It is important to retake the PSAT even though you are no longer in the 10th grade. This is because you can qualify for the National Merit Scholarsip Program. as well as the National Hispanic Recognition Program. These programs provide recognition, look good on your college applications, and provide scholarship opportunities, so you do not have to pay as much money for college.
English Section: 75questions; 45 mins
Math Section: 60questions; 60 mins
Reading Section: 40questions; 35mins
Science Section: 40questions; 35mins
Writing Section (Optional): 1 essay; 40mins
Reading Section: 52questions; 65 mins
Writing & Language: 44questions; 35mins
Math No Calc. Section: 20questions; 25 mins
Math Calc. Section: 38questions; 55 mins
Essay (Optional): 1 esay; 50mins


ALIEN A Film Review Copyright Dragan Antulov 1998
When science fiction becomes topic of discussions among people who aren't fans or even superficial connoisseurs of the genre, they usually have very narrow definition of what science fiction film is or what it should be. Thanks to the influence of people like George Lucas, Steven Spielberg or Gene Roddenberry, science fiction is considered escapist genre about fantastic worlds almost always better than our own. However, in the late 1970s, after debilitating fuel crisis, unprecedented political turmoil, and unstoppable decline of world economy, future didn't seem bright anymore. Filmmakers suddenly began using the media of science fiction films to express their anxiety and scepticism towards things to come. ALIEN, second feature film by Ridley Scott, also joined the trend, this time by using big budget resources to resurrect almost forgotten sub-genre of SF horror, until then strictly in the B-movie domain.
The plot of ALIEN is set in a future that might look far, yet in the same time share many elements with the world of today. Interstellar flight isn't some breathtaking scientific achievement anymore and private companies use spaceships to bring precious ore to Earth. One of such ships is "Nostromo", whose seven-member crew spends most of the time hibernated. After they are awaken by ship's computer, they realise that they are still far away from Earth. "Nostromo" received a distress signal from an unknown planet and the crew, led by Captain Dallas (Tom Skeritt) is ordered to investigate. They land on the planet and small expedition finds huge alien derelict. Inside the derelict, second officer Kane (John Hurt) finds thousands of eggs, but one of them explodes and alien organism gets attached for his face. Comatose Kane is brought back to the ship, but first officer Ripley (Sigourney Weaver) don't want to let him out of quarantine. Her decision is overridden by science officer Ash (Holm) who breaks security procedure in order to study the organism.
The entire concept of ALIEN - concept that can explain its great success as horror film - is just another story of human beings confronted with their inherent limitations. Technology can make human life longer or easier, yet it can't remove human imperfection, brilliantly demonstrated in this film both on physical and emotional level. Space, which could create sense of wonders with its inconceivable beauty, can in the same time be the source of horrors beyond imagination, horrors that leave puny humans as helpless victims, despite all their superior technology and intelligence.
To illustrate that point, director Scott hired the talents of a Swiss artist H.R. Giger, who had created the most notorious monster in history of cinema. Like nobody else before and after him, Giger succeeded in making a creature that is completely alien from anything in human experience, yet in the same time with shapes and forms that bring back the worst fears from our collective subconscious. Whether it is its a huge head, encroaching tail, clawed hand or double set of constantly salivating mouth with sharp teeth - even the glimpses are enough to make people anxious. Scott very cleverly decided never to show the monster in full shot, leaving to the viewer to draw complete picture in his mind, most probably consumed by irrational fear.
Excellent contrast to all-powerful, invincible and unstoppable monster are imperfect humans that reluctantly become its enemy and prey. The screenwriter Dan O'Bannon, who had already explored those themes in his low-budget classic DARK STAR few years earlier, created very human and believable characters to illustrate such flaws. They aren't scientists or astronauts, they are simple working men more interested in simple pay check than all the wonders of universe, more likely to use four letter words than esoteric scientific terms. Scene immediately after their awakening brilliantly illustrates their disenchantment with space technology - after spending months in hypersleep and travelling tens of light years, the first thing they do is to gather around table, smoke cigarettes and bicker about low wages. Those characters are also enhanced through superb acting by one of the strongest ensemble cast in history of cinema. Most notable is Sigourney Weaver in her debut role, who would prove that women can carry action movie in the sequel; here, in the first film, she is just very capable space officer, with strong will and more sense than anybody else, yet also consumed by terror when she must face the deadly foe. She managed to steal the top spot from nominally lead actor, Tom Skeritt, who plays Captain like a man who can establish authority without raising voice, yet in the same time burdened by indecision and lack of courage. John Hurt is also good, although he has the least of screen time. Ian Holm is wonderful as antipathetic bureaucrat that values scientific results more than human life; small hints about his character only later reveal the even more disturbing truth. Veronica Cartwright, originally cast to play Ripley, is also more than impressive in a quite different role of navigation officer Lambert; at first only bickering, her character turns into incarnation of frailty and panic. Finally, O'Bannon's script manages to create some kind of social stratification even in the such little universe like "Nostromo" - two of the characters are low paid blue-collar types that dwell in the dark and greasy lower decks. Yaphet Kotto is excellent as Parker, bickering giant that has a great heart; and, finally, Harry Dean Stanton gives another subtle performance of his quiet and dim-witted, yet very sympathetic assistant Brett.
With strong cast and good script, ALIEN also benefited from very good director. Ridley Scott, who was at his prime in late 1970s and early 1980s, showed great skill in creating very distinctive visual images. ALIEN perhaps doesn't look as spectacular as his next masterpiece, BLADE RUNNER, but the thick atmosphere is here, with elaborate and extremely detailed production and costume design. Special effects are still impressive and very convincing, even after two decades of science fiction movies that used superior techniques. The disturbing feel of the movie is also enhanced by haunting score written by veteran film composer Jerry Goldsmith, one of his best. Scott is also good in editing, when he manages to create some of the most shocking scenes in the history of cinema. But, ALIEN functions wonderfully as whole, not just like a mechanical sum of all those good elements. The film has a relatively slow pace in the first half, when Scott allows us to meet and study characters and claustrophobic environment they inhabit. Then the plot thickens and second half is a frightening thrill-ride. Unlike many less talented horror directors of that era, Scott doesn't put emphasis on gore (although the legendary dining scene has a plenty of it) and tries to scare the viewers by creating haunting atmosphere and implying terrible events instead.
The fear and anxiety that viewers share with the movie protagonists made ALIEN one of the most successful horror films, if not the best horror film of all times. Fact that it created numerous imitations and spawned three sequels so far speaks enough of its quality and importance. Yet the best recommendation for this film is the multitude of fans that watch this movie time and time again, re-experiencing same, often not so pleasant emotions, yet enjoying the rarely good work of art when they see one.
RATING: 10/10 (+++++)
Review written on October 23rd 1998
Dragan Antulov a.k.a. Drax Fido: 2:381/100 E-Mail<EMAIL_ADDRESS><EMAIL_ADDRESS>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
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This report is not about me, but about a 16-year-old patient I took care of in an intensive care unit.
History that I obtained from the patient's family started with the boy's father coming home from work and seeing his son sitting on the couch watching a TV that wasn't on and talking to a friend that wasn't there.
 At this point his son looked around the room and had what his father said was his last moment of clarity.  Dad figured that the boy had taken LSD and sent the boy to his room to trip. Dad found a pot on the stove that had some burnt material in and the pot had boiled dry and was smoking.
Within 15 minutes of getting home, the father heard his son screaming at the top of his lungs 'like a girl' and then the sound of breaking glass as the boy slammed his computer chair into his window. At some point, Dad decides that this isn't your regular bad trip and drives the boy to our hospital.
When he came in, his heart rate was 178. We pushed drugs to slow his heart rate down. His pupils were dilated to 7mm. He was screaming and thrashing around. We had to call in 4 of our security officers to do a use of force and place the boy in four point restraints, where a patient is strapped to a bed by their wrists and ankles. The patient was writhing so hard on the bed that he was arching his back and coming up off the bed. It was an awful lot like the scene from the Exorcist. Everyone panics and we can't give the patient something to safely sedate him until you have some idea what he's on.
His father called in the family and a friend of the patient, who had some idea what the boy had taken. He told us that his friend had read about 'gypsum weed' on the internet and had made a tea out of it to get high. Due to the nature of the plant and how he took it, it is impossible for us to say how much he took.
There was really nothing we could do for this boy to help him while he was in the ICU. These hallucinations are not like the ones users get on LSD or other drugs. They cannot differentiate between the trip and the reality.
When the boy awoke on the 3rd day of his stay with us, his family had been through turmoil, and he was unable to walk or eat for another 2 days. At discharge, his total bill was over $140,000 for the skilled care that he had required. He was embarrassed, physically and mentally weakened, and had mandatory chemical dependency treatment (and a severe chewing out by his Dad) to look forward to.
I can say I'll probably always be able to recognize a Datura patient if I ever see another one.
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.


Babe: Pig in the City
Babe: Pig in the City is a movie starring Magda Szubanski, Elizabeth Daily, and Mickey Rooney. Babe, fresh from his victory in the sheepherding contest, returns to Farmer Hoggett's farm, but after Farmer Hoggett is injured and...


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RegExpInitialize ( _obj_, _pattern_, _flags_ )
The abstract operation RegExpInitialize takes arguments _obj_, _pattern_, and _flags_. It performs the following steps when called:
1. If _pattern_ is *undefined*, let _P_ be the empty String.
1. Else, let _P_ be ? ToString(_pattern_).
1. If _flags_ is *undefined*, let _F_ be the empty String.
1. Else, let _F_ be ? ToString(_flags_).
1. If _F_ contains any code unit other than *"d"*, *"g"*, *"i"*, *"m"*, *"s"*, *"u"*, or *"y"* or if it contains the same code unit more than once, throw a *SyntaxError* exception.
1. If _F_ contains *"u"*, let _u_ be *true*; else let _u_ be *false*.
1. If _u_ is *true*, then
1. Let _patternText_ be ! StringToCodePoints(_P_).
1. Let _patternCharacters_ be a List whose elements are the code points of _patternText_.
1. Else,
1. Let _patternText_ be the result of interpreting each of _P_'s 16-bit elements as a Unicode BMP code point. UTF-16 decoding is not applied to the elements.
1. Let _patternCharacters_ be a List whose elements are the code unit elements of _P_.
1. Let _parseResult_ be ParsePattern(_patternText_, _u_).
1. If _parseResult_ is a non-empty List of *SyntaxError* objects, throw a *SyntaxError* exception.
1. Assert: _parseResult_ is a Parse Node for |Pattern|.
1. Set _obj_.[[OriginalSource]] to _P_.
1. Set _obj_.[[OriginalFlags]] to _F_.
1. Set _obj_.[[RegExpMatcher]] to the Abstract Closure that evaluates _parseResult_ by applying the semantics provided in using _patternCharacters_ as the pattern's List of |SourceCharacter| values and _F_ as the flag parameters.
1. Perform ? Set(_obj_, *"lastIndex"*, *+0*𝔽, *true*).
1. Return _obj_.


If set to true, GraphGL requests will include some additional data to audit log in the server side.
[Modifier]
An implementation of AuthContextProvider. If none passed, a default one will be used. The default one doesn't add any headers.
Interface that can be implemented to receive information about the data conflict
[Modifier]
Interface that defines how object state is progressed This interface needs to match state provider supplied on server.
[Modifier]
The conflict resolution strategy your client should use. By default it takes client version.
[Modifier]
If set to true, GraphGL file uploads will be enabled and supported
The URL of http server
[Modifier]
Cache updates functions for your mutations Argument allows to restore optimistic responses on application restarts.
[Modifier]
Interface for detecting changes in network status.
See WebNetworkStatus
and CordovaNetