᏳ ᎤᏕᏁᎸᎯ ᎤᏪᏥ ᎡᎶᎯ ᎤᎷᎯᏍᏗᏱ, ᎾᏍᎩ ᎢᏳᏩᏂᏐᏗᏱ ᎬᏂᏛ ᎢᎩᏩᏛᏗᏱ.
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A-ni hi-a gv-ni-ge-sv nu-li-s-ta-nv-gi U-ne-la-nv-hi i-gi-ge-yu-sv-i, U-ne-la-nv-hi-ye-no tsu-nv-se u-wa-sv-hi-yu u-de-ne-lv-hi U-we-tsi e-lo-hi u-lu-hi-s-di-yi, na-s-gi i-yu-wa-ni-so-di-yi gv-ni-dv i-gi-wa-dv-di-yi.
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| Herein is love, not that we loved God, but that he loved us, and sent his Son to be the propitiation for our sins.
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| ᎠᏂ ᎯᎠ ᎠᏓᎨᏳᏗ ᎨᏒᎢ, ᎥᏝ ᎤᏁᎳᏅᎯ ᎡᏗᎨᏳᎢᏳ ᏄᎵᏍᏔᏅᎢ, ᎠᏴᏍᎩᏂ ᎢᎩᎨᏳᎯᏳ ᏄᎵᏍᏔᏅᎢ, ᎠᎴ ᏧᏅᏒ ᎤᏤᎵᎦ ᎤᏪᏥ ᎠᎵᏍᎪᎸᏔᏅᎯ ᎠᎫᏴᏙᏗ ᎢᎩᏍᎦᏍᏨᎢ.
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A-ni hi-a a-da-ge-yu-di ge-sv-i, v-tla U-ne-la-nv-hi e-di-ge-yu-i-yu nu-li-s-ta-nv-i, a-yv-s-gi-ni i-gi-ge-yu-hi-yu nu-li-s-ta-nv-i, a-le tsu-nv-sv u-tse-li-ga U-we-tsi a-li-s-go-lv-ta-nv-hi a-gu-yv-do-di i-gi-s-ga-s-tsv-i.
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| Beloved, if God so loved us, we ought also to love one another.
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| ᎢᏨᎨᏳᎢ, ᎢᏳᏃ ᎤᏁᎳᏅᎯ ᎾᏍᎩ ᎢᎦᎢ ᎢᎩᎨᏳᎯᏳ ᎢᏳᎵᏍᏔᏅᎯ ᏱᎩ, ᎠᏴ ᎾᏍᏉ ᏗᎦᏓᎨᏳᎯᏳ ᏱᎩ.
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I-tsv-ge-yu-i, i-yu-no U-ne-la-nv-hi na-s-gi i-ga-i i-gi-ge-yu-hi-yu i-yu-li-s-ta-nv-hi yi-gi, a-yv na-s-quo di-ga-da-ge-yu-hi-yu yi-gi.
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| No man hath seen God at any time. If we love one another, God dwelleth in us, and his love is perfected in us.
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| ᎥᏝ ᎢᎸᎯᏳ ᎩᎶ ᎤᏁᎳᏅᎯ ᎤᎪᎲᎯ ᏱᎩ. ᎢᏳᏃ ᏗᎦᏓᎨᏳᎯᏳ ᏱᎩ, ᎤᏁᎳᏅᎯ ᎢᎩᏯᎠ, ᎠᎴ ᎠᏴ ᎡᏗᎨᏳᏒ ᎤᎧᎵᏨᎯ ᎨᏐᎢ.
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V-tla i-lv-hi-yu gi-lo U-ne-la-nv-hi u-go-hv-hi yi-gi. I-yu-no di-ga-da-ge-yu-hi-yu yi-gi, U-ne-la-nv-hi i-gi-ya-a, a-le a-yv e-di-ge-yu-sv u-ka-li-tsv-hi ge-so-i.
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| Hereby know we that we dwell in him, and he in us, because he hath given us of his Spirit.
|
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| ᎯᎠ ᎾᏍᎩ ᎢᏓᏙᎴᎰᎯᏍᏗᎭ ᎡᏗᏯᎥᎢ ᎠᎴ ᎠᏴ ᎢᎩᏯᎥᎢ, ᎾᏍᎩ ᏅᏗᎦᎵᏍᏙᏗᏍᎬ ᎢᎦᎵᏍᎪᎸᏓᏁᎸᎯ ᎨᏒ ᎤᏩᏒ ᎤᏤᎵ ᎠᏓᏅᏙ.
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Hi-a na-s-gi i-da-do-le-ho-hi-s-di-ha e-di-ya-v-i a-le a-yv i-gi-ya-v-i, na-s-gi nv-di-ga-li-s-do-di-s-gv i-ga-li-s-go-lv-da-ne-lv-hi ge-sv u-wa-sv u-tse-li A-da-nv-do.
|
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| And we have seen and do testify that the Father sent the Son to be the Saviour of the world.
|
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| ᎠᎴ ᎠᏴ ᎣᎩᎪᎲ ᎠᎴ ᎣᏥᏃᎮᎭ ᎠᎦᏴᎵᎨ ᏧᏅᏒ ᎤᏪᏥ ᎡᎶᎯ ᎠᏍᏕᎵᏍᎩ ᎢᏳᎵᏍᏙᏗᏱ.
|
|---|
A-le a-yv o-gi-go-hv a-le o-tsi-no-he-ha A-ga-yv-li-ge tsu-nv-sv U-we-tsi e-lo-hi A-s-de-li-s-gi i-yu-li-s-do-di-yi.
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| Whosoever shall confess that Jesus is the Son of God, God dwelleth in him, and he in God.
|
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| ᎩᎶ ᎯᎠ ᎢᎦᏪᏍᎩ ᏥᏌ ᎤᏁᎳᏅᎯ ᎤᏪᏥ ᎨᏒᎢ, ᎾᏍᎩ ᎤᏁᎳᏅᎯ ᎣᏯᎣᎢ, ᎠᎴ ᎾᏍᎩ ᎤᏁᎳᏅᎯᏱ ᎠᏯᎣᎢ.
|
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Gi-lo hi-a i-ga-we-s-gi Tsi-sa U-ne-la-nv-hi U-we-tsi ge-sv-i, na-s-gi U-ne-la-nv-hi o-ya-o-i, a-le na-s-gi U-ne-la-nv-hi-yi a-ya-o-i.
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| And we have known and believed the love that God hath to us. God is love; and he that dwelleth in love dwelleth in God, and God in him.
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| ᎠᎴ ᎠᏴ ᎢᎩᎦᏙᎥᏒ ᎠᎴ ᎢᎪᎯᏳᏅ ᎤᏁᎳᏅᎯ ᎢᎩᎨᏳᎯᏳ ᎨᏒᎢ. ᎤᏁᎳᏅᎯ ᎠᏓᎨᏳᏗ ᎨᏒ ᏚᏙᎥ, ᎠᏓᎨᏳᏗᏃ ᎨᏒ ᎡᎯ ᎤᎾᎳᏅᎯ ᎠᏯᎣᎢ, ᎠᎴ ᎤᎾᎳᏅᎯ ᎾᏍᎩ ᎤᏯᎣᎢ.
|
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A-le a-yv i-gi-ga-do-v-sv a-le i-go-hi-yu-nv U-ne-la-nv-hi i-gi-ge-yu-hi-yu ge-sv-i. U-ne-la-nv-hi a-da-ge-yu-di ge-sv du-do-v, a-da-ge-yu-di-no ge-sv e-hi U-na-la-nv-hi a-ya-o-i, a-le U-na-la-nv-hi na-s-gi u-ya-o-i.
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| Herein is our love made perfect, that we may have boldness in the day of judgment: because as he is, so are we in this world.
|
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| ᎠᏂ ᎯᎠ ᎢᎦᏓᎨᏳᏗ ᎨᏒ ᎤᎧᎵᏨᎯ, ᏄᎵᏍᏔᏅ ᎾᏍᎩ ᏂᏗᎾᏰᏍᎬᎾ ᎢᏳᎵᏍᏙᏗᏱ ᏗᎫᎪᏙᏗᏱ ᎢᎦ ᎨᏎᏍᏗ, ᏅᏗᎦᎵᏍᏙᏗᎭ ᎾᏍᎩᏯ ᏄᏍᏛ ᎾᏍᏉ ᎾᏍᎩ ᏂᎦᏍᏗ ᎠᏴ ᎠᏂ ᎡᎶᎯ.
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A-ni hi-a i-ga-da-ge-yu-di ge-sv u-ka-li-tsv-hi, nu-li-s-ta-nv na-s-gi ni-di-na-ye-s-gv-na i-yu-li-s-do-di-yi di-gu-go-do-di-yi i-ga ge-se-s-di, nv-di-ga-li-s-do-di-ha na-s-gi-ya nu-s-dv na-s-quo na-s-gi ni-ga-s-di a-yv a-ni e-lo-hi.
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| There is no fear in love; but perfect love casteth out fear: because fear hath torment. He that feareth is not made perfect in love.
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| ᎥᏝ ᎦᎾᏰᎯᏍᏗ ᎨᏒ ᏳᏓᏑᏯ ᎠᏓᎨᏳᏗ ᎨᏒᎢ, ᎤᎧᎵᏨᎯᏍᎩᏂ ᎨᏒ ᎠᏓᎨᏳᏗ ᎦᏄᎪᏫᏍᎦ ᎦᎾᏰᎯᏍᏗ ᎨᏒᎢ; ᎦᎾᏰᎯᏍᏗᏰᏃ ᎨᏒ ᎠᎩᎵᏯ ᎤᏪᎭ; ᎩᎶ ᏥᎦᎾᏰᏍᎪᎢ ᎥᏝ ᎤᎧᎵᏨᎯ ᏱᎨᏐ ᎠᏓᎨᏳᏗ ᎨᏒᎢ.
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V-tla ga-na-ye-hi-s-di ge-sv yu-da-su-ya a-da-ge-yu-di ge-sv-i, u-ka-li-tsv-hi-s-gi-ni ge-sv a-da-ge-yu-di ga-nu-go-wi-s-ga ga-na-ye-hi-s-di ge-sv-i; ga-na-ye-hi-s-di-ye-no ge-sv a-gi-li-ya u-we-ha; gi-lo tsi-ga-na-ye-s-go-i v-tla u-ka-li-tsv-hi yi-ge-so a-da-ge-yu-di ge-sv-i.
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| We love him, because he first loved us.
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| ᎡᏗᎨᏳᎠ ᎾᏍᎩ ᏅᏗᎦᎵᏍᏙᏗᎭ ᎠᏴ ᎢᎬᏱ ᎢᎩᎨᏳᎯᏳ ᏄᎵᏍᏔᏅᎢ.
|
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E-di-ge-yu-a na-s-gi nv-di-ga-li-s-do-di-ha a-yv i-gv-yi i-gi-ge-yu-hi-yu nu-li-s-ta-nv-i.
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| If a man say, I love God, and hateth his brother, he is a liar: for he that loveth not his brother whom he hath seen, how can he love God whom he hath not seen?
|
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| ᎢᏳᏃ ᎩᎶ ᎤᏁᎳᏅᎯ ᏥᎨᏳᎠ ᏯᏗᎭ, ᏯᏍᎦᎦᏃ ᏗᎾᏓᏅᏟ, ᎦᏰᎪᎩ ᎾᏍᎩ, ᎤᎨᏳᎯᏰᏃ ᏂᎨᏒᎾ ᏱᎩ ᏗᎾᏓᏅᏟ ᎾᏍᎩ ᎤᎪᎲᎯ, ᎦᏙ ᏱᎦᎵᏍᏙᏓ ᎤᎨᏳᎯᏳ ᏱᎩ ᎤᏁᎳᏅᎯ ᎤᎪᎲᎯ ᏂᎨᏒᎾ ᎨᏒᎢ.
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I-yu-no gi-lo U-ne-la-nv-hi tsi-ge-yu-a ya-di-ha, ya-s-ga-ga-no di-na-da-nv-tli, ga-ye-go-gi na-s-gi, u-ge-yu-hi-ye-no ni-ge-sv-na yi-gi di-na-da-nv-tli na-s-gi u-go-hv-hi, ga-do yi-ga-li-s-do-da u-ge-yu-hi-yu yi-gi U-ne-la-nv-hi u-go-hv-hi ni-ge-sv-na ge-sv-i.
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| And this commandment have we from him, That he who loveth God love his brother also.
|
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| ᎯᎠᏃ ᎾᏍᎩ ᏗᎧᎿᎭᏩᏛᏍᏗ ᎢᎩᏁᎸᎯ, ᎾᏍᎩ ᎢᏳᏃ ᎩᎶ ᎤᎨᏳᎯᏳ ᎨᏎᏍᏗ ᎤᏁᎳᏅᎯ, ᎾᏍᏉ ᎤᎨᏳᎯᏳ ᎨᏎᏍᏗ ᏗᎾᏓᏅᏟ.
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Hi-a-no na-s-gi di-ka-hna-wa-dv-s-di i-gi-ne-lv-hi, na-s-gi i-yu-no gi-lo u-ge-yu-hi-yu ge-se-s-di U-ne-la-nv-hi, na-s-quo u-ge-yu-hi-yu ge-se-s-di di-na-da-nv-tli.
|
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Class: HttpArchive::Page
- Inherits:
-
Object
- Object
- HttpArchive::Page
- Defined in:
- lib/http_archive/classes.rb
Overview
Holds general data of the page and its loading process
Instance Attribute Summary (collapse)
-
- (String) id
Returns the id of the page.
-
- (Fixnum) on_content_load
Returns the amount of milliseconds sice the page load started and the content is loaded.
-
- (Fixnum) on_load
Returns the amount of milliseconds sice the page load started and the page itself is loaded.
-
- (String) started_datetime
Returns the datetime as string when loading of the page started.
-
- (String) title
Returns the title of the page.
Instance Attribute Details
- (String) id
Returns the id of the page
30 31 32 |
# File 'lib/http_archive/classes.rb', line 30 def id @id end |
- (Fixnum) on_content_load
Returns the amount of milliseconds sice the page load started and the content is loaded
36 37 38 |
# File 'lib/http_archive/classes.rb', line 36 def on_content_load @on_content_load end |
- (Fixnum) on_load
Returns the amount of milliseconds sice the page load started and the page itself is loaded
39 40 41 |
# File 'lib/http_archive/classes.rb', line 39 def on_load @on_load end |
- (String) started_datetime
Returns the datetime as string when loading of the page started
27 28 29 |
# File 'lib/http_archive/classes.rb', line 27 def started_datetime @started_datetime end |
- (String) title
Returns the title of the page
33 34 35 |
# File 'lib/http_archive/classes.rb', line 33 def title @title end |


