THE CURSE OF THE JADE SCORPION A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): ** 1/2
THE CURSE OF THE JADE SCORPION, written and directed by Woody Allen, suffers from the Woody Allen curse. It's a malaise of mediocrity that has struck almost all of his recent films. They're all merely okay -- cute at times with some nice laughs but as forgettable as sitcom reruns.
The setup this time features Woody as crackerjack detective CW Briggs during Woody's favorite period, the 1940s. This allows him to get a lot of his favorite music, jazz, into the score. The weird and wacky theme music for the film manages to be the movie's most endearing and original accomplishment.
Briggs has running verbal battles with the office's new efficiency expert, Betty Ann "Fitz" Fitzgerald (Helen Hunt). Although they appear to despise each other, they subconsciously are head-over-heels in love. Their stream of insults and CW's sexual innuendos form the best part of the dialog. When she finds a deck of playing cards decorated with naked women in his apartment, it confirms her assessment of him as a slimeball. As an explanation, he tells her, "I used to date the six of spades." Many of the jokes, however, are retreads such as the one about the blind beggar. Guess what? He can see.
The plot concerns a hypnotist, Voltan (David Ogden Stiers), who puts both CW and Fitz under a spell. This allows Voltan later to turn them into zombies.
A subtheme concerns the affair that Fitz is having with their boss, Chris Magruder (Dan Aykroyd). This dissolves into the old routine of whether the married man will leave his wife or not. Elizabeth Berkley (SHOWGIRLS) and Charlize Theron are given a couple of throwaway parts -- Berkley as a woman who looks easy, but isn't, and Theron as a blonde bombshell, who is happiest in bed.
As the story evolves, it turns more and more into a love story. If you like to see old, frail, balding guys taking home the smart, good-looking women, you'll like where the story goes. I must admit that I still find Woody charming, but I suspect it is more in reverence for his great films like ANNIE HALL and HANNAH AND HER SISTERS than for his current pictures. The best that can be said of his latest film is that it's kind of entertaining.
THE CURSE OF THE JADE SCORPION runs too long at 1:44. It is rated PG-13 for some sexual content and would be acceptable for kids around 9 and up.
My son Jeffrey, age 12, gave it ** 1/2. He said that it was funny, but that the film "wasn't the greatest and wasn't the worst." It was okay.
The film opens nationwide in the United States on Friday, August 24, 2001. In the Silicon Valley, it will be showing at the Camera Cinemas.
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This was only my second experience with methylone, but it was so personally remarkable that I felt it worth documenting.
My first experience had been with a single 200mg dose, oral, a week prior to this one, and had been thoroughly pleasant, but I felt it could have been improved.
On the night of this experience a friend and I were going to a club night that we both enjoy a great deal anyway, and it seemed like a good time to split the last of the caps I had. We had expected another friend, but in her absence we were left with an additional smaller cap (150mg), and as there was nothing else to use it for in the near future I let my curiosity get the better of me and decided to take it too, mainly to see what effect it had on duration. What follows is a report of the effects, to the best of my recollection, covering both the initial effects at the 200mg dose and the additional character of the experience following the boost.
Setting is initially the dubstep room of a large club night, somewhat busy. I buy myself a bottle of water and nip to the bathroom.
T+0:00 Ė Take the 200mg cap. I am slightly tipsy, but barely noticeably, from pre club drinks. No food in about 6 hours. I know what to expect from the initial comeup, so Iím currently quite excited, as well as being stoked about the quality of the night anyway.
T+0:30 Ė First feelings start to come on, quite identical to those I had with my first experience. The first effect is a marked release of bodily fatigue- movement becomes easier, tiredness seems to dissipate. Thereís no feeling of urgency to the stimulation, itís just comfortable.
T+0:45 Ė The stimulation persists, but now the sense of wellbeing is coming on too. Significant eye dilation. This is my favourite part of the 200mg experience- itís a feeling of rightness and calm. I feel unshakable, but not overly self assured or arrogant. Thereís a tingling sensation that moves up the spine in waves and seems to roll off of the top of the head, and my scalp has become extremely sensitive to touch. Iím also more talkative, more alert, and am losing my awareness of personal space and boundaries, as expected, but Iím very cogent still, and am not rabbiting.
T+1:15 Ė The physical experience has held steady for about 15 minutes at this point. Weíve moved to the psytrance room, and the music is intense and amazing. At this point the mental experience seems to become slightly more introspective than a typical club drug experience- thoughts turn inward while dancing, and experiences are examined and accepted. This is therapeutic in a way, but I decide to boost soon to get into more of a party mood, in theory.
T+1:30 Ė I slip into the bathroom to drop the second 150mg cap. I feel slightly apprehensive at this point, as I realise 350mg is quite a large dose, even considering my size, so my friend has been asked to keep an eye on me. Down it goes.
T+2:00 Ė Feels like the first come up is coming on again. The introspection has faded, and I feel a little more stimulated. Where the physical sensation, the tingling, was in waves on 200mg, it is now quite constant but mild. Otherwise no further effect yet.
T+2:15 Ė This is when the real difference occurs. I am dancing quite happily when a sudden warm wave of sensation rips up my body and through my scalp, leaving me feeling like Iím glowing with energy. My vision blurs slightly and I have to stop dancing, physically shocked by the intensity and suddenness of this effect. Within a few seconds the intensity fades enough for me to move over to where my friend is dancing, just in time to be hit by the sensation again- this time it feels like I physically canít move for a moment. Expletives are uttered, and I have to wait about a minute for these pulses to tick over before I can do anything. This is a very, very different experience to the lower dose.
T+2:30 Ė There is a more typically stimulant like effect now, I feel thereís a sense of urgency to my thought processes and actions. Iím dancing harder, and feel physically amazing. I certainly feel more loved up at this level too- I am approaching people randomly and making connections with them, and itís beautiful. I am still occasionally caught off guard by a wave of physical sensation, and literally am left gasping with my hands over my mouth. Thereís some severe drymouth kicking in that nothing seems to alleviate, which I never had on 200mg.
T+3:00 Ė Effects persist much as before, but Iím gurning like a champion now too. Do manage to bite my tongue (which is more irritating now than it was at the time). Music is divine, though, and the lightshow looks amazing. When I occasionally break from the floor I do end up rabbiting, but no-one seems to mind.
T+3:30 Ė Physical effects start to roll off. Iím still quite tactile, but Iím starting to feel fatigued from the dancing. Thereís still mental stimulation- Iím very awake. Some introspection begins again now. Dry mouth and gurning persist.
T+4:00 - Notice quite suddenly that the mood elevation has completely dropped, leaving me feeling quite anxious, and meaning the duration has been the same as for the 200mg dose. The comedown is quite unpleasant by comparison, and I head for the smoking area to sit down and calm down.  I sit around for an hour and a half chatting to friends and strangers while the worst of the mood change dissipates, before heading back for a nice cup of tea and a chill out in the sun.
After Effects:
There were some after effects that persisted at this larger dose that were completely absent with 200mg. Iím not sure how much of itís actually the drug and not just me, so Iíll just list what Iíve experienced to be thorough:
- Fitful sleep in spite of feelings of tiredness. No surprise really, but I actually found that on 200mg after coming down sleep was easy and very good, so it is a difference.
- Lack of appetite even 24 hours after come down, but incredible thirst even beyond that explainable by normal clubbing dehydration. Iíd made sure to keep well watered all night, and still I was drinking taps dry today.
- A lack of motivation / energy for a day or so after the experience. Again no real surprise, but this wasnít experienced on the 200mg dose.
- Some nasal congestion, which may or may not have anything to do with the chemical.
Overall Iím glad I tried the bigger dose, but I couldnít manage this sort of experience with the chemical every time. I doubt that even with my quite slow metabolism I could extend duration of the experience, but the intensification was something I will pursue again, albeit sparingly.
I am very, very pleased to have found a chemical so enjoyable that is easily and legally available, and would certainly recommend it. My only caveat would be that dosage seems very variable between people, and I definitely wouldnít advise going straight to 350mg.
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.


| [1] |
K. Kawasaki, A. Mochizuki, M. Matsushita, T. Umeda, and N. Shigesada.
Modeling spatio-temporal patterns generated bybacillus subtilis.
Journal of Theoretical Biology, Vol. 188, No. 2, pp. 177-185,
1997. [ bib | DOI | http ] Colonies of bacteria,Bacillus subtilis, that grow on the surface of thin agar plates show various morphological patterns in response to environmental conditions, such as the nutrient concentration, the solidity of an agar medium and temperature. For instance, the colony pattern shows a dense-branching morphology with a smooth circular envelope (DBM-like) in a nutrient-poor semi-solid agar medium, and it turns to a simple disk-like colony as both the nutrient concentration and the agar's softness increase. These patterns have been shown to involve cell movement inside colonies. In a DBM-like colony, individual cells actively move, particularly in the expanding periphery of the colony, while they become immotile at the inner region of the colony where nutrient is very low. In a disk-like colony, cells are highly active in the whole region of the colony. Based on such experimental observations, we develop a diffusion-reaction model, in which density dependent cell movements are incorporated by the level of nutrient concentration available for the cell. Numerical simulations of the model under different environmental conditions closely reproduce various colony patterns ranging from DBM-like pattern to the homogeneous disk-like one in a unifying manner. The analysis also predicts the growth velocity of a colony as a function of the nutrient concentration.
|
| [2] |
Ryojiro Honda, Jun ichi Wakita, and Makoto Katori.
Self-elongation with sequential folding of a filament of bacterial
cells.
Journal of the Physical Society of Japan, Vol. 84, No. 11, p.
114002, 2015. [ bib | DOI | arXiv | http ] |
| [3] |
Zoltán Csahók and Tamás Vicsek.
Lattice-gas model for collective biological motion.
Phys. Rev. E, Vol. 52, pp. 5297-5303, November 1995. [ bib | DOI | http ] |


Hello my Name is ZI Zoraez
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| Event | Description | Location | Date |
|---|---|---|---|
| {Event} | {Description} | {Location} | {Date} |


| # | Sta | Rlx | Name | Row Text |
| 0 | | 0 | autoRow0 | the "Earth" "atmosphere" is made of 0 to 4 percent <water> <vapor> "by volume" (7051-de21-70b0-8c49) |
| 1 | | 0 | autoRow1 | <water> <vapor> is a kind of "greenhouse gas" (dc5f-51cc-0c46-18df) |
| 2 | ST | 0 | autoRow2 | greenhouse gases absorbing infrared radation increases the temperature of the atmosphere (244e-0024-b731-9ef5) |
| 3 | ST | 0 | autoRow3 | <water> <vapor> is "largest amount of" "greenhouse gas" "in atmosphere" (9d62-f0d6-f0a5-6846) |
| 4 | ST | 0 | autoRow4 | if greenhouse gases are absent , then the atmosphere will be cooler (3489-a424-c6b0-568a) |
| 5 | ST | 0 | autoRow5 | greenhouse gases are a part of the atmosphere (7d84-2263-9ae5-822c) |
| 6 | ST | 0 | autoRow6 | the greenhouse effect is when carbon in the air heats a planet 's atmosphere (b8bd-0f06-439a-5721) |
| 7 | ST | 0 | autoRow7 | carbon dioxide can trap solar energy (7d5e-daf7-f39b-de15) |
| 8 | ST | 0 | autoRow8 | as the amount of carbon dioxide in the atmosphere increases , the temperature of the planet ; the atmosphere will increase (7ab2-0e60-962b-f6ab) |


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welcome to my website
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The is flexibility in the handling of blank space in @x:Act scripts. This simplifies the content of xdom parse trees and provides the flexibility needed for pretty printing using the Unparse function. There are five kinds of spaces.
In the xdom tree, spaces are recorded in two places. First every node has a spaceBefore and spaceAfter value. Second, xdom:element nodes also have a spaceBeforeBody and spaceAfterBody value.
When parsing spaces are recorded in the xdom nodes both before and after the space. For example,
Here a.spaceAfter and b.spaceAfter are both set to "line". The Unparse function combines adjacent spaces and uses their "maximum". For example combining "space" and "lines" yields "lines". Combining "line" and "line" yields "line". Combining "lines" and "erase" yields "erase". When rendering, adjacent spaces are also combined using some "magic" interpreter logic that tracks space codes at the beginning and end of strings to be rendered.
The is flexibility in the generation of space characters in the result of rendered commands. If precise control of spacing is needed, an alternative is the use @x:Act @x:tag command that renders an specified xdom tree.


A false history trying to be remembered
In 2015 Dylan Roof entered a primarily African American church with the intent of killing the people inside. He shot 77 times killing 9 people.
After the shooting the media reported on his social media account. There were many images of him posing with confederate flags and items of similar imagery. Ultimately this sparked discussion about the more than
1,500 monuments and public spaces named and dedicated to Confederates.
Some try to justify their existence by saying that the statues are history or represent Southern Pride. There are groups out there such as the Daughters of the
Confederacy and the Sons of the Confederacy that are dedicated to push a different narrative with claims that the secession of the South was due to
numerous other factors in order to downplay the slavery as a reason that the South left, fought and lost against the United States.
This project exists to reject this false narrative and to show why these monuments are not history. They exist to create a false history, a false narrative
about the events of the Civil War and even events in modern day.
This project is mostly looking at the Confederate flag and Confederate monuments and what history and symbolism is actually behind them.
The Confederates lost the war in 1865. As of writing this that is about 152 years ago. For 152 years people have been holding onto a symbol that stood for a group that left the United States because they wanted to own people. Most people are familiar with this particular flag.
This is one of the flags from the confederacy. There were several.
This particular one is the battle flag.
A battle flag that was mostly well known due to being used by Robert E. Lee, a Confederate general who distanced himself from it once the south lost.
So what were the other flags?
The Stars and Bars was the first flag on the Confederacy. It was flag adopted, in part, due to people on the committee not wanting to drift too far away from the United States flag. The flag of the country they seceded from. It was also later changed because it was too close to the United States’ flag.
The “Stainless Banner” was the Confederacy’s second attempt at creating a flag. This particular one had a fundamental flaw in it’s design. If no wind was blowing it looked like they were surrendering as it just looked like a white flag. An article by William Tappan Thompson wrote, "As a people we are fighting to maintain the Heaven-ordained supremacy of the white man over the inferior or colored race; a white flag would thus be emblematical of our cause."
This flag was made with the intent of stopping the complaints the second flag created. Adding the red stripe at the end was so it hopefully wouldn’t be mistaken for a flag of truce. This flag was adopted about two months before the end of the Civil War.
So if these were the actual flags of the Confederacy, then why is one of the Confederate battle flags seeing the most use?
Well that goes back to 1948. 69 years ago. 83 years after the Civil War ended. A political party known as the State’s Rights Democratic Party, or the Dixiecrats, began using it.
The party’s name contains a current piece of rhetoric on the secession of the South that it was state’s rights. But the Dixiecrats weren’t subtle with their racism.
This was a group that wanted to protect state’s rights. Specifically states rights to legislate racial segregation. The Dixiecrats were very much against racial integration and the removal of Jim Crow Laws.
The party didn’t last long. It made a run at winning the 1948 presidential election and lost. Had it not been for Executive order 9981, an order forcing the military to integrate,
this state’s rights group may not have really been noticed.
But that limelight was enough. One of the symbols they used for their campaign, the confederate battle flag was back to being used after a long period of being forgotten.
It became a symbol to rally around for those who shared the same ideology. The ideology that whites were superior.
The use of the flag as a symbol of those against basic rights for African Americans has shown up time and time again.
The Mississippi flag is currently the only flag in the United States that has the Confederate battle flag’s saltire. Mississippi used this flag since 1894, 29 years after the Civil War ended, but technically used it unofficially as it had been omitted from the state’s legal code until 1993. In 2000 Governor Ronnie Musgrove issued an executive order making it the state’s official flag. Another flag design was proposed, in the style of the “Stars and Bars” flag, but voters chose to keep the 1894 flag.
This flag was created two years after the Brown v. Board of Education court that ruled that schools needed to desegregate. This flag’s design was suggested by John Sammons Bell, an outspoken supporter of segregation. The flag’s redesign was suggested during the same time there were bills trying to be passed that would stop cities from desegregating public spaces.
These monuments represent a false history of our country. These monuments don’t record history.
The Robert E. Lee elementary school doesn’t tell a tale of the past. The nameless soldier monument doesn’t contain the past of the South. We have places that do that.
We have museums that collect information and artifacts from the war. They tell the unabridged history, regardless of how hard it is for some to hear. These kinds of things:
This map helps us visualize areas of growth, rather than the actual amount of growth. We can see several areas were most of the states experienced a rather large increase in monuments. The two biggest ones that stand out are at the beginning of the Jim Crow era and the Civil Rights era.
31 out of 50 states have at least one confederate monument. Many southern states have dozens of monuments. But there are also many states without any real connection to the confederates that have confederate monuments in them. 31 states have Confederate monuments in them as of the creation of this site. 62% of all States have confederate monuments and more are still being added. Virginia has 119 Confederate monuments. Were all of them created to preserve history? Are 119 of them needed in one state to preserve that history?
To say that the United States has a complicated history with race would be an extreme understatement. We have had continuing issues with racism that has extended much longer than it has right too.
 To get a good idea on these monuments and why they don’t fall into place with the ”it’s history” rhetoric it is a good idea to see when these monuments are being made and how many are being made.
We see time and time again throughout history attempts to set back progress for the African American community. Whenever African Americans began making new strides in getting basic human rights these monuments would suddenly increase.
The United States declared it’s independence 241 years ago. It’s been around 49 years since we began giving African Americans basic human rights. There are people alive today who lived through a time when these rights didn’t exist.
These monuments represent a false history of our country. These monuments don’t record history.
The Robert E. Lee elementary school doesn’t tell a tale of the past. The nameless soldier monument doesn’t contain the past of the South. We have places that do that.
We have museums that collect information and artifacts from the war. They tell the unabridged history, regardless of how hard it is for some to hear. These kinds of things:
They aren’t history. This is something easily fact checked and shown incorrect. This is not something that should be in public under the guise of history.
Because it isn’t history. It is erasing history. The defense of the confederate monuments and confederate battle flag just show a lack of actual historical knowledge.
We can see surges of monuments being created whenever African Americans began getting more rights.
The Confederate battle flag went unused for years until it was revived by a political party whose major speaking piece was that segregation was good.
The people creating these statues are trying to push the “Lost Cause” myth. The idea that this war was an honorable struggle for the Southern way of life.
In a very, very narrow way it is a bit true. A major reason states left the United States was due to slavery. But these groups want to minimize slavery.
They want to ignore it and instead memorialize those who fought against the United States because they wanted to own people.
There are hundreds of these monuments across the United States in states that have no business ever having one.
The barebones argument that they are historical no longer holds up once they enter arear that are not the south.
These participation trophies don't deserve to decorate the public spaces that everyone gets to use.
These monuments embody the myth of the Lost Cause. This narrative is dangerous for many reasons, but there is one reason above them all that makes it dangerous. They are blatant lies. They cannot be history because they tell lies. At best the statues have historical value on their own, not from what they are supposed to represent.
Above all it cannot be stressed enough. Destroying these monuments will not erase history. We are history. We have extensive documents about the war and the various causes. We have hundreds of books about the Civil War. One book alone tells more than every single Confederate monument that exists.
It is an actual guardian of information. These monuments are not.
If you’re worried about history being erased read a book about the war or visit a museum to become another person who this information lives on through.


Software EngineerWeb Developer
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Advise to my juniors and reflection on school and college life (ToDo)
What Have I learnt till now ?? Important Question :)
Thanks!
As a customary I would like to say to all
"Hello World"
Exactly the same day I was born :P


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I am your period reminder assistant, Swipe right to know about features
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Welcome to STARS TV PROGRAM GUIDE
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@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.agentsubscriptionfrontend.config.AppConfig @import uk.gov.hmrc.agentsubscriptionfrontend.views.html.components._ @this(mainTemplate:MainTemplate, p:p, a:a, h1:h1) @(continueUrl: String)(implicit request: Request[_], msgs: Messages, appConfig: AppConfig) @mainTemplate(title = msgs("alreadySubscribed.title")) { @h1("alreadySubscribed.title") @p("alreadySubscribed.p1")
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--- title: "BEES OF RED BUTTE GARDEN LECTURE" date: "2000-01-01 18:09:01 -06:00" categories: - lecture subtitle: "" event-notes: "" layout: "events" event-image: "bees-lecture.jpg" event-dates: "SEPTEMBER TBD @" event-location: "Red Butte Garden Classroom" button-type: "register" tickets-link: "https://55218.blackbaudhosting.com/55218/Lecture-Bees-of-Red-Butte-Garden" tickets-button: "" registration-info: "Limit: 40 People" instructor-info: "Dr. Joseph Wilson" member-cost: "Free" public-cost: "Garden Admission" published: false ---
REGISTRATION REQUIRED
How many bee species are in North America? 100? 200? How about 4000!
Red Butte Garden is home to about 132 different species of bees, from 34 different genre, and all six North American bee families!
Learn about the huge variety of native bees with Dr. Joseph Wilson, assistant professor of Entomology at Utah State University Tooele, and author of The Bees In Your Backyard: A Guide to North American Bees. (Princeton University Press): http://press.princeton.edu/titles/10593.html


Yellowblue's file structure is (hopefully) well organized. Don't be overwhelmed by the large number of files. This will help you customize and maintain your site easily and quickly.
Here is Yellowblue's file structure:
/
|-- 404 # Folder that contains the 404 page
| |-- index.html # The actual 404 page
|-- _data # Jekyll's _data folder
| |-- icons.yml # A YAML file that contains formatted information about the theme's icons
|-- _includes # Jekyll's _includes folder
| |-- patterns # The partials used in the Pattern Library page
| | |-- buttons # The partials for the buttons' patterns
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| | |-- icons # The partials for the icons' patterns
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|-- _posts # Your posts go here
|-- _sass # Sass files
| |-- base # Base Sass files
| | |-- _animations.scss # CSS animations (including animate.css)
| | |-- _buttons.scss # buttons
| | |-- _fluidbox.scss # CSS for fluidbox
| | |-- _forms.scss # forms
| | |-- _icomoon.scss # CSS for Icomoon icons
| | |-- _icons.scss # icons
| | |-- _layout.scss # everything that has to do with layout
| | |-- _normalize.scss # normalize.css
| | |-- _notifications.scss # notifications
| | |-- _prism.scss # CSS for prism.js
| | |-- _tables.scss # tables
| | |-- _typography.scss # everything that has to do with typography
| | |-- _vars.scss # the Sass variables
| |-- color_schemes # The files for Color Schemes presets
| | |-- _gray.scss # The Gray color scheme preset
| | |-- _green.scss # The Green color scheme preset
| | |-- _red.scss # The Red color scheme preset
| | |-- _white.scss # The White color scheme preset
| | |-- _yellowblue.scss # The Yellowblue (default) color scheme preset
| |-- site # Sass files for the theme itself
| | |-- _404.scss # styling for the 404 page
| | |-- _content.scss # everything that has to do with the content of the site
| | |-- _footer.scss # styling for the footer page
| | |-- _header.scss # styling for the header
| | |-- _nav.scss # styling for the navigation
| | |-- _pagination.scss # styling for the pagination
| | |-- _posts.scss # styling for the posts
| | |-- _sidebar.scss # styling for the sidebar
| |-- _custom.scss # Your custom sass/css code goes here
|-- _site # The folder that gets deployed
|-- archives # The folder that contains the archives page
| |-- index.html # The actual archives page
|-- category # The folder that contains the teasers for certain categories
|-- contact # The folder that contains the contact page
| |-- index.html # The actual contact page
|-- css # Jekyll's CSS folder
| |-- main.scss # The site's main scss file
|-- icons # The folder that contains the icons list page
| |-- index.html # The actual icons list page
|-- images # The site's images go here
|-- js # Jekyll's CSS folder
| |-- fluidbox.js # fluidbox.js library
| |-- jquery.js # jquery.js library
| |-- prism.js # prism.js library
| |-- scripts.js # custom javascript goes here
| |-- scrollreveal.js # scrollreveal.js library
|-- pattern-library # The folder that contains the Pattern Library page
| |-- index.html # The actual Pattern Library page
|-- tag # The folder that contains the teasers for certain tags
|-- thank-you # The folder that contains the thank you page after submitting the contant form
| |-- index.html # The actual thank you page
|-- _config.yml # Jekyll's settings
|-- favicon.ico # favicon.ico
|-- favicon.png # favicon.png
|-- feed.xml # posts feed
|-- index.html # the site's home page
|-- sitemap.xml # the sitemap


Class 1 Lab
The Questions you have been asked is below!
What is your name?
How old are you?
What is your hobby?
What do you do in your free time?


Rabbi Lau: "What Sabbath are they willing to respect?"
After the mass breaking of Sabbath in and around Tel Aviv, Chief Rabbi Lau voices his objection
by Arutz Sheva StaffChief Rabbi David Lau voiced, this morning (Sunday), his objection to the mass breaking of Sabbath in and around Tel Aviv, after the reform of public transport on the Jewish day of rest.
"They speak of compromise, compromise with whom? The Sabbath is being disregarded and the trend is only growing. Before others arrive with new demands, we need to ask, what kind of Sabbath are they willing to respect?" Rabbi Lau wondered to an interview to 'Kan Moreshet'.
In the interview, Lau explains there is mutual responsibility, and the haredi public need to analyse themselves to how this situation occurred.
For the first time in the history of the State of Israel, public transport was run on Sabbath, the buses ran in Tel Aviv, Giv'atayim, Ramat HaSharon and Kiryat Ono.
The Deputy Minister of Education, Meir Porush, commented that the current political stalemate is being utilized by municipalities to disregard the Sabbath.
"The Sabbath is a shield of the Jewish people for generations and not a political tool. It hurts to see municipal politicians utilizing the current political crises to gain popular support by destroying the Sabbath.


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Featured
Special title treatment
With supporting text below as a natural lead-in to additional content.
Go somewhere


Bindaas
Be worry free !
sytem build
1.0.0
Projects
API Browser
Server Administration
Access Control
Audit Logs
Bindaas is produced by
Department of Biomedical Informatics Emory University


{% extends 'layouts/base.html' %} {% block content %}
In order to use this app you have to set it up first. - Please look up your DSL speed from your Internet Plan. You will need it in the next step. Let's get started!


A button can be added to the page that will scroll to the top when clicked. Add the scroll-top
directive to the body
element.
Several attributes can be set to customize the behavior of the button:
If set to 'up' then the button will only appear when the user begins to scroll up and hide when they scroll down. The default behavior is to show the button when the page has been scrolled down.
This defines the number of pixels the button will be positioned from the bottom of the page.


NYPD: $140K in jewelry, bags stolen from mall magnate’s home
by The Associated PressNEW YORK (AP) — Police say a group of burglars stole $140,000 worth of luggage and jewelry from a shopping mall magnate’s New York City mansion.
The Bronx home belongs to Don Ghermezian, the CEO of the company that runs Mall of America in Minnesota and the new American Dream complex in New Jersey.
The New York Police Department says a group of unidentified people broke into the house, in the affluent Riverdale neighborhood, through a rear window on a balcony on Saturday evening.
Police released security camera footage showing four people were involved. The suspects fled the scene.
Authorities say Ghermezian was out of town at the time. He told The New York Daily News no one was injured during the burglary.
No arrests have been made.
The Associated Press


Built-in Windows® Kerberos Service Names
The following list is of all of the built-in service names available in Windows®. This is also the list which is implicitly present if a single SPN (host) exists for a given account.
alerter
appmgmt
browser
cifs
cisvc
clipsrv
dcom
dhcp
dmserver
dns
dnscache
eventlog
eventsystem
fax
http
ias
iisad
mcsvc
messenger
min
msiserver
netdde
netddedsm
netlogon
netman
nmagent
oakley
plugplay
policyagent
protectedstorage
rasman
remoteaccess
replicator
rpc
rpclocator
rpcss
rsvp
samss
scardsvr
scesrv
schedule
scm
seclogon
snmp
spooler
tapisrv
time
trksvr
trkwks
ups
w3svc
wins
www


How Color Affects Mood and Mental Health
Color theory is the idea that the colors a person is surrounded ...
Colour is simply light of varying wavelengths and, as such, is a form of energy that is used in Colour Therapy. This light energy affects all living cells (as we all know, without light all living things are affected). Used in the right way, these different frequencies of light, i.e. colours, can have a profound and healing effect on all creation, human or our pets. It is a well-known scientific fact that everything has a vibration, that is to say that all things have their own vibration/frequency, and that includes US! Colour Therapy is a truly holistic, non-invasive and powerful therapy which dates back thousands of years; evidence of this can be found in ancient texts from India, China and Egypt. Even when you are tuned in to the color around you, like on a sunny day when a wall looks brighter than usual, or on a cloudy day when color seems drab and lifeless, it can still be challenging to pin down exactly why you are responding in a particular way. That's because multiple signals all converge into a single response to a color or color scheme. Color psychology reveals that there is more happening under the surface than we realize.
Allow us to fulfill your experience with colors
And give some personalized expert
color therapy!
Color theory is the idea that the colors a person is surrounded ...
Color therapy (or chromotherapy) is an alternative remedy that uses color and light to treat physical..
With a long history in the annals of complementary and alternative medicine...
Simply put, colour is just light of varying wavelengths and frequencies. Electromagnetic waves constantly surround us....


The Tinnos Farm Trip (TFT) is An agriculture tourism company located in RWANDA, , this country contains Gishwati forest with cow farms and park of Great Apes,rivers and many populations farms of different crops like :tea, maize ,wheat, irish potatoes, wheat and pyrethrum. Rwanda is known as a safe place with customer care culture among st the population. The TFT.ltd will be set up as a sole proprietorship of TWAGIRAMUNGU Innocent. The TFT will reside in all province of Rwanda with province coordinator. Each province coordinator also will have subordinates that represent districts as well sector representatives.
Each district will have a special area of farms for visit with amazing agriculture farming practices that will help the Rwandan farmers to farm extensively. TFT will have agriculture sciences graduates as staffs , volunteers for explaining the visitors who came in farms trip events, and security officers so as to be sure of security of their personal properties and the camera man for taking photos to be shared with visitors for their whole day farm trip. While trekking in farm trip their will be a plenty of fresh fruits and drinks and fresh agriculture related products offered to customers.
TFT will have partnership with music superstars and other fashioned people and government leaders so as to attract and offer an amazing service to our clients,Tinnos Farm Trip (TFT) will have an attractive big buses for travel while tripping in the country of thousands hills.


SRM Helper is a one stop solution for each and every need of a Srmite specially focussing on NCR Campus for now.
Use the above given menu to navigate.
Together we stand as the largest community in SRM NCR with 1000+ members.
Notes, question papers, ppts?
We have got your back.
To make life of Srmites simpler.


Patch.convenientFromDiff Async
Patch.convenientFromDiff(diff).then(function(stdVectorPatchdata) {
// Use stdVectorPatchdata
});
| Parameters |
Type |
|
| diff |
Diff |
|
Patch.fromBlobAndBuffer Async
Patch.fromBlobAndBuffer(old_blob, old_as_path, buffer, buffer_len, buffer_as_path, opts).then(function(patch) {
// Use patch
});
| Parameters |
Type |
|
| old_blob |
Blob |
Blob for old side of diff, or NULL for empty blob |
| old_as_path |
String |
Treat old blob as if it had this filename; can be NULL |
| buffer |
String |
Raw data for new side of diff, or NULL for empty |
| buffer_len |
Number |
Length of raw data for new side of diff |
| buffer_as_path |
String |
Treat buffer as if it had this filename; can be NULL |
| opts |
DiffOptions |
Options for diff, or NULL for default options |
| Returns |
|
| Patch |
The generated patch; NULL on error |
Patch.fromBlobs Async
Patch.fromBlobs(old_blob, old_as_path, new_blob, new_as_path, opts).then(function(patch) {
// Use patch
});
| Parameters |
Type |
|
| old_blob |
Blob |
Blob for old side of diff, or NULL for empty blob |
| old_as_path |
String |
Treat old blob as if it had this filename; can be NULL |
| new_blob |
Blob |
Blob for new side of diff, or NULL for empty blob |
| new_as_path |
String |
Treat new blob as if it had this filename; can be NULL |
| opts |
DiffOptions |
Options for diff, or NULL for default options |
| Returns |
|
| Patch |
The generated patch; NULL on error |
Patch.fromDiff Async
Patch.fromDiff(diff, idx).then(function(patch) {
// Use patch
});
| Parameters |
Type |
|
| diff |
Diff |
Diff list object |
| idx |
Number |
Index into diff list |
| Returns |
|
| Patch |
Output parameter for the delta patch object |
Patch#getDelta Sync
var diffDelta = patch.getDelta();
Patch#getHunk Async
patch.getHunk(hunk_idx).then(function(result) {
// Use result
});
| Parameters |
Type |
| hunk_idx |
Number |
| Returns |
|
| Number |
Output count of total lines in this hunk |
Patch#getLineInHunk Async
patch.getLineInHunk(hunk_idx, line_of_hunk).then(function(diffLine) {
// Use diffLine
});
| Parameters |
Type |
| hunk_idx |
Number |
| line_of_hunk |
Number |
| Returns |
|
| DiffLine |
The git_diff_line data for this line |
Patch#lineStats Sync
var result = patch.lineStats();
| Returns |
|
| Number |
Count of deletion lines in output, can be NULL. |
Patch#numHunks Sync
var result = patch.numHunks();
Patch#numLinesInHunk Sync
var result = patch.numLinesInHunk(hunk_idx);
| Parameters |
Type |
| hunk_idx |
Number |
| Returns |
|
| Number |
Number of lines in hunk or -1 if invalid hunk index |
Patch#size Sync
var result = patch.size(include_context, include_hunk_headers, include_file_headers);
| Parameters |
Type |
| include_context |
Number |
| include_hunk_headers |
Number |
| include_file_headers |
Number |
| Returns |
|
| Number |
The number of bytes of data |


Chapter 1 – The Machine Learning landscape
This is the code used to generate some of the figures in chapter 1.
| Run in Google Colab |
Code example 1-1¶
Although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead.
# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
This function just merges the OECD’s life satisfaction data and the IMF’s GDP per capita data. It’s a bit too long and boring and it’s not specific to Machine Learning, which is why I left it out of the book.
def prepare_country_stats(oecd_bli, gdp_per_capita):
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,
left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
The code in the book expects the data files to be located in the current directory. I just tweaked it here to fetch the files in datasets/lifesat.
import os
datapath = os.path.join("datasets", "lifesat", "")
# To plot pretty figures directly within Jupyter
%matplotlib inline
import matplotlib as mpl
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# Download the data
import urllib
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
os.makedirs(datapath, exist_ok=True)
for filename in ("oecd_bli_2015.csv", "gdp_per_capita.csv"):
print("Downloading", filename)
url = DOWNLOAD_ROOT + "datasets/lifesat/" + filename
urllib.request.urlretrieve(url, datapath + filename)
Downloading oecd_bli_2015.csv
Downloading gdp_per_capita.csv
# Code example
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model
# Load the data
oecd_bli = pd.read_csv(datapath + "oecd_bli_2015.csv", thousands=',')
gdp_per_capita = pd.read_csv(datapath + "gdp_per_capita.csv",thousands=',',delimiter='\t',
encoding='latin1', na_values="n/a")
# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()
# Select a linear model
model = sklearn.linear_model.LinearRegression()
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = [[22587]] # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.96242338]]
[[5.96242338]]
/Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver. linalg.lstsq(X, y)
Note: you can ignore the rest of this notebook, it just generates many of the figures in chapter 1.¶
Create a function to save the figures.
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "fundamentals"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
Make this notebook’s output stable across runs:
np.random.seed(42)
Load and prepare Life satisfaction data¶
If you want, you can get fresh data from the OECD’s website.
Download the CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI
and save it to datasets/lifesat/
.
oecd_bli = pd.read_csv(datapath + "oecd_bli_2015.csv", thousands=',')
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
oecd_bli.head(2)
| Indicator | Air pollution | Assault rate | Consultation on rule-making | Dwellings without basic facilities | Educational attainment | Employees working very long hours | Employment rate | Homicide rate | Household net adjusted disposable income | Household net financial wealth | ... | Long-term unemployment rate | Personal earnings | Quality of support network | Rooms per person | Self-reported health | Student skills | Time devoted to leisure and personal care | Voter turnout | Water quality | Years in education |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||||||
| Australia | 13.0 | 2.1 | 10.5 | 1.1 | 76.0 | 14.02 | 72.0 | 0.8 | 31588.0 | 47657.0 | ... | 1.08 | 50449.0 | 92.0 | 2.3 | 85.0 | 512.0 | 14.41 | 93.0 | 91.0 | 19.4 |
| Austria | 27.0 | 3.4 | 7.1 | 1.0 | 83.0 | 7.61 | 72.0 | 0.4 | 31173.0 | 49887.0 | ... | 1.19 | 45199.0 | 89.0 | 1.6 | 69.0 | 500.0 | 14.46 | 75.0 | 94.0 | 17.0 |
2 rows × 24 columns
oecd_bli["Life satisfaction"].head()
Country
Australia 7.3
Austria 6.9
Belgium 6.9
Brazil 7.0
Canada 7.3
Name: Life satisfaction, dtype: float64
Load and prepare GDP per capita data¶
Just like above, you can update the GDP per capita data if you want. Just download data from http://goo.gl/j1MSKe (=> imf.org) and save it to datasets/lifesat/
.
gdp_per_capita = pd.read_csv(datapath+"gdp_per_capita.csv", thousands=',', delimiter='\t',
encoding='latin1', na_values="n/a")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
gdp_per_capita.head(2)
| Subject Descriptor | Units | Scale | Country/Series-specific Notes | GDP per capita | Estimates Start After | |
|---|---|---|---|---|---|---|
| Country | ||||||
| Afghanistan | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 599.994 | 2013.0 |
| Albania | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 3995.383 | 2010.0 |
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
full_country_stats
| Air pollution | Assault rate | Consultation on rule-making | Dwellings without basic facilities | Educational attainment | Employees working very long hours | Employment rate | Homicide rate | Household net adjusted disposable income | Household net financial wealth | ... | Time devoted to leisure and personal care | Voter turnout | Water quality | Years in education | Subject Descriptor | Units | Scale | Country/Series-specific Notes | GDP per capita | Estimates Start After | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||||||
| Brazil | 18.0 | 7.9 | 4.0 | 6.7 | 45.0 | 10.41 | 67.0 | 25.5 | 11664.0 | 6844.0 | ... | 14.97 | 79.0 | 72.0 | 16.3 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 8669.998 | 2014.0 |
| Mexico | 30.0 | 12.8 | 9.0 | 4.2 | 37.0 | 28.83 | 61.0 | 23.4 | 13085.0 | 9056.0 | ... | 13.89 | 63.0 | 67.0 | 14.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 9009.280 | 2015.0 |
| Russia | 15.0 | 3.8 | 2.5 | 15.1 | 94.0 | 0.16 | 69.0 | 12.8 | 19292.0 | 3412.0 | ... | 14.97 | 65.0 | 56.0 | 16.0 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 9054.914 | 2015.0 |
| Turkey | 35.0 | 5.0 | 5.5 | 12.7 | 34.0 | 40.86 | 50.0 | 1.2 | 14095.0 | 3251.0 | ... | 13.42 | 88.0 | 62.0 | 16.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 9437.372 | 2013.0 |
| Hungary | 15.0 | 3.6 | 7.9 | 4.8 | 82.0 | 3.19 | 58.0 | 1.3 | 15442.0 | 13277.0 | ... | 15.04 | 62.0 | 77.0 | 17.6 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 12239.894 | 2015.0 |
| Poland | 33.0 | 1.4 | 10.8 | 3.2 | 90.0 | 7.41 | 60.0 | 0.9 | 17852.0 | 10919.0 | ... | 14.20 | 55.0 | 79.0 | 18.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 12495.334 | 2014.0 |
| Chile | 46.0 | 6.9 | 2.0 | 9.4 | 57.0 | 15.42 | 62.0 | 4.4 | 14533.0 | 17733.0 | ... | 14.41 | 49.0 | 73.0 | 16.5 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 13340.905 | 2014.0 |
| Slovak Republic | 13.0 | 3.0 | 6.6 | 0.6 | 92.0 | 7.02 | 60.0 | 1.2 | 17503.0 | 8663.0 | ... | 14.99 | 59.0 | 81.0 | 16.3 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 15991.736 | 2015.0 |
| Czech Republic | 16.0 | 2.8 | 6.8 | 0.9 | 92.0 | 6.98 | 68.0 | 0.8 | 18404.0 | 17299.0 | ... | 14.98 | 59.0 | 85.0 | 18.1 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 17256.918 | 2015.0 |
| Estonia | 9.0 | 5.5 | 3.3 | 8.1 | 90.0 | 3.30 | 68.0 | 4.8 | 15167.0 | 7680.0 | ... | 14.90 | 64.0 | 79.0 | 17.5 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 17288.083 | 2014.0 |
| Greece | 27.0 | 3.7 | 6.5 | 0.7 | 68.0 | 6.16 | 49.0 | 1.6 | 18575.0 | 14579.0 | ... | 14.91 | 64.0 | 69.0 | 18.6 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 18064.288 | 2014.0 |
| Portugal | 18.0 | 5.7 | 6.5 | 0.9 | 38.0 | 9.62 | 61.0 | 1.1 | 20086.0 | 31245.0 | ... | 14.95 | 58.0 | 86.0 | 17.6 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 19121.592 | 2014.0 |
| Slovenia | 26.0 | 3.9 | 10.3 | 0.5 | 85.0 | 5.63 | 63.0 | 0.4 | 19326.0 | 18465.0 | ... | 14.62 | 52.0 | 88.0 | 18.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 20732.482 | 2015.0 |
| Spain | 24.0 | 4.2 | 7.3 | 0.1 | 55.0 | 5.89 | 56.0 | 0.6 | 22477.0 | 24774.0 | ... | 16.06 | 69.0 | 71.0 | 17.6 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 25864.721 | 2014.0 |
| Korea | 30.0 | 2.1 | 10.4 | 4.2 | 82.0 | 18.72 | 64.0 | 1.1 | 19510.0 | 29091.0 | ... | 14.63 | 76.0 | 78.0 | 17.5 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 27195.197 | 2014.0 |
| Italy | 21.0 | 4.7 | 5.0 | 1.1 | 57.0 | 3.66 | 56.0 | 0.7 | 25166.0 | 54987.0 | ... | 14.98 | 75.0 | 71.0 | 16.8 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 29866.581 | 2015.0 |
| Japan | 24.0 | 1.4 | 7.3 | 6.4 | 94.0 | 22.26 | 72.0 | 0.3 | 26111.0 | 86764.0 | ... | 14.93 | 53.0 | 85.0 | 16.3 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 32485.545 | 2015.0 |
| Israel | 21.0 | 6.4 | 2.5 | 3.7 | 85.0 | 16.03 | 67.0 | 2.3 | 22104.0 | 52933.0 | ... | 14.48 | 68.0 | 68.0 | 15.8 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 35343.336 | 2015.0 |
| New Zealand | 11.0 | 2.2 | 10.3 | 0.2 | 74.0 | 13.87 | 73.0 | 1.2 | 23815.0 | 28290.0 | ... | 14.87 | 77.0 | 89.0 | 18.1 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 37044.891 | 2015.0 |
| France | 12.0 | 5.0 | 3.5 | 0.5 | 73.0 | 8.15 | 64.0 | 0.6 | 28799.0 | 48741.0 | ... | 15.33 | 80.0 | 82.0 | 16.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 37675.006 | 2015.0 |
| Belgium | 21.0 | 6.6 | 4.5 | 2.0 | 72.0 | 4.57 | 62.0 | 1.1 | 28307.0 | 83876.0 | ... | 15.71 | 89.0 | 87.0 | 18.9 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 40106.632 | 2014.0 |
| Germany | 16.0 | 3.6 | 4.5 | 0.1 | 86.0 | 5.25 | 73.0 | 0.5 | 31252.0 | 50394.0 | ... | 15.31 | 72.0 | 95.0 | 18.2 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 40996.511 | 2014.0 |
| Finland | 15.0 | 2.4 | 9.0 | 0.6 | 85.0 | 3.58 | 69.0 | 1.4 | 27927.0 | 18761.0 | ... | 14.89 | 69.0 | 94.0 | 19.7 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 41973.988 | 2014.0 |
| Canada | 15.0 | 1.3 | 10.5 | 0.2 | 89.0 | 3.94 | 72.0 | 1.5 | 29365.0 | 67913.0 | ... | 14.25 | 61.0 | 91.0 | 17.2 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 43331.961 | 2015.0 |
| Netherlands | 30.0 | 4.9 | 6.1 | 0.0 | 73.0 | 0.45 | 74.0 | 0.9 | 27888.0 | 77961.0 | ... | 15.44 | 75.0 | 92.0 | 18.7 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 43603.115 | 2014.0 |
| Austria | 27.0 | 3.4 | 7.1 | 1.0 | 83.0 | 7.61 | 72.0 | 0.4 | 31173.0 | 49887.0 | ... | 14.46 | 75.0 | 94.0 | 17.0 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 43724.031 | 2015.0 |
| United Kingdom | 13.0 | 1.9 | 11.5 | 0.2 | 78.0 | 12.70 | 71.0 | 0.3 | 27029.0 | 60778.0 | ... | 14.83 | 66.0 | 88.0 | 16.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 43770.688 | 2015.0 |
| Sweden | 10.0 | 5.1 | 10.9 | 0.0 | 88.0 | 1.13 | 74.0 | 0.7 | 29185.0 | 60328.0 | ... | 15.11 | 86.0 | 95.0 | 19.3 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 49866.266 | 2014.0 |
| Iceland | 18.0 | 2.7 | 5.1 | 0.4 | 71.0 | 12.25 | 82.0 | 0.3 | 23965.0 | 43045.0 | ... | 14.61 | 81.0 | 97.0 | 19.8 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 50854.583 | 2014.0 |
| Australia | 13.0 | 2.1 | 10.5 | 1.1 | 76.0 | 14.02 | 72.0 | 0.8 | 31588.0 | 47657.0 | ... | 14.41 | 93.0 | 91.0 | 19.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 50961.865 | 2014.0 |
| Ireland | 13.0 | 2.6 | 9.0 | 0.2 | 75.0 | 4.20 | 60.0 | 0.8 | 23917.0 | 31580.0 | ... | 15.19 | 70.0 | 80.0 | 17.6 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 51350.744 | 2014.0 |
| Denmark | 15.0 | 3.9 | 7.0 | 0.9 | 78.0 | 2.03 | 73.0 | 0.3 | 26491.0 | 44488.0 | ... | 16.06 | 88.0 | 94.0 | 19.4 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 52114.165 | 2015.0 |
| United States | 18.0 | 1.5 | 8.3 | 0.1 | 89.0 | 11.30 | 67.0 | 5.2 | 41355.0 | 145769.0 | ... | 14.27 | 68.0 | 85.0 | 17.2 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 55805.204 | 2015.0 |
| Norway | 16.0 | 3.3 | 8.1 | 0.3 | 82.0 | 2.82 | 75.0 | 0.6 | 33492.0 | 8797.0 | ... | 15.56 | 78.0 | 94.0 | 17.9 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 74822.106 | 2015.0 |
| Switzerland | 20.0 | 4.2 | 8.4 | 0.0 | 86.0 | 6.72 | 80.0 | 0.5 | 33491.0 | 108823.0 | ... | 14.98 | 49.0 | 96.0 | 17.3 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 80675.308 | 2015.0 |
| Luxembourg | 12.0 | 4.3 | 6.0 | 0.1 | 78.0 | 3.47 | 66.0 | 0.4 | 38951.0 | 61765.0 | ... | 15.12 | 91.0 | 86.0 | 15.1 | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 101994.093 | 2014.0 |
36 rows × 30 columns
full_country_stats[["GDP per capita", 'Life satisfaction']].loc["United States"]
GDP per capita 55805.204
Life satisfaction 7.200
Name: United States, dtype: float64
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
sample_data = full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
missing_data = full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[remove_indices]
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.axis([0, 60000, 0, 10])
position_text = {
"Hungary": (5000, 1),
"Korea": (18000, 1.7),
"France": (29000, 2.4),
"Australia": (40000, 3.0),
"United States": (52000, 3.8),
}
for country, pos_text in position_text.items():
pos_data_x, pos_data_y = sample_data.loc[country]
country = "U.S." if country == "United States" else country
plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,
arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))
plt.plot(pos_data_x, pos_data_y, "ro")
plt.xlabel("GDP per capita (USD)")
save_fig('money_happy_scatterplot')
plt.show()
Saving figure money_happy_scatterplot
sample_data.to_csv(os.path.join("datasets", "lifesat", "lifesat.csv"))
sample_data.loc[list(position_text.keys())]
| GDP per capita | Life satisfaction | |
|---|---|---|
| Country | ||
| Hungary | 12239.894 | 4.9 |
| Korea | 27195.197 | 5.8 |
| France | 37675.006 | 6.5 |
| Australia | 50961.865 | 7.3 |
| United States | 55805.204 | 7.2 |
import numpy as np
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.xlabel("GDP per capita (USD)")
plt.axis([0, 60000, 0, 10])
X=np.linspace(0, 60000, 1000)
plt.plot(X, 2*X/100000, "r")
plt.text(40000, 2.7, r"$\theta_0 = 0$", fontsize=14, color="r")
plt.text(40000, 1.8, r"$\theta_1 = 2 \times 10^{-5}$", fontsize=14, color="r")
plt.plot(X, 8 - 5*X/100000, "g")
plt.text(5000, 9.1, r"$\theta_0 = 8$", fontsize=14, color="g")
plt.text(5000, 8.2, r"$\theta_1 = -5 \times 10^{-5}$", fontsize=14, color="g")
plt.plot(X, 4 + 5*X/100000, "b")
plt.text(5000, 3.5, r"$\theta_0 = 4$", fontsize=14, color="b")
plt.text(5000, 2.6, r"$\theta_1 = 5 \times 10^{-5}$", fontsize=14, color="b")
save_fig('tweaking_model_params_plot')
plt.show()
Saving figure tweaking_model_params_plot
from sklearn import linear_model
lin1 = linear_model.LinearRegression()
Xsample = np.c_[sample_data["GDP per capita"]]
ysample = np.c_[sample_data["Life satisfaction"]]
lin1.fit(Xsample, ysample)
t0, t1 = lin1.intercept_[0], lin1.coef_[0][0]
t0, t1
(4.853052800266436, 4.911544589158484e-05)
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3))
plt.xlabel("GDP per capita (USD)")
plt.axis([0, 60000, 0, 10])
X=np.linspace(0, 60000, 1000)
plt.plot(X, t0 + t1*X, "b")
plt.text(5000, 3.1, r"$\theta_0 = 4.85$", fontsize=14, color="b")
plt.text(5000, 2.2, r"$\theta_1 = 4.91 \times 10^{-5}$", fontsize=14, color="b")
save_fig('best_fit_model_plot')
plt.show()
Saving figure best_fit_model_plot
cyprus_gdp_per_capita = gdp_per_capita.loc["Cyprus"]["GDP per capita"]
print(cyprus_gdp_per_capita)
cyprus_predicted_life_satisfaction = lin1.predict([[cyprus_gdp_per_capita]])[0][0]
cyprus_predicted_life_satisfaction
22587.49
5.96244744318815
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(5,3), s=1)
plt.xlabel("GDP per capita (USD)")
X=np.linspace(0, 60000, 1000)
plt.plot(X, t0 + t1*X, "b")
plt.axis([0, 60000, 0, 10])
plt.text(5000, 7.5, r"$\theta_0 = 4.85$", fontsize=14, color="b")
plt.text(5000, 6.6, r"$\theta_1 = 4.91 \times 10^{-5}$", fontsize=14, color="b")
plt.plot([cyprus_gdp_per_capita, cyprus_gdp_per_capita], [0, cyprus_predicted_life_satisfaction], "r--")
plt.text(25000, 5.0, r"Prediction = 5.96", fontsize=14, color="b")
plt.plot(cyprus_gdp_per_capita, cyprus_predicted_life_satisfaction, "ro")
save_fig('cyprus_prediction_plot')
plt.show()
Saving figure cyprus_prediction_plot
sample_data[7:10]
| GDP per capita | Life satisfaction | |
|---|---|---|
| Country | ||
| Portugal | 19121.592 | 5.1 |
| Slovenia | 20732.482 | 5.7 |
| Spain | 25864.721 | 6.5 |
(5.1+5.7+6.5)/3
5.766666666666667
backup = oecd_bli, gdp_per_capita
def prepare_country_stats(oecd_bli, gdp_per_capita):
oecd_bli = oecd_bli[oecd_bli["INEQUALITY"]=="TOT"]
oecd_bli = oecd_bli.pivot(index="Country", columns="Indicator", values="Value")
gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
gdp_per_capita.set_index("Country", inplace=True)
full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,
left_index=True, right_index=True)
full_country_stats.sort_values(by="GDP per capita", inplace=True)
remove_indices = [0, 1, 6, 8, 33, 34, 35]
keep_indices = list(set(range(36)) - set(remove_indices))
return full_country_stats[["GDP per capita", 'Life satisfaction']].iloc[keep_indices]
# Code example
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model
# Load the data
oecd_bli = pd.read_csv(datapath + "oecd_bli_2015.csv", thousands=',')
gdp_per_capita = pd.read_csv(datapath + "gdp_per_capita.csv",thousands=',',delimiter='\t',
encoding='latin1', na_values="n/a")
# Prepare the data
country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Visualize the data
country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction')
plt.show()
# Select a linear model
model = sklearn.linear_model.LinearRegression()
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = [[22587]] # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.96242338]]
[[5.96242338]]
oecd_bli, gdp_per_capita = backup
missing_data
| GDP per capita | Life satisfaction | |
|---|---|---|
| Country | ||
| Brazil | 8669.998 | 7.0 |
| Mexico | 9009.280 | 6.7 |
| Chile | 13340.905 | 6.7 |
| Czech Republic | 17256.918 | 6.5 |
| Norway | 74822.106 | 7.4 |
| Switzerland | 80675.308 | 7.5 |
| Luxembourg | 101994.093 | 6.9 |
position_text2 = {
"Brazil": (1000, 9.0),
"Mexico": (11000, 9.0),
"Chile": (25000, 9.0),
"Czech Republic": (35000, 9.0),
"Norway": (60000, 3),
"Switzerland": (72000, 3.0),
"Luxembourg": (90000, 3.0),
}
sample_data.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(8,3))
plt.axis([0, 110000, 0, 10])
for country, pos_text in position_text2.items():
pos_data_x, pos_data_y = missing_data.loc[country]
plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,
arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))
plt.plot(pos_data_x, pos_data_y, "rs")
X=np.linspace(0, 110000, 1000)
plt.plot(X, t0 + t1*X, "b:")
lin_reg_full = linear_model.LinearRegression()
Xfull = np.c_[full_country_stats["GDP per capita"]]
yfull = np.c_[full_country_stats["Life satisfaction"]]
lin_reg_full.fit(Xfull, yfull)
t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]
X = np.linspace(0, 110000, 1000)
plt.plot(X, t0full + t1full * X, "k")
plt.xlabel("GDP per capita (USD)")
save_fig('representative_training_data_scatterplot')
plt.show()
Saving figure representative_training_data_scatterplot
full_country_stats.plot(kind='scatter', x="GDP per capita", y='Life satisfaction', figsize=(8,3))
plt.axis([0, 110000, 0, 10])
from sklearn import preprocessing
from sklearn import pipeline
poly = preprocessing.PolynomialFeatures(degree=60, include_bias=False)
scaler = preprocessing.StandardScaler()
lin_reg2 = linear_model.LinearRegression()
pipeline_reg = pipeline.Pipeline([('poly', poly), ('scal', scaler), ('lin', lin_reg2)])
pipeline_reg.fit(Xfull, yfull)
curve = pipeline_reg.predict(X[:, np.newaxis])
plt.plot(X, curve)
plt.xlabel("GDP per capita (USD)")
save_fig('overfitting_model_plot')
plt.show()
/Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:1508: RuntimeWarning: overflow encountered in multiply sqr = np.multiply(arr, arr, out=arr) /Users/ageron/miniconda3/envs/tf2/lib/python3.6/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
Saving figure overfitting_model_plot
full_country_stats.loc[[c for c in full_country_stats.index if "W" in c.upper()]]["Life satisfaction"]
Country
New Zealand 7.3
Sweden 7.2
Norway 7.4
Switzerland 7.5
Name: Life satisfaction, dtype: float64
gdp_per_capita.loc[[c for c in gdp_per_capita.index if "W" in c.upper()]].head()
| Subject Descriptor | Units | Scale | Country/Series-specific Notes | GDP per capita | Estimates Start After | |
|---|---|---|---|---|---|---|
| Country | ||||||
| Botswana | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 6040.957 | 2008.0 |
| Kuwait | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 29363.027 | 2014.0 |
| Malawi | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 354.275 | 2011.0 |
| New Zealand | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 37044.891 | 2015.0 |
| Norway | Gross domestic product per capita, current prices | U.S. dollars | Units | See notes for: Gross domestic product, curren... | 74822.106 | 2015.0 |
plt.figure(figsize=(8,3))
plt.xlabel("GDP per capita")
plt.ylabel('Life satisfaction')
plt.plot(list(sample_data["GDP per capita"]), list(sample_data["Life satisfaction"]), "bo")
plt.plot(list(missing_data["GDP per capita"]), list(missing_data["Life satisfaction"]), "rs")
X = np.linspace(0, 110000, 1000)
plt.plot(X, t0full + t1full * X, "r--", label="Linear model on all data")
plt.plot(X, t0 + t1*X, "b:", label="Linear model on partial data")
ridge = linear_model.Ridge(alpha=10**9.5)
Xsample = np.c_[sample_data["GDP per capita"]]
ysample = np.c_[sample_data["Life satisfaction"]]
ridge.fit(Xsample, ysample)
t0ridge, t1ridge = ridge.intercept_[0], ridge.coef_[0][0]
plt.plot(X, t0ridge + t1ridge * X, "b", label="Regularized linear model on partial data")
plt.legend(loc="lower right")
plt.axis([0, 110000, 0, 10])
plt.xlabel("GDP per capita (USD)")
save_fig('ridge_model_plot')
plt.show()
Saving figure ridge_model_plot
backup = oecd_bli, gdp_per_capita
def prepare_country_stats(oecd_bli, gdp_per_capita):
return sample_data
# Replace this linear model:
import sklearn.linear_model
model = sklearn.linear_model.LinearRegression()
# with this k-neighbors regression model:
import sklearn.neighbors
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors=3)
X = np.c_[country_stats["GDP per capita"]]
y = np.c_[country_stats["Life satisfaction"]]
# Train the model
model.fit(X, y)
# Make a prediction for Cyprus
X_new = np.array([[22587.0]]) # Cyprus' GDP per capita
print(model.predict(X_new)) # outputs [[ 5.76666667]]
[[5.76666667]]


Created by JP Maxwell MD a Hospitalist with Swedish Medical Center in Seattle, Washington and David Hui MD, MSc an Associate Professor of Oncology and Palliative Care at MD Anderson in Houston, Texas.
This is a non-profit endeavor to promote the use of validated prognostic scores to help predict survival more accurately. It is a calculator only and no information is being saved or stored. This work is not endorsed by nor affiliated with our respective employers. If you have any questions about how it works feel free to look at the source code that is linked below. It is open licensed and thus free to build upon or reuse if you wish.
FAQ:
Q: Why don't you include prognostic model X, Y or Z?
A: There are a lot of prognostic models out there and more are being developed all the time. If you have any suggestions about other prognostic models please email us!
Q: Where was the background picture taken?
A: This was taken on a hike in the foothills surrounding the Yakima Valley in the fall after some rain.


BT keeps Champions League soccer rights in US$1.5 billion UK deal
BT has retained the UK broadcast rights to Champions League soccer for three years from 2021 at an annual cost of 400 million pounds (US$512 million), despite reported competition from rival Sky.
LONDON: BT has retained the UK broadcast rights to Champions League soccer for three years from 2021 at an annual cost of 400 million pounds (US$512 million), despite reported competition from rival Sky.
The price is in line with the current three-year contract but BT said it covered an additional 77 matches as well as the rights to highlights and in-match clips.
The renegotiation had been seen as a test of the appetite of BT Chief Executive Philip Jansen, who took on the role in February, to spend on sports rights.
The deal also covers matches from the Europa League and a planned third competition for European clubs, BT said in a statement on Friday.
"We're delighted to remain the home of UEFA Champions League in the UK," said Marc Allera, CEO of BT's Consumer Division
BT has had the rights to European club soccer since 2015 after ousting Sky and ITV. Sky, now owned by U.S. media group Comcast, had been expected to bid to win back the rights.
The news helped BT shares to make up some lost ground after Britain's opposition Labour Party said it would nationalise BT's fixed line network to provide free full-fibre broadband for all if it wins the election next month.
(US$1 = 0.7815 pounds)
(Reporting by Keith Weir; Editing by Mark Potter and Alexander Smith)


Man drowns after car slips off Donegal pier
One man has drowned and another cheated death after a tragedy on Arranmore Island off the coast of Donegal.
The accident happened when a car slipped off the local pier on the island overnight.
Two men in the car became submerged underwater.
Local sources say the men may have been trying to turn the car on the pier when it slipped into the sea.
One managed to get out of the vehicle but one was left trapped inside.
It is understood the man who died was a 26-year-old local man who was the driver of the car.
A full Garda investigation into the cause of the tragedy is underway.
READ MORE
Calls for Sinn Féin candidate to apologise over alleged Facebook Live video


Swedish word of the day: hoppsan
by Catherine EdwardsHere's a helpful word to help make your Swedish sound more natural.
When you're learning a language, some of the words that can improve your fluency most quickly are the small filler words. In Swedish, that includes oj, typ, liksom – and hoppsan.
Hoppsan translates as 'oops', or 'whoops' in English. But it's slightly less common, and in the same context a Swede might instead say oj, ojdå, or simply oops.
Hoppsan acknowledges that something unfortunate, often unintended and unexpected, has happened; something's gone wrong. But it also implies that the problem isn't a serious one.
It's about showing that there's been a mistake, and not about who's responsible, so you could say hoppsan whether it's you or someone else who's made the mishap.
It has an informal and jokey tone and you'll often hear it said to children in a cheerful way, "hoppsan!", if they fall over or drop something, to reassure them that it's no big deal.
You'll also hear it among adults too, although less frequently. If you bump into a stranger, it would be more usual to say oj or förlåt (sorry) than the more friendly hoppsan.
Among adults, hoppsan might be a neutral (and informal) acknowledgement of an accident (hoppsan, jag tappade den – whoops, I dropped it) but it could also be used teasingly or sarcastically, to make fun of someone being accident-prone or to imply that something wasn't really an accident.
Expressions that sound a bit like hoppsan pop up frequently in European languages. As well as English 'oops', the French say oups, the Spanish say opa, and the Germans hoppla.
The -san ending is specific to Swedish, and it's a common addition to exclamations. For example, hejsan is an alternative way to say 'hello', and jajamensan, an emphatic agreement. Jajamensan has a different origin, but the -san in hejsan and hoppsan probably comes from a shortening of 'sade han' ('he said'). In spoken Swedish the -de ending of sade is often dropped to become 'sa han'.
Examples
Hoppsan, det tänkte jag inte på
Whoops, I didn't think about that
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Wing¶
-
class
components.liftingsurfaces.wing.
Wing
(*args, **kwargs)[source]¶ This class will create the wing geometry based on the required: Wing Area (class I output), Aspect Ratio (class I input), taper ratio (assumed), dihedral angle (assumed), wing twist angle (assumed) and airfoil selection. It also can perform an avl analysis on the wing to obtain the linear value of C_L vs alpha and the pitching moment vs. angle of attack.
Returns: ParaPy Geometry of the Main Wing Surface
Return type: Parameters: - wing_loading (float) – This is the required wing loading from the class I weight estimation in [N/m^2]
- weight_mtow (float) – This is the required MTOW from the class I weight estimation in kg
- stall_speed (float) – This is the stall speed of the wing from the class I weight estimation in m/s.
- rho (float) – This is the ISA STD sea level density in kg/m^3.
- fuse_width_factor (float) – This is the assumed factor of the semispan which the fuselage extends over the wing.
- hide_bbox (bool) – This is a switch to hide/show the bbox of the wing section within the fuselage.
- hide_mac (bool) – This overwrites input from LiftingSurface to hide the Mean Aerodynamic Chord part
- mesh_deflection (float) – The default value is an optimum between a good quality render and performance.
- color (tuple) – Changes the color of the wing skin to the one defined in MyColors
- ply_number (int) – Changes the number of ply’s of carbon fiber pre-preg.
-
alpha_cases
[source]¶ ” Here we define the run cases for AVL. We are running input cases alpha from 0 to 10 with 25 data points at the speed required for the controllability curve (1.2 x v_s).
Returns: AVL List of RunCases Return type: Case
-
avl_data_grabber
[source]¶ Here, we grab and sort the data by angle of attack from AVL.
Returns: Sort AVL data Return type: dict
-
avl_results
[source]¶ ” Here, the results are extracted and stored in the memory of ParaPy.
Returns: Dictionary with AVL Results Return type: dict
-
avl_session
[source]¶ ” Here we define the AVL session with the cases above.
Returns: AVL Run Session Return type: Session
-
center_of_gravity
[source]¶ Location of the center of gravity w.r.t the origin (root airfoil leading edge).
Returns: Location of CG w.r.t. Root LE in SI meter Return type: Point
-
color
¶ Changes the color of the wing skin to the one defined in MyColors
-
component_type
[source]¶ This attribute names the component ‘wing’.
Returns: String with wing component name Return type: str
-
external_shape
[source]¶ This defines the external shape for the ExternalBody class in definitions.
Returns: Wing ParaPy Geometry Return type: Tuple
-
fuse_width_factor
¶ Below is the assumed factor of the semi_span which the fuselage extends over the wing.
-
get_wingfuse_bounds
[source]¶ - ” This attribute is obtaining (the dimensions of) a bounded box at a fuselage width factor of the semispan
- which will be used to size the local fuselage frames. These frames drive the shape of the fuselage.
Returns: HT center section bounding box Return type: bbox
-
hide_bbox
¶ Below is a switch to hide/show the bbox of the wing section within the fuselage.
-
hide_mac
¶ Overwrites input from LiftingSurface to hide the Mean Aerodynamic Chord part
-
internal_shape
[source]¶ ” This part creates the bounding box for the part of the wing within the fuselage.
Returns: Wing fuselage frame bbox Return type: Box
-
left_wing
[source]¶ ” This part mirrors the right wing across the X-Z plane to make the left wing.
Returns: ParaPy Left Wing Geometry Return type: MirroredShape
-
lift_coef_control
[source]¶ This is the Required C_L from the lift equation at 1.2*stall_speed @ MTOW for the controllability curve of the scissor plot.
Returns: Lift Coefficient for Controllability Curve of Scissor Plot Return type: float
-
lift_coef_control_index
[source]¶ ” This attribute returns the index of the AVL data corresponding to the case when C_L is closest to the required C_L by the lift equation at 1.2 * V_s for the controllability curve in scissor plot.
Returns: Index of data set with C_L for Controllability curve of Scissor Plot. Return type: int
-
lift_coef_max
[source]¶ This attribute computes the C_L from the lift equation at the provided
stall_speed
to be used in later performance estimations.Returns: Lift Coefficient at Stall (C_L_max) Return type: float
-
lift_coef_vs_alpha
[source]¶ This estimates the lift curve slope.
Returns: Lift Coefficient Gradient [1/rad] Return type: float
-
mesh_deflection
¶ Below is the chosen mesh deflection. It’s is an optimum point between a good quality render and performance
-
moment_coef_control
[source]¶ ” This attribute gets the pitching moment of the wing about its aerodynamic center from avl corresponding to index found from the above lift coefficient at 1.2* V_s.
Returns: C_mac at 1.2*V_s Return type: float
-
planform_area
[source]¶ Instantiating the required variable name for the class ExternalBody
Returns: Wing planform area in m^2 Return type: float
-
plot_liftgradient
[source]¶ This plots the lift coefficient vs angle of attack for the current wing in PyCharm.
Returns: Lift Coefficient vs. angle of attack Plot
-
plot_momentgradient
[source]¶ This plots the moment coefficient vs angle of attack for the current wing in PyCharm.
Returns: Moment Coefficient vs. angle of attack Plot
-
ply_number
¶ Changes the number of ply’s of carbon fiber http://www.ijera.com/papers/Vol4_issue5/Version%202/J45025355.pdf
-
rho
¶ Below is the ISA STD sea level density in kg/m^3.
-
right_cut_plane
[source]¶ ” This makes a plane at the right wing spanwise location where the fuselage is to end.
Returns: Plane at spanwise distance from root chord to cut location. Return type: Plane
-
root_section
[source]¶ ” This defines the root section with the chosen airfoil.
Returns: AVL Root Section Return type: Section
-
s_req
[source]¶ This attribute contains the calculation of the required TOTAL wing area from the design point.
Returns: Calculation of wing aera from design point in m^2 Return type: float
-
show_avlgeom
[source]¶ Allows the user to view the AVL Geometry in a seperate Pop-up window
Returns: AVL Geometry Return type: AVL PltLib Viewer
-
stall_speed
¶ The stall speed of the wing from the class I weight estimation.
-
tip_section
[source]¶ ” Here we define the tip AVL section with proper location and airfoil.
Returns: AVL Tip Section Return type: Section
-
weight_mtow
¶ Below is the required MTOW from the class I weight estimation.
-
wing_cut_loc
[source]¶ ” This calculates the spanwise distance of the cut, inside of which, the wing is inside the fuselage. This location is where the wing bbox to size the fuselage frame(s) ends in the spanwise direction. The height of this bbox is the maximum thickness of the wing at this cut location.
Returns: Spanwise distance from root chord to cut location Return type: float
-
wing_geom
[source]¶ ” Here we define the AVL geometry.
Returns: AVL Wing Geometry Return type: Geometry
-
wing_loading
¶ Below is the required wing loading from the class I weight estimation.


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THE DOOM GENERATION A film review by Will FitzHugh Copyright 1996 Will FitzHugh
This is billed 'A Heterosexual Film by Gregg Araki' who I gather has made some low-budget homosexual films which I haven't seen. This one is a charming coming-of-age piece. At least charming when compared to any other movie that features blood-splattered convenience store robberies, motel-room sex and various psychotic characters met in cheesy retail stores. The main characters, Jordan and Amy, are two love-struck punkers ('three months' Jordan says reverently when asked how long they've been going out) who get caught up in a whirlwind escape from the law after they meet up with Xavier, a slightly twisted guy who gets them out of tight spots by blowing people away. One bit of sample dialog should give you the flavor:
Jordan: What's your name? Xavier: Xavier. Jordan: That's kind of complicated. Can I just call you X? Xavier: OK.  Jordan: OK.
You get the idea. Kind of like a John Hughes movie but with sex, violence and bad language. Everything's very stilted. I assume it's on purpose. As David St. Hubbins said, it's a fine line between stupid and clever, but this film refuses to accept that and blurs them together entirely. Heidi Fleiss has a cameo. Araki pays homage to 'Easy Rider' by having our gang brutalized by redneck nazis at the end. Good double bill with 'Half Cocked' if they ever come out on video.
I saw this movie alone but it could be good for a date if you want to test his/her reaction to things generally considered vile. Might as well initiate the discussion of bodily fluids as soon as possible in a relationship. You're gonna have to deal with them anyway, right?
Copyright 1996 Will FitzHugh
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THE PRINCE OF EGYPT
*** (out of 4) - a good movie
Release Date: December 18, 1998 The Voices of: Val Kilmer, Ralph Fiennes, Sandra Bullock, Jeff Goldblum, Danny Glover, Michelle Pfeiffer, Patrick Stewart, Steve Martin, Martin Short, Helen Mirren Directed by: Brenda Chapman, Steve Hickner, Simon Wells Distributed by: DreamWorks Pictures MPAA Rating: PG (intense depiction of thematic elements) URL: http://www.execpc.com/~kinnopio/reviews/1998/prince.htm
Near the end of 1996, analysts remarked that the year would be the last in which Disney would stand alone as the animation king. The following year, 1997, saw the first sleeper entry into the genre from a studio other than the Magic Kingdom: Twentieth Century Fox gave baronial Russia to the masses in the form of ANASTASIA. At best, that's all it was -- a sleeper hit, designed to surprise but not shake the industry. In 1998, however, things have been decidedly different. There have been a number of pushes on Disney's death grip on the market, and fifty-one weeks into '98, a new champion has emerged.
DreamWorks' THE PRINCE OF EGYPT succeeds where all of the other conventionally animated products have failed. The other two contenders, Warner Brothers' QUEST FOR CAMELOT and Disney's own annual entry, MULAN, lacked inspiration and drive respectively. Although CAMELOT was borderline-abysmal, and can't really be considered a shot at the title, MULAN had the makings of winner. Unfortunately, it was saturated with traditional, Disney-cute animation, and lost points against the rapidly-changing face of the competition. In making THE PRINCE OF EGYPT, DreamWorks has strayed from all of these hallmarks and created a genuinely unique product.
The animation is flawless, and, aside from A BUG'S LIFE, is the best-looking movie to date. The animators have nimbly mixed both flat and spatial effects, and the 2D/3D combination is something that artists from Disney and Fox have heretofore struggled with. The key to success, however, was that the visuals were used not just because they looked cool but also as a method of advancing the plot. Namely, some of the most important action sequences (like the parting of the Red Sea) were done almost entirely in 3D. DreamWorks is the first studio to prove that they've got the stuff of success, and that they're capable of challenging Disney.
The story is also well-written, and the characters voiced with talent. For the most part, the plot follows its source material -- the Book of Exodus -- although only in the most general sense. The writers took a few creative liberties, and most do not damage the story in any way. In fact, the Shakespearean relationship between Moses (Val Kilmer) and the Pharaoh Rameses (Ralph Fiennes) makes the story more interesting if anything. Moses, a Hebrew, is saved from the slaughter of the firstborn sons by his mother when she sets him in a basket on the Nile; he floats into the Pharaoh Seti (Patrick Stewart)'s palace and is taken in by the royal family. Moses grows up but learns of his heritage and flees into the desert, where he discovers the burning bush and is commissioned by God to return to Egypt and free the slaves. By this time, Rameses is now Pharaoh, and the two brothers, now on opposite sides of the line, face off in a battle of wills and divine intervention.
The cast of voices is most impressive, and also includes such noteables as Sandra Bullock, Jeff Goldblum, Danny Glover, and Steve Martin. The script does not have a lot of one-liners in it, and God is reduced to a voice that might well be mistaken for the same one that spoke to Kevin Costner in FIELD OF DREAMS, but overall the story is exciting and interesting. It's decidedly for an older crowd, and the tykes who haven't had a few years of religion class will be hopelessly lost. But for those who make the bid, THE PRINCE OF EGYPT can be a great watch.
-- Craig Roush<EMAIL_ADDRESS>-- Kinnopio's Movie Reviews http://www.execpc.com/~kinnopio
The review above was posted to the
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If a person is unconscious or has a change in mental status, follow these first aid steps:
- Call or tell someone to call 911
- Check the person's airway, breathing, and pulse frequently. If necessary, begin CPR
- If the person is breathing and lying on their back, and you do not think there is a spinal injury, carefully roll the person toward you onto their side. Bend the top leg so both hip and knee are at right angles. Gently tilt their head back to keep the airway open. If breathing or pulse stops at any time, roll the person onto their back and begin CPR
- If you think there is a spinal injury, leave the person where you found them (as long as breathing continues). If the person vomits, roll the entire body at one time to their side. Support their neck and back to keep the head and body in the same position while you roll
- Keep the person warm until medical help arrives
- If you see a person fainting, try to prevent a fall. Lay the person flat on the floor and raise their feet about 12 inches (30 centimeters)
- If fainting is likely due to low blood sugar, give the person something sweet to eat or drink when they become conscious
If the person is unconscious from choking:
- Begin CPR. Chest compressions may help dislodge the object
- If you see something blocking the airway and it is loose, try to remove it. If the object is lodged in the person's throat, DO NOT try to grasp it. This can push the object farther into the airway
- Continue CPR and keep checking to see if the object is dislodged until medical help arrives
DO NOT:
- DO NOT give an unconscious person any food or drink
- DO NOT leave the person alone
- DO NOT place a pillow under the head of an unconscious person.
- DO NOT slap an unconscious person's face or splash water on their face to try to revive them.
Source: https://www.redcross.ca/crc/pdf/First-Aid-and-CPR-2017_digital.pdf