RA Module
Subscriber
Register Subscriber Email
RA
View All Certificate Requests
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Logout
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{% extends 'base.html' %} {% load crispy_forms_tags %} {% block content %}
Please visit Trello authorization page and copy token. Next to paste it in form below.


I've been hesitant to try Meth even thought most of my drug experiments have been positive. In my area (Northern Arizona) there are quite a few tweekers who have let their use of the drug get way out of control. While drinking with a friend a few days ago I suggested that he find some Meth. He made some calls and late that night it came, he took his portion and left me with what I had paid for. I had been drinking all night and was more concerned with sleeping than getting really messed up.
Today, completly sober and with a little food in my stomach I tried the Meth I acquired that night. I'm not sure of the dosage, all I can say is that I took about %60 of what I had (I think I had a 'regular dosege'). I have an aversion to snorting drugs so I decided to take it orally. I washed it down with some OJ and watched some TV. A little over half an hour later I started to feel a steadily building energy.  I cleaned my room and much of the house and just felt generally more useful. I didn't hesitate to do mundane tasks like I normally would. I then called up a girl I just met and asked her to a movie. We met up and I noticed that I wasn't nervous, instead I felt like I was more engaging, talkative but not babbling, probably because of the slight euphoria combined with energy.
Apparently the most common method for taking speed is snorting which gives an actual rush but I'm very satisfied with my low-dose taken orally, it has been satisfying and interesting. I've retained all my ability to focus but letting my mind wander with music is also fun.
The negative effects have been mild, some slight jaw clenching, and a very mild shaking of the hands and sometimes feet, nothing too obvious. I've also been sweating some (probably because of the increased metabolism). But there hasn't been an adverse reaction. Remember to keep drinking water.
Timeline-o-meth:
5:00pm Took approx %45 of supply
5:45pm started to feel effects
6:00pm Took another %15 of supply
6:30pm Plateau of effects
3:00am Time of writing this, somewhat diminished effects but still going.
Its not my drug of choice but still interesting. I would do this again......in moderation.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


North
Kino
:(
Access denied
Error 403
Oops, you don't have the right to access this directory.
Go to homepage


{% extends "base.html" %} {% block title %}Flagging Program - Live Water Quality Alerts{% endblock %} {% block content %}
CRWA's Flagging Program uses a system of color-coded flags to indicate whether or not the river's water quality is safe for boating at eleven boating locations between Watertown and Boston. Flag colors are based on E. coli and cyanobacteria (blue-green algae) levels:
CRWA estimates E. coli bacteria levels using models that take into account a variety of environmental factors including rainfall and river flow. CRWA collects water samples weekly to verify the model forecasts. Cyanobacteria monitoring is conducted every 1-2 weeks.
The best way to keep up with the CRWA flagging updates is to sign up for email alerts. Email communications from the CRWA are the official source for status updates relating to the Charles River.
You can also follow the CRWA Flagging Program on Twitter to get automated updates from this website. Our Twitterr bot posts automatic updates every time the predictive model is run.
{% endblock %}


About Brochure’ 16¶
Admission brochure of Gbu for 2016 built with Sphinx
Developed Under:
Amit K. Awasthi
Developed By:
Shobhit Sharma
Source : github_link
For queries<EMAIL_ADDRESS>Admission brochure of Gbu for 2016 built with Sphinx
Developed Under:
Amit K. Awasthi
Developed By:
Shobhit Sharma
Source : github_link
For queries : shobhit95sharma@gmail.com


why not maximize the amount of rewards you get on money you're already spending?
if you haven't already, make sure you've read the read section here


The Rainmaker
As reviewed by James Brundage
They're ever-present. Every day goes by and someone cracks a joke about them. We loathe them more than anything else in the world and there are more bad jokes about them than the Catholic Church.
They're lawyers. Those three-piece suit wearers of Armani who make upwards of a hundred dollars an hour. And they're the one sect of society that is universally hated and takes pride in it.
And, with every novel and every film made out of them, John Grisham makes us love them. In the newest, best, and most interesting John Grisham book adapted to the screen we see a rarity -- a mix of old talent and new talent. Matt Damon stars as a young, idealistic lawyer tackling an insurance company. He's mixed with the directorial skills of Francis Ford Coppola ("The Godfather"), the young talent of Claire Daines (tackling the serious role of a wife being abused by her husband), of John Voight, and of Danny DeVito).
So what is it that John Grisham chooses to tackle in his moral crusade to give lawyers a good name? The lawyers that have a bad name: the ambulance chasing, insurance-settlement getting lawyers that make the word "schiester" what it is.
The film makes you love a character that does just that for a living, explaining all of the things which we wonder about (such as why in the hell they drool when the sirens blare, anyway?).
What's the best thing about this new film? The classic realism that Francis Ford Coppala has always shown (with the exception of "Dracula", of course). In this film, he reasserts his mastery over the American Silver Screen. A sure-fire winner, and a definite Oscar Contender for this year. See it if you're in need for a good dose of idealism.
http://fly.to/criticsheave
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


|
Bosch XDK110 Solace MQTT App
2.0.1
MQTT Reference Application for the Bosch XDK110
|
Go to the source code of this file.
Functions | |
| Retcode_T | AppConfig_Init (const char *deviceId) |
| Initialize the module. More... | |
| const SNTP_Setup_T * | AppConfig_GetSntpSetupInfoPtr (void) |
| Returns the SNTP setup info as read from the config. More... | |
| const WLAN_Setup_T * | AppConfig_GetWlanSetupInfoPtr (void) |
| Returns the WLAN setup info as read from the config. More... | |
| const AppXDK_MQTT_Connect_T * | AppConfig_GetMqttConnectInfoPtr (void) |
| Returns the Mqtt connect info. More... | |
| const char * | AppConfig_GetBaseTopicStr (void) |
| Returns baseTopic string. More... | |
| bool | AppConfig_GetIsMqttBrokerConnectionSecure (void) |
| Returns the isSecure flag. More... | |


Chapter 6 Bivariate linear regression models
6.1 Seminar
rm(list = ls())
6.1.1 Packages
We will need to install a package in this week’s seminar. Packages are like apps for your phone. R comes with some core functionality and allows users to add functionality. These add-ons are called packages. We first need to install a package (but only once). Every time we start R, we need to load the package.
To install a package, we write install.packages("package.name")
. To load a package, we write library(package.name)
.
This week’s package is called texreg
and it makes it easy to produce publication quality output from our regression models. We’ll discuss this package in more detail as we go along. For now let’s install the package and then load the package.
install.packages("texreg") # install only once
library(texreg) # load in the beginning of every R session
We will use a dataset collected by the US census bureau that contains several socioeconomic indicators. You can load the dataset directly from the internet.
dat <- read.csv("https://raw.githubusercontent.com/philippbroniecki/statistics1/master/data/communities.csv")
We will be exploring the relationship between the unemployment rate and low education. The variable names for these variables are not terribly clear and so first we will rename these variables using the names()
function and the which()
function from last week.
names(dat)
[1] "state" "county" "community"
[4] "communityname" "fold" "population"
[7] "householdsize" "racepctblack" "racePctWhite"
[10] "racePctAsian" "racePctHisp" "agePct12t21"
[13] "agePct12t29" "agePct16t24" "agePct65up"
[16] "numbUrban" "pctUrban" "medIncome"
[19] "pctWWage" "pctWFarmSelf" "pctWInvInc"
[22] "pctWSocSec" "pctWPubAsst" "pctWRetire"
[25] "medFamInc" "perCapInc" "whitePerCap"
[28] "blackPerCap" "indianPerCap" "AsianPerCap"
[31] "OtherPerCap" "HispPerCap" "NumUnderPov"
[34] "PctPopUnderPov" "PctLess9thGrade" "PctNotHSGrad"
[37] "PctBSorMore" "PctUnemployed"
names(dat)[which(names(dat) == "PctUnemployed")] <- "UnemploymentRate"
names(dat)[which(names(dat) == "PctNotHSGrad")] <- "NoHighSchool"
The first variable (UnemploymentRate
) measures the proportion of citizens in each community who are unemployed. The second variable (NoHighSchool
) measures the proportion of citizens in each community who failed to finish high-school.
If we summarize these variables with the summary()
function, we will see that they are both measured as proportions (they vary between 0 and 1):
summary(dat$UnemploymentRate)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.2200 0.3200 0.3635 0.4800 1.0000
summary(dat$NoHighSchool)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.2300 0.3600 0.3833 0.5100 1.0000
It will be a little easier to interpret the regression output if we convert these to percentages rather than proportions. We can do this with the following lines of code:
dat$UnemploymentRate <- dat$UnemploymentRate*100
dat$NoHighSchool <- dat$NoHighSchool*100
We can begin by drawing a scatterplot with the percentage of unemployed people on the y-axis and the percentage of adults without high-school education on the x-axis.
plot(
y = dat$UnemploymentRate,
x = dat$NoHighSchool,
xlab = "Adults without High School education (%)",
ylab = "Unemployment (%)",
bty = "n",
pch = 16,
col = rgb(red = 110, green = 200, blue = 110, alpha = 80, maxColorValue = 255)
)
From looking at the plot, what is the association between the unemployment rate and lack of high-school level education?
In order to answer that question empirically, we will run a linear regression using the lm()
function in R. The lm()
function needs to know a) the relationship we’re trying to model and b) the dataset for our observations. The two arguments we need to provide to the lm()
function are described below.
| Argument | Description |
|---|---|
formula |
The formula describes the relationship between the dependent and independent variables, for example dependent.variable ~ independent.variable In our case, we’d like to model the relationship using the formula: UnemploymentRate ~ NoHighSchool |
data |
This is simply the name of the dataset that contains the variable of interest. In our case, this is the merged dataset called communities . |
For more information on how the lm()
function works, type help(lm) in R.
model1 <- lm(UnemploymentRate ~ NoHighSchool, data = dat)
The lm()
function has modeled the relationship between PctUnemployed
and NoHighSchool
and we’ve saved it in an object called model1
. Let’s use the summary()
function to see what this linear model looks like.
summary(model1)
Call:
lm(formula = UnemploymentRate ~ NoHighSchool, data = dat)
Residuals:
Min 1Q Median 3Q Max
-42.347 -8.499 -1.189 7.711 56.470
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.89520 0.64833 12.18 <0.0000000000000002 ***
NoHighSchool 0.74239 0.01496 49.64 <0.0000000000000002 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.52 on 1992 degrees of freedom
Multiple R-squared: 0.553, Adjusted R-squared: 0.5527
F-statistic: 2464 on 1 and 1992 DF, p-value: < 0.00000000000000022
<IP_ADDRESS> Interpreting Regression Output
The output from lm()
might seem overwhelming at first so let’s break it down one item at a time.
| # | Description |
|---|---|
The dependent variable, also sometimes called the outcome variable. We are trying to model the effects of NoHighSchool on UnemploymentRate so UnemploymentRate is the dependent variable. |
|
The independent variable or the predictor variable. In our example, NoHighSchool is the independent variable. |
|
| The differences between the observed values and the predicted values are called residuals. R produces a summary of the residuals. | |
The coefficients for the intercept and the independent variables. Using the coefficients we can write down the relationship between the dependent and the independent variables as: UnemploymentRate = 7.8952023 + ( 0.7423853 * NoHighSchool ) This tells us that for each unit increase in the variable NoHighSchool , the UnemploymentRate increases by 0.7423853. |
|
| The p-value for each of the coefficients in the model. Recall that according to the null hypotheses, the value of the coefficient of interest is zero. The p-value tells us whether can can reject the null hypotheses or not. | |
| The standard error estimates the standard deviation of the sampling distribution of the coefficients in our model. We can think of the standard error as the measure of precision for the estimated coefficients. | |
| The t statistic is obtained by dividing the coefficients by the standard error. | |
| The R-squared and adjusted R-squared tell us how much of the variance in our model is accounted for by the independent variable. The adjusted R-squared is always smaller than R-squared as it takes into account the number of independent variables and degrees of freedom. |
Now let’s add a regression line to the scatter plot using the abline()
function.
## First we run the same "plot" function as before
plot(
UnemploymentRate ~ NoHighSchool, data = dat,
xlab = "Adults without High School education (%)",
ylab = "Unemployment (%)",
frame.plot = FALSE,
pch = 16,
col = rgb(red = 110, green = 200, blue = 110, alpha = 80, maxColorValue = 255)
)
## Then we use the "abline" function to plot the regression line from our saved model object
abline(model1, lwd = 3,
col = rgb(red = 230, green = 150, blue = 0, alpha = 255, maxColorValue = 255))
We can see by looking at the regression line that it matches the coefficients we estimated above. For example, when NoHighSchool
is equal to zero (i.e. where the line intersects the Y-axis), the predicted value for UnemploymentRate
seems to be above 0 but below 10. This is good, as the intercept coefficient we estimated in the regression was 7.895
.
Similarly, the coefficient for the variable NoHighSchool
was estimated to be 0.74239
, which implies that a one point increase in the percentage of citizens with no high-school education is associated with about .74 of a point increase in the percentage of citizens who are unemployed. The line in the plot seems to reflect this: it is upward sloping, so that higher levels of the no high-school variable are associated with higher levels of unemployment, but the relationship is not quite 1-to-1. That is, for each additional percentage point of citzens without high school education, the percentage of citizens who are unemployed increases by a little less than one point.
While the summary()
function provides a slew of information about a fitted regression model, we often need to present our findings in easy to read tables similar to what you see in journal publications. The texreg
package we installed earlier allows us to do just that.
Let’s take a look at how to display the output of a regression model on the screen using the screenreg()
function from texreg
.
screenreg(model1)
=========================
Model 1
-------------------------
(Intercept) 7.90 ***
(0.65)
NoHighSchool 0.74 ***
(0.01)
-------------------------
R^2 0.55
Adj. R^2 0.55
Num. obs. 1994
RMSE 13.52
=========================
*** p < 0.001, ** p < 0.01, * p < 0.05
Here, the output includes some of the most salient details we need for interpretation. We can see the coefficient for the NoHighSchool
variable, and the estimated coefficient for the intercept. Below these numbers, in brackets, we can see the standard errors. The table also reports the R^2, the adjusted R^2, the number of observations (n) and the root-mean-squared-error (RMSE).
One thing to note is that the table does not include either t-statistics or p-values for the estimated coefficents. Instead, the table employs a common device of using stars to denote whether a variable is statistically significant at a given alpha level.
***
indicates that the coefficient is significant at the 99.9% confidence level (alpha = 0.001)**
indicates that the coefficient is significant at the 99% confidence level (alpha = 0.01)*
indicates that the coefficient is significant at the 95% confidence level (alpha = 0.05)
Returning to our example, are there other variables that might affect the unemployment rate in our dataset? For example, is the unemployment rate higher in rural areas? To answer this question, we can swap NoHighSchool
for a different independent variable. Let’s use the variable population
, which measures the proportion of adults who live in cities (rather than rural areas). Again, we can transform this proportion to a percentage with the following code:
dat$population <- dat$population*100
Let’s fit a linear model using population
as the independent variable:
model2 <- lm(UnemploymentRate ~ population, data = dat)
summary(model2)
Call:
lm(formula = UnemploymentRate ~ population, data = dat)
Residuals:
Min 1Q Median 3Q Max
-35.252 -14.715 -3.946 11.054 64.980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.02042 0.49206 71.171 < 0.0000000000000002 ***
population 0.23139 0.03532 6.552 0.000000000072 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.01 on 1992 degrees of freedom
Multiple R-squared: 0.0211, Adjusted R-squared: 0.02061
F-statistic: 42.93 on 1 and 1992 DF, p-value: 0.00000000007201
We can show regression line from the model2
just like we did with our first model.
plot(
UnemploymentRate ~ population, data = dat,
xlab = "Adults living in cities (%)",
ylab = "unemployment (%)",
frame.plot = FALSE,
pch = 16,
col = rgb(red = 110, green = 200, blue = 110, alpha = 100, maxColorValue = 255)
)
abline(model2, lwd = 2,
col = rgb(red = 230, green = 150, blue = 0, alpha = 255, maxColorValue = 255))
So we now have two models! Often, we will want to compare two estimated models side-by-side. We might want to say how the coefficients for the independent variables we included differ in model1
and model2
, for example. Or we may want to ask: Does model2
offer a better fit than model1
?
It is often useful to print the salient details from the estimated models side-by-side. We can do this by using the screenreg()
function.
screenreg(list(model1, model2))
======================================
Model 1 Model 2
--------------------------------------
(Intercept) 7.90 *** 35.02 ***
(0.65) (0.49)
NoHighSchool 0.74 ***
(0.01)
population 0.23 ***
(0.04)
--------------------------------------
R^2 0.55 0.02
Adj. R^2 0.55 0.02
Num. obs. 1994 1994
RMSE 13.52 20.01
======================================
*** p < 0.001, ** p < 0.01, * p < 0.05
What does this table tell us?
- The first column replicates the results from our first model. We can see that a one point increase in the percentage of citizens without high-school education is associated with an increase of 0.74 percentage points of unemployment, on average.
- The second column gives us the results from the second model. Here, a one point increase in the percentage of citizens who live in cities is associated with an increase of 0.23 percentage points of unemployment, on average
- We can also compare the R^2 values from the two models. The R^2 for
model1
is 0.55 and formodel2
is 0.02. This suggests that the model withNoHighSchool
as the explanatory variable explains about 55% of the variation in unemployment. The model withpopulation
as the explanatory variable, on the other hand, explains just 2% of the variation in unemployment.
Finally, and this is something that might help with your coursework, let’s save the same output as a Microsoft Word document using htmlreg()
.
htmlreg(list(model1, model2), file = "Regressions_on_Unemployment.doc")
6.1.2 Fitted values
Once we have estimated a regression model, we can use that model to produce fitted values. Fitted values represent our “best guess” for the value of our dependent variable for a specific value of our independent variable.
To calculate fitted values we use the predict()
function. Let’s say that, on the basis of model1
we would like to know what the unemployment rate is likely to be for a community where the percentage of adults without a high-school education is equal to 10%.
The predict function takes two main arguments.
| Argument | Description |
|---|---|
object |
The object is the model object that we would like to use to produce fitted values. Here, we would like to base the analysis on model1 and so specify object = model1 here. |
newdata |
This is an optional argument which we use to specify the values of our independent variable(s) that we would like fitted values for. If we leave this argument empty, R will automatically calculate fitted values for all of the observations in the data that we used to estimate the original model. If we include this argument, we need to provide a data.frame which has a variable with the same name as the independent variable in our model. Here, we specify newdata = data.frame(NoHighSchool = 10) , as we would like the fitted value for a community where 10% of adults did not complete high-school. |
predict(model1, newdata = data.frame(NoHighSchool = 10))
1
15.31906
Note that in this simple case, we can calculate the fitted value manually. The fitted value formula is:
\[\hat{Y}_{i} = \hat{\beta}_0 + \hat{\beta}_1 * X_i\]
So, we can substitute in the relevant coefficients from model1
and the number 10 for our X variable (as we want a fitted value for when X is equal to 10), and we get:
\[\hat{Y}_{i} = 7.9 + 0.74 * 10 = 15.3\]
which is the same as the result we obtained from the predict()
function! The good thing about the predict()
function, however, is that we will be able to use it for all the models we study on this course, and it can be useful for calculating many different fitted values. This will save a lot of time which might be wasted doing the calculations by hand.
6.1.3 Additional Resources
6.1.4 Exercises
- Open a new script and save it as assignment6.
- Clear your workspace.
- Load the non-western foreigners dataset from week 2.
- Estimate a model that explains subjective number of immigrants per 100 British citizens using only one independent variable. Justify your choice. (You do not have to pick the best variable but try to make a reasonable argument why more of x should lead to more/less of y).
- Plot a scatterplot of the relationship and add the regression line to the plot.
- Interpret the regression output and try to imagine that you are communicating your results to someone who does not know anything about statistics.
- Estimate another model (i.e. choose a different independent variable) on the same dependent variable. Justify the choice.
- Interpret the new regression output.
- Compare the two models and explain which one you would choose.
- Produce a table with both models next to each other in some text document. You can use
texreg
from the seminar, do it manually, or use something else. - Consider the following table. This analysis asks whether individuals who have spent longer in education have higher yearly earnings. The analysis is based on a sample of 300 individuals. The dependent variable in this analysis is the yearly income of the individual in UK pounds (
earnings
). The independent variable measures the number of years the individual spent in full-time education (education
).
- Interpret the coefficient on the
education
variable. - Using the values given in the table, calculate the test-statistic
- Can we reject the null hypothesis of no effect at the 95% confidence level? (Just looking at the stars is not sufficient here! How can we work out the result of the hypothesis test?)
=========================
Model 1
-------------------------
(Intercept) 3663.85
(2326.99)
education 1270.81 ***
(160.97)
-------------------------
R^2 0.17
Adj. R^2 0.17
Num. obs. 300
RMSE 14018.52
=========================
*** p < 0.001, ** p < 0.01, * p < 0.05
- Save the script that includes all previous tasks.
- Source your script, i.e. run the entire script all at once without error message.


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Part IX: UI and Combat
EditNow we want to show the player what's going on. So we need to have some kind of HUD on the screen to tell them what their current/max health is, and how many coins they have. For the health icon you can use this image , or make your own. Make sure you save this in "assets/images".
We'll start by making a new HUD class which will hold all our HUD elements:
package; import flixel.FlxBasic; import flixel.FlxG; import flixel.FlxSprite; import flixel.group.FlxTypedGroup; import flixel.text.FlxText; import flixel.util.FlxColor; using flixel.util.FlxSpriteUtil; class HUD extends FlxTypedGroup<FlxSprite> { private var _sprBack:FlxSprite; private var _txtHealth:FlxText; private var _txtMoney:FlxText; private var _sprHealth:FlxSprite; private var _sprMoney:FlxSprite; public function new() { super(); _sprBack = new FlxSprite().makeGraphic(FlxG.width, 20, FlxColor.BLACK); _sprBack.drawRect(0, 19, FlxG.width, 1, FlxColor.WHITE); _txtHealth = new FlxText(16, 2, 0, "3 / 3", 8); _txtHealth.setBorderStyle(FlxText.BORDER_SHADOW, FlxColor.GRAY, 1, 1); _txtMoney = new FlxText(0, 2, 0, "0", 8); _txtMoney.setBorderStyle(FlxText.BORDER_SHADOW, FlxColor.GRAY, 1, 1); _sprHealth = new FlxSprite(4, _txtHealth.y + (_txtHealth.height/2) - 4, AssetPaths.health__png); _sprMoney = new FlxSprite(FlxG.width - 12, _txtMoney.y + (_txtMoney.height/2) - 4, AssetPaths.coin__png); _txtMoney.alignment = "right"; _txtMoney.x = _sprMoney.x - _txtMoney.width - 4; add(_sprBack); add(_sprHealth); add(_sprMoney); add(_txtHealth); add(_txtMoney); forEach(function(spr:FlxSprite) { spr.scrollFactor.set(); }); } public function updateHUD(Health:Int = 0, Money:Int = 0):Void { _txtHealth.text = Std.string(Health) + " / 3"; _txtMoney.text = Std.string(Money); _txtMoney.x = _sprMoney.x - _txtMoney.width - 4; } }
This class extends
FlxTypedGroup<FlxSprite>
so that it can hold all of our FlxSprite objects. It is composed of 5 different items: a background (black, with a 1-pixel thick white line along the bottom), 2 FlxText objects: 1 for Health, and 1 for Money, and 2 FlxSprites, for the icons to go next to the FlxTexts. At the end of our constructor, we have a forEach call - we use this to iterate through each of the items in this group, and it just sets their scrollFactor.x and scrollFactor.y to 0, meaning, even if the camera scrolls, all of these items will stay at the same position relative to the screen.Finally, we have a function that we can call from anywhere to tell the HUD what it should display.
Now let's get it to work and have it update whenever we pick up a coin. In your PlayState, add to the top of the class:
private var _hud:HUD; private var _money:Int = 0; private var _health:Int = 3;
In the create function, before
super.create()
, add:_hud = new HUD(); add(_hud);
Finally, in the playerTouchCoin function we added earlier, somewhere inside the if statement, add:
_money++; _hud.updateHUD(_health, _money);
Go ahead and test out your game, and the HUD should update each time you pick up a coin!
If we had a way to 'hurt' the player, we could also update the health on the HUD… but in order to do that, we need to figure out how we're going to do combat!
Let's begin by establishing what we want our combat system to achieve. First, we're not going to be making the next Final Fantasy game here, this is just a basic demonstration to show how a few different elements can work. So, I think all we want to do is have a simple interface that appears when the player touches an enemy that shows the Player's health, and the Enemy's health (in a health bar, for obfuscation), and gives the player 2 options: FIGHT or FLEE.
If they choose to fight, we'll roll some random chance checks to see if the player hits the enemy, and if the enemy hits the player - a hit will do 1 damage. Once the enemy dies, we'll continue on. If they choose to flee, we'll do a check to see if they do flee or not - if they do, the interface closes and the enemy will be stunned for a few seconds so the player can move away. If they fail to flee, the enemy will get a free hit against the player. We'll also show the damage and Misses on the interface.
This all seems simple enough, but it's actually going to require several components working together to make it work. It's the most complicated piece of our game so far.
The first component will be our CombatHUD class. This is a pretty big class - it's going to do most of the heavy lifting with our combat logic. You can see the complete class here:
Take some time to read through it to see how it works, then add it to your project. You will need to add a small function to our Enemy.hx class, as well:
public function changeEnemy(EType:Int):Void { if (etype != EType) { etype = EType; loadGraphic("assets/images/enemy-" + Std.string(etype) + ".png", true, 16, 16); } }
Next, we need to get our CombatHUD into our PlayState. Add this to the top of the PlayState Class:
private var _inCombat:Bool = false; private var _combatHud:CombatHUD;
Move down to the create function, and, after we add the HUD, and before we call super.create, add:
_combatHud = new CombatHUD(); add(_combatHud);
Go down to our update function, and change it so that we're ONLY checking for collision and overlaps and things when we're not in combat. Everything after the super.update should look like this:
if (!_inCombat) { FlxG.collide(_player, _mWalls); FlxG.overlap(_player, _grpCoins, playerTouchCoin); FlxG.collide(_grpEnemies, _mWalls); _grpEnemies.forEachAlive(checkEnemyVision); FlxG.overlap(_player, _grpEnemies, playerTouchEnemy); } else { if (!_combatHud.visible) { _health = _combatHud.playerHealth; _hud.updateHUD(_health, _money); if (_combatHud.outcome == VICTORY) { _combatHud.e.kill(); } else { _combatHud.e.flicker(); } _inCombat = false; _player.active = true; _grpEnemies.active = true; } }
So, we're adding a check to see if the player touches an enemy. If they do, we'll call a callback to see if we should start combat or not.
If we're in combat, we're simply going to keep checking to see if the combatHUD is still visible - once it becomes invisible, we know that combat has finished, and we can determine the outcome. If the outcome is VICTORY (one of our 4 enum values), we will kill the enemy, but if the player Fled the battle, we will make the enemy flicker, to show that the player is safe from fighting it again for a short amount of time.
Next, lets add the functions to handle the player touching an enemy:
private function playerTouchEnemy(P:Player, E:Enemy):Void { if (P.alive && P.exists && E.alive && E.exists && !E.isFlickering()) { startCombat(E); } } private function startCombat(E:Enemy):Void { _inCombat = true; _player.active = false; _grpEnemies.active = false; _combatHud.initCombat(_health, E); }
All we're doing here is checking to see if the player is alive and exists and the enemy is alive and exists and not flickering (flickering enemies are those we've just fled from), and if so, we start combat.
The startCombat function simply sets our inCombat flag (so we know not to do collisions), and sets the player and all the enemies to inactive, so they no longer update.
Finally, we call the initCombat function in our CombatHUD, which initializes it and makes it start working.
Finally, we want enemies that are flickering to not move - they should act kind of stunned for a second after the enemy flees.
In the Enemy class, under update, add:
if (isFlickering()) return;
At the very top, before doing anything else in that function.
You will also need to add
using flixel.util.FlxSpriteUtil;
to the top of the Enemy.hx class.
And that should do it! Test out your game and make sure that it works!
Next, we'll cover winning and losing and setting up all our different FlxStates.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Enjoy a little luxury with ingredients straight from your freezer and store cupboard - it's ready in under 30 minutes, too!
Each serving provides 424kcal, 34g protein, 32g carbohydrate (of which 8g sugars), 17g fat (of which 7g saturates), 4g fibre and 1.7g salt.
Heat the butter and oil in a large, non-stick frying pan over a medium heat. Add the onion and fry for 3 minutes, stirring regularly, until completely softened but not coloured. Sprinkle over the flour and stir the onions well.
In a measuring jug, mix the milk and water. Gradually add 700ml/1¼ pints of the liquid to the onions, stirring after each addition.
Add the potatoes and salt to the pan and season generously with freshly ground black pepper. Bring the mixture to the boil, then reduce the heat until the mixture is simmering. Simmer, stirring regularly, for 10-12 minutes, or until the potatoes are almost tender. Stir in the sweetcorn and leeks and return the mixture to a gentle simmer.
Place the smoked-haddock fillets on top of the chowder and continue to simmer for 10-15 minutes, or until the fish has thawed and the flesh flakes easily when prodded with a knife. (This will depend on the thickness of each fillet.) Turn the fish twice as the fillets simmer, without allowing them to break up if possible.
Carefully remove the cooked smoked-haddock fillets from the chowder using a slotted spoon and set aside in a warm place. (Remove the skin at this point if you prefer.) Stir the remaining milk and water into the pan and return to a simmer, stirring.
If using the eggs, fill a wide saucepan half full with boiling water. Reduce the heat until the water is barely simmering. Carefully crack the eggs into the water one at a time, spacing them well apart. Poach very gently for 3-4 minutes, or until the whites have set but the yolks remain runny. Remove the poached eggs from the pan using a slotted spoon
To serve, spoon the chowder into warm bowls, seasoning again with salt and pepper, if necessary. Top each serving with one of the haddock fillets and a poached egg, if using. Garnish with parsley, if using. Serve with slices of crusty bread.


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{% extends 'layout.html' %} {% block title %}Profile{% endblock %} {% block content %} {% set profile = data['user:user-profile'] %}
Your account details:
| Display Name | {{ profile['user:user-display-name'] }} |
| Nickname | {{ profile['user:user-nickname'] }} |
| User ID | {{ profile['user:user-id'] }} |
| User Email | {{ profile['user:user-email'] }} |
| Phone Number | {{ profile['user:user-phone-number'] }} |
| Registration Date | {{ profile['user:user-registration-date']|datetime }} |
| Active Ads | {{ profile['user:user-active-ad-count'] }} |
| Reply Rate | {{ '%s%%' % profile['user:reply-rate'] if profile['user:reply-rate'] is not none else '' }} |
| Responsiveness | {{ profile['user:user-responsiveness'] }} |
| Total Reviews | {{ profile['user:total-reviews'] }} |
| Average Review Score | {{ profile['user:average-review-score'] }} |


| Trait1 | Trait2 | pvalue |
|---|---|---|
| pct. time in light | hot plate latency | 0.109 |
| distance traveled | hot plate latency | 0.108 |
| pct. time in light | distance traveled | 0.871 |
Fred Boehm
EMAIL<EMAIL_ADDRESS>WEBSITE: https://fboehm.us/
ORCID: 0000-0002-1644-5931
qtl2pleio
R package: https://github.com/fboehm/qtl2pleio
Jiang, Changjian, and Zhao-Bang Zeng. 1995. “Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci.” Genetics 140 (3). Genetics Soc America: 1111–27.
Keller, Mark P, Daniel M Gatti, Kathryn L Schueler, Mary E Rabaglia, Donnie S Stapleton, Petr Simecek, Matthew Vincent, et al. 2018. “Genetic Drivers of Pancreatic Islet Function.” Genetics. Genetics Soc America, genetics–300864.
Logan, Ryan W, Raymond F Robledo, Jill M Recla, Vivek M Philip, Jason A Bubier, Jeremy J Jay, Carter Harwood, et al. 2013. “High-Precision Genetic Mapping of Behavioral Traits in the Diversity Outbred Mouse Population.” Genes, Brain and Behavior 12 (4). Wiley Online Library: 424–37.
Recla, Jill M, Raymond F Robledo, Daniel M Gatti, Carol J Bult, Gary A Churchill, and Elissa J Chesler. 2014. “Precise Genetic Mapping and Integrative Bioinformatics in Diversity Outbred Mice Reveals Hydin as a Novel Pain Gene.” Mammalian Genome 25 (5-6). Springer: 211–22.


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| Mathematics | |||
| and | many more ... |
FURY is a powerful tool for engineers and visualisation experts. It allows them to create high-quality visualisations quickly and easily, without the need for extensive programming knowledge. Its functional programming paradigm and support for large data sets make it an ideal tool for visualising complex systems and data, such as those used in engineering and scientific research.
View MoreFURY's ability to create stunning visualisations of complex physical phenomena. For example, FURY can be used to create detailed visualisations of fluid dynamics, such as the flow of water or gas. It can also be used to visualise the motion of particles in a simulation.
View MoreFURY can be used to create detailed visualisations of the structure of molecules, including their bonding and interactions. It can also be used to visualise the motion of atoms and molecules in a simulation, such as the motion of atoms and molecules in a material.
View MoreFURY can be used to create detailed visualisations of the structure of galaxies, including the distribution of stars, gas, and dust. It can also be used to visualise the motion of celestial bodies in a simulation, such as the motion of stars and planets in a solar system.
View MoreFURY can easily create 3D visualizations of aerodynamic simulations, which can help engineers understand the performance of an aircraft in different flight conditions. This can be done by importing the simulation data into FURY and then using the library's built-in functions to create 3D models of the aircraft, along with visualizations of the airflow patterns and other important parameters.
View MoreWith FURY, researchers can create detailed models of cells and organelles, such as the mitochondria, ribosomes, and endoplasmic reticulum. This allows for a more in-depth understanding of the structure and function of these important cellular components.
View MoreOne of the key features of FURY is its ability to handle large amounts of data. This makes it perfect for data scientists who need to work with large datasets, as well as for visualizers who need to create interactive and engaging visualizations.
View MoreOne of the key features of FURY is its ability to handle large and complex networks. It can handle millions of nodes and edges, and can display the network in a variety of different ways, including node-link diagrams, matrix plots, and force-directed layouts. This allows users to explore their networks in a variety of different ways, and to find the best way to visualize their data.
View MoreFURY allows users to easily visualise complex mathematical data in a way that is easy to understand. For example, a mathematician studying a complex equation could use FURY to create a 3D plot of the equation's solutions, which would make it much easier to understand the behaviour of the equation.
View MoreSome Molecular Dynamics applications using FURY.
Built with FURY: Neuroscience application examples


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{{totalOperations}} operations are shown in the table below.
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Each entry is shown as baseline(difference), while difference is converted to percentage w.r.t baseline if applicable.
Each numeric column can be sorted against the baseline.


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AWS::OpsWorks::Instance
Creates an Amazon Elastic Compute Cloud (Amazon EC2) instance for an AWS OpsWorks stack. Instances for AWS OpsWorks stacks handle the work of serving applications and balancing traffic, for example.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "Type": "AWS::OpsWorks::Instance", "Properties": { "AgentVersion" :
String
, "AmiId" :String
, "Architecture" :String
, "AutoScalingType" :String
, "AvailabilityZone" :String
, "BlockDeviceMappings" : [BlockDeviceMapping, ...
], "EbsOptimized" :Boolean
, "ElasticIps" : [String, ...
], "Hostname" :String
, "InstallUpdatesOnBoot" :Boolean
, "InstanceType" :String
, "LayerIds" : [String, ...
], "Os" :String
, "RootDeviceType" :String
, "SshKeyName" :String
, "StackId" :String
, "SubnetId" :String
, "Tenancy" :String
, "TimeBasedAutoScaling" :TimeBasedAutoScaling
, "VirtualizationType" :String
, "Volumes" : [String, ...
] } }
YAML
Type: "AWS::OpsWorks::Instance" Properties: AgentVersion:
String
AmiId:String
Architecture:String
AutoScalingType:String
AvailabilityZone:String
BlockDeviceMappings: -BlockDeviceMapping
EbsOptimized:Boolean
ElasticIps: -String
Hostname:String
InstallUpdatesOnBoot:Boolean
InstanceType:String
LayerIds: -String
Os:String
RootDeviceType:String
SshKeyName:String
StackId:String
SubnetId:String
Tenancy:String
TimeBasedAutoScaling:TimeBasedAutoScaling
VirtualizationType:String
Volumes: -String
Properties
AgentVersion
-
The version of the AWS OpsWorks agent that AWS OpsWorks installs on each instance. AWS OpsWorks sends commands to the agent to performs tasks on your instances, such as starting Chef runs. For valid values, see the
AgentVersion
parameter for the CreateInstance action in the AWS OpsWorks Stacks API Reference.Required: No
Type: String
Update requires: No interruption
AmiId
-
The ID of the custom Amazon Machine Image (AMI) to be used to create the instance. For more information about custom AMIs, see Using Custom AMIs in the AWS OpsWorks User Guide.
Note
If you specify this property, you must set the
Os
property toCustom
.Required: No
Type: String
Update requires: Updates are not supported.
Architecture
-
The instance architecture.
Required: No
Type: String
Update requires: Some interruptions
AutoScalingType
-
For scaling instances, the type of scaling. If you specify load-based scaling, do not specify a time-based scaling configuration. For valid values, see CreateInstance in the AWS OpsWorks Stacks API Reference.
Required: No
Type: String
Update requires: Replacement
AvailabilityZone
-
The instance Availability Zone.
Required: No
Type: String
Update requires: Replacement
BlockDeviceMappings
-
A list of block devices that are mapped to the AWS OpsWorks instance. For more information, see the
BlockDeviceMappings
parameter for the CreateInstance action in the AWS OpsWorks Stacks API Reference.Required: No
Type: List of AWS OpsWorks Instance BlockDeviceMapping
Update requires: Replacement
EbsOptimized
-
Whether the instance is optimized for Amazon Elastic Block Store (Amazon EBS) I/O. If you specify an Amazon EBS-optimized instance type, AWS OpsWorks enables EBS optimization by default. For more information, see Amazon EBS–Optimized Instances in the Amazon EC2 User Guide for Linux Instances.
Required: No
Type: Boolean
Update requires: Replacement
ElasticIps
-
A list of Elastic IP addresses to associate with the instance.
Required: No
Type: List of String values
Update requires: No interruption
Hostname
-
The name of the instance host.
Required: No
Type: String
Update requires: No interruption
InstallUpdatesOnBoot
-
Whether to install operating system and package updates when the instance boots.
Required: No
Type: Boolean
Update requires: Some interruptions
InstanceType
-
The instance type, which must be supported by AWS OpsWorks. For more information, see CreateInstance in the AWS OpsWorks Stacks API Reference.
If you specify an Amazon EBS-optimized instance type, AWS OpsWorks enables EBS optimization by default. For more information about Amazon EBS-optimized instance types, see Amazon EBS–Optimized Instances in the Amazon EC2 User Guide for Linux Instances.
Required: Yes
Type: String
Update requires: Some interruptions
LayerIds
-
The IDs of the AWS OpsWorks layers to associate with this instance.
Required: Yes
Type: List of String values
Update requires: Some interruptions
Os
-
The instance operating system. For more information, see CreateInstance in the AWS OpsWorks Stacks API Reference.
Required: No
Type: String
Update requires: Replacement
RootDeviceType
-
The root device type of the instance.
Required: No
Type: String
Update requires: Replacement
SshKeyName
-
The SSH key name of the instance.
Required: No
Type: String
Update requires: Some interruptions
StackId
-
The ID of the AWS OpsWorks stack that this instance will be associated with.
Required: Yes
Type: String
Update requires: Replacement
SubnetId
-
The ID of the instance's subnet. If the stack is running in a VPC, you can use this parameter to override the stack's default subnet ID value and direct AWS OpsWorks to launch the instance in a different subnet.
Required: No
Type: String
Update requires: Replacement
Tenancy
-
The tenancy of the instance. For more information, see the
Tenancy
parameter for the CreateInstance action in the AWS OpsWorks Stacks API Reference.Required: No
Type: String
Update requires: Replacement
TimeBasedAutoScaling
-
The time-based scaling configuration for the instance.
Required: No
Type: AWS OpsWorks TimeBasedAutoScaling Type
Update requires: Replacement
VirtualizationType
-
The instance's virtualization type,
paravirtual
orhvm
.Required: No
Type: String
Update requires: Replacement
Volumes
-
A list of AWS OpsWorks volume IDs to associate with the instance. For more information, see AWS::OpsWorks::Volume.
Required: No
Type: List of String values
Update requires: No interruption
Return Values
Ref
When the logical ID of this resource is provided to the Ref
intrinsic
function, Ref
returns the resource name. For example:
{ "Ref": "
myInstance1
" }
For the AWS OpsWorks instance myInstance1
, Ref
returns the
AWS OpsWorks instance ID.
For more information about using the Ref
function, see Ref.
Fn::GetAtt
Fn::GetAtt
returns a value for a specified attribute of this type.
The following are the available attributes and sample return values.
-
AvailabilityZone
The Availability Zone of the AWS OpsWorks instance, such as
us-east-2a
. -
PrivateDnsName
The private DNS name of the AWS OpsWorks instance.
-
PrivateIp
The private IP address of the AWS OpsWorks instance, such as
<IP_ADDRESS>
. -
PublicDnsName
The public DNS name of the AWS OpsWorks instance.
-
PublicIp
The public IP address of the AWS OpsWorks instance, such as
<IP_ADDRESS>
.Note
Use this attribute only when the AWS OpsWorks instance is in an AWS OpsWorks layer that auto-assigns public IP addresses.
For more information about using Fn::GetAtt
, see Fn::GetAtt.
Examples
Create Basic AWS OpsWorks Instances
The following example creates two AWS OpsWorks instances that are associated with
the
myStack
AWS OpsWorks stack and the myLayer
AWS OpsWorks layer:
JSON
"myInstance1" : { "Type" : "AWS::OpsWorks::Instance", "Properties" : { "StackId" : {"Ref":"myStack"}, "LayerIds" : [{"Ref":"myLayer"}], "InstanceType" : "m1.small" } }, "myInstance2" : { "Type" : "AWS::OpsWorks::Instance", "Properties" : { "StackId" : {"Ref":"myStack"}, "LayerIds" : [{"Ref":"myLayer"}], "InstanceType" : "m1.small" } }
YAML
myInstance1: Type: "AWS::OpsWorks::Instance" Properties: StackId: Ref: "myStack" LayerIds: - Ref: "myLayer" InstanceType: "m1.small" myInstance2: Type: "AWS::OpsWorks::Instance" Properties: StackId: Ref: "myStack" LayerIds: - Ref: "myLayer" InstanceType: "m1.small"
Define a Time-based Auto Scaling Instance
In the following example, the DBInstance
instance is online for four
hours from UTC 1200-1600 on Friday, Saturday, and Sunday. The instance is offline
for
all other times and days.
JSON
"DBInstance" : { "Type" : "AWS::OpsWorks::Instance", "Properties" : { "AutoScalingType" : "timer", "StackId" : {"Ref":"Stack"}, "LayerIds" : [{"Ref":"DBLayer"}], "InstanceType" : "m1.small", "TimeBasedAutoScaling" : { "Friday" : { "12" : "on", "13" : "on", "14" : "on", "15" : "on" }, "Saturday" : { "12" : "on", "13" : "on", "14" : "on", "15" : "on" }, "Sunday" : { "12" : "on", "13" : "on", "14" : "on", "15" : "on" } } } }
YAML
DBInstance: Type: "AWS::OpsWorks::Instance" Properties: AutoScalingType: "timer" StackId: Ref: "Stack" LayerIds: - Ref: "DBLayer" InstanceType: "m1.small" TimeBasedAutoScaling: Friday: 12: "on" 13: "on" 14: "on" 15: "on" Saturday: 12: "on" 13: "on" 14: "on" 15: "on" Sunday: 12: "on" 13: "on" 14: "on" 15: "on"


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Programming Basics: Background and Repetition
| About
Contents
Index
Symbols
|
A
|
B
|
C
|
D
|
E
|
F
|
G
|
H
|
I
|
J
|
K
|
L
|
M
|
N
|
O
|
P
|
Q
|
R
|
S
|
T
|
U
|
V
|
W
|
Z
Symbols
2-3 tree
80/20 rule
A
abstract data type
accept
accepting state
activation record
acyclic graph
address
adjacency list
adjacency matrix
adjacent
ADT
adversary
adversary argument
aggregate type
algorithm
algorithm analysis
alias
all-pairs shortest paths problem
allocated
allocation
alphabet
alphabet trie
amortized analysis
amortized cost
ancestor
antisymmetric
approximation algorithm
arm
array
array-based list
array-based queue
array-based stack
ASCII character coding
assembly code
asymptotic algorithm analysis
asymptotic analysis
attribute
automata
automatic variable
average case
average seek time
AVL Tree
B
B$^*$-tree
B$^+$-tree
B-tree
backing storage
backtracking
bad reference
bag
balanced tree
base
base case
base class
base type
basic operation
best case
best fit
BFS
big-Oh notation
binary insert sort
binary search
binary search tree
binary search tree property
binary tree
binary trie
binning
Binsort
bintree
bit vector
bitmap
block
Boolean expression
Boolean variable
boom
bounding box
branch-and-bounds algorithm
breadth-first search
break-even point
BST
bubble sort
bucket
bucket hashing
bucket sort
buddy method
buffer
buffer passing
buffer pool
buffering
C
caching
call stack
Cartesian product
ceiling
child
circular first fit
circular list
class
class hierarchy
clause
client
clique
closed
closed hash system
closed-form solution
cluster
CNF
code generation
code optimization
cohesion
Collatz sequence
collision
collision resolution
collision resolution policy
comparable
comparator
comparison
compile-time polymorphism
compiler
complete binary tree
complete graph
complex number
Composite design pattern
composite type
composition
computability
computation
computational complexity theory
configuration
Conjunctive Normal Form
connected component
connected graph
constant running time
constructive induction
container
container class
context-free grammar
context-free language
context-sensitive grammar
cost
cost model
countable
countably infinite
CPU
current position
cycle
cylinder
cylinder index
cylinder overflow
D
DAG
data field
data item
data member
data structure
data type
deallocated
deallocation
decision problem
decision tree
deep copy
degree
delegation mental model for recursion
dense graph
depth
depth-first search
depth-first search tree
dequeue
dereference
derivation
descendant
deserialization
design pattern
deterministic
deterministic algorithm
Deterministic Finite Acceptor
Deterministic Finite Automata
DFA
DFS
DFT
diagonalization argument
dictionary
dictionary search
digraph
Dijkstra's algorithm
diminishing increment sort
direct access
direct proof
directed acyclic graph
directed edge
directed graph
dirty bit
Discrete Fourier Transform
discriminator
disjoint
disjoint sets
disk access
disk controller
disk drive
disk I/O
disk-based space/time tradeoff
distance
divide and conquer
divide-and-conquer recurrences
divide-and-guess
domain
double buffering
double hashing
double rotation
doubly linked list
DSA
dynamic
dynamic allocation
dynamic array
dynamic memory allocation
dynamic programming
E
edge
edit distance
efficient
element
empirical comparison
empty
encapsulation
enqueue
entry-sequenced file
enumeration
equidistribution property
equivalence class
equivalence relation
estimation
,
[1]
evaluation
exact-match query
exceptions
exchange
exchange sort
expanding the recurrence
exponential growth rate
expression tree
extent
external fragmentation
external sort
F
factorial
failure policy
family of languages
FIFO
file allocation table
file manager
file processing
file structure
final state
FIND
Finite Automata
Finite State Acceptor
Finite State Automata
Finite State Machine
first fit
fixed-length coding
floor
Floyd's algorithm
flush
,
[1]
flyweight
folding method
Ford and Johnson sort
forest
free block
free block list
free store
free tree
freelist
frequency count
FSA
FSM
full binary tree theorem
full tree
function
G
garbage
garbage collection
general tree
grammar
graph
greedy algorithm
growth rate
guess-and-test
guided traversal
H
halt state
halted configuration
halting problem
handle
hanging configuration
hard algorithm
hard problem
harmonic series
hash function
hash system
hash table
hashing
head
header node
heap
heapsort
height
height balanced
heuristic
heuristic algorithm
home position
home slot
homogeneity
Huffman codes
Huffman coding tree
Huffman tree
I
I/O head
image-space decomposition
in degree
incident
index file
indexing
induction hypothesis
induction step
induction variable
information theoretic lower bound
inherit
initial state
inode
inorder traversal
Insertion Sort
instance variable
integer function
inter-sector gap
interface
intermediate code
intermediate code generation
internal fragmentation
internal node
internal sort
interpolation
interpolation search
interpreter
inversion
inverted file
inverted list
irreflexive
ISAM
iterator
J
job
jump search
K
K-ary tree
k-path
kd tree
key
key sort
key space
key-space decomposition
key-value pair
knapsack problem
Kruskal's algorithm
L
labeled graph
language
Las Vegas algorithms
leaf node
least frequently used
least recently used
left recursive
length
level
lexical analysis
lexical scoping
LFU
lifetime
LIFO
linear congruential method
linear growth rate
linear index
linear order
linear probing
linear probing by steps
linear search
link node
linked list
linked stack
list
literal
load factor
local storage
local variable
locality of reference
logarithm
logical file
logical form
lookup table
lower bound
lower bounds proof
LRU
M
main memory
map
mapping
mark array
mark/sweep algorithm
master theorem
matching
matching problem
max heap
maximal match
maximum lower bound
maximum match
MCST
measure of cost
member
member function
memory allocation
memory deallocation
memory hierarchy
memory leak
memory manager
memory pool
memory request
merge insert sort
Mergesort
message
message passing
metaphor
method
mid-square method
min heap
minimal-cost spanning tree
minimum external path weight
mod
model
modulus
Monte Carlo algorithms
move-to-front
MST
multi-dimensional search key
multi-dimensional search structure
multilist
N
natural numbers
necessary fallacy
neighbor
node
non-deterministic
non-deterministic algorithm
non-deterministic choice
non-deterministic polynomial time algorithm
non-strict partial order
non-terminal
NP
NP-Complete
NP-Completeness proof
NP-hard
nth roots of unity
O
object
object-oriented programming paradigm
object-space decomposition
octree
Omega notation
one-way list
open addressing
open hash system
operating system
optimal static ordering
optimization problem
out degree
overflow
overflow bucket
overhead
P
page
parameter
parent
parent pointer representation
parity
parity bit
parse tree
parser
partial order
partially ordered set
partition
pass by reference
pass by value
path
path compression
PDA
peripheral storage
permutation
persistent
physical file
physical form
Pigeonhole Principle
pivot
platter
point quadtree
point-region quadtree
pointee
pointer
pointer-based implementation for binary tree nodes
polymorphism
pop
poset
position
postorder traversal
potential
powerset
PR quadtree
prefix property
preorder traversal
Prim's algorithm
primary clustering
primary index
primary key
primary key index
primary storage
primitive data type
primitive element
primitive nth root of unity
priority
priority queue
probabilistic algorithm
probabilistic data structure
probe function
probe sequence
problem
problem instance
problem lower bound
problem upper bound
procedural
procedural programming paradigm
production
production rule
program
promotion
proof
,
[1]
by contradiction
by induction
direct
proof by contradiction
proof by induction
proving the contrapositive
pseudo polynomial
pseudo random
pseudo-random probing
push
pushdown automata
Q
quadratic growth rate
quadratic probing
quadtree
queue
Quicksort
R
radix
radix sort
RAM
random access
random access memory
random permutation
randomized algorithm
range
range query
read/write head
rebalancing operation
record
recurrence relation
recurrence with full history
recursion
recursive call
recursive data structure
recursive function
recursively enumerable
Red-Black Tree
reduction
reference
reference count algorithm
reference parameter
reflexive
Region Quadtree
regular expression
regular grammar
regular language
relation
replacement selection
reserved block
resource constraints
root
rotation
rotational delay
rotational latency
run
run file
run-time polymorphism
runtime environment
runtime stack
S
scanner
scope
search key
search lower bound
search problem
search tree
search trie
searching
secondary clustering
secondary index
secondary key
secondary key index
secondary storage
sector
sector header
seed
seek
selection sort
self-organizing list
self-organizing list heuristic
separate chaining
sequence
sequential access
sequential fit
sequential search
sequential tree representation
serialization
set
set former
set product
shallow copy
Shellsort
shifting method
shortest path
sibling
signature
signature file
simple cycle
simple path
simple type
simulating recursion
single rotation
single-source shortest paths problem
singly linked list
skip list
slot
snowplow argument
software engineering
software reuse
solution space
solution tree
sorted list
sorting lower bound
sorting problem
space/time tradeoff
sparse graph
sparse matrix
spatial
spatial application
spatial attribute
spatial data
spatial data structure
spindle
Splay Tree
splaying
stable
stack
stack frame
stack variable
stale pointer
start state
start symbol
state
State Machine
static
static scoping
Strassen's algorithm
strategy
stream
strict partial order
strong induction
subclass
subgraph
subset
subtract-and-guess
subtree
successful search
summation
superset
symbol table
symmetric
symmetric matrix
syntax analysis
T
tail
terminal
Theta notation
token
tombstone
topological sort
total order
total path length
Towers of Hanoi problem
track
track-to-track seek time
trailer node
transducer
transitive
transpose
trap state
traversal
tree
tree traversal
trie
truth table
tuple
Turing machine
Turing-acceptable
Turing-computable function
Turing-decidable
two-coloring
type
U
unary notation
uncountable
uncountably infinite
underflow
undirected edge
undirected graph
uninitialized
UNION
UNION/FIND
unit production
unsolveable problem
unsorted list
unsuccessful search
unvisited
upper bound
V
value parameter
variable-length coding
vector
vertex
virtual memory
visit
visited
visitor
volatile
W
weight
weighted graph
weighted path length
weighted union rule
working memory
worst case
worst fit
Z
zigzig
Zipf distribution
zone
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Example 1¶
Usage with D9 (the current default viewer)¶
Start up a DS9 window (by default), a new DS9 window will be opened
viewer=imexam.connect()
If you already have a window running, you can ask for a list of windows
imexam.list_active_ds9()
DS9 ds9 gs 82a7e75f:57222 sosey
You can attach to a current DS9 window be specifying its unique name
viewer=imexam.connect('ds9')
If you haven’t given your windows unique names, then you must use the ip:port address
viewer=imexam.connect('82a7e75f:57222')
Usage with Ginga viewer¶
Start up a ginga window using the HTML5 backend
viewer=imexam.connect(viewer='ginga')
All commands after the viewer is opened are the same¶
Load a fits image into the window
viewer.load_fits('test.fits')
Scale to default using zscale
viewer.scale()
Change to heat map colorscheme
viewer.cmap(color='heat')
Make some marks on the image and save the regions
viewer.save_regions('test.reg')
Delete all the regions you made, then load from file
viewer.load_regions('test.reg')
Plot stuff at cursor location, in a while loop. Type a key when the mouse is over your desired location and continue plotting with the available options
viewer.imexam()
'a': 'aperture sum, with radius region_size, optional sky subtraction',
'j': '1D line fit ',
'k': '1D column fit ',
'm': 'median square region stats, in region_size square',
'n': 'move to the next frame',
'p': 'move to the previous frame',
'x': 'return x,y coords of pixel',
'y': 'return x,y coords of pixel',
'l': 'return line plot',
'c': 'return column plot',
'r': 'return curve of growth plot',
'h': 'return a histogram in the region around the cursor'
'e': 'return a contour plot in a region around the cursor'
's': 'save current figure to plotname'
'b': 'return the gauss fit center of the object'
'w': 'display a surface plot around the cursor location'
Quit out and delete windows and references
viewer.close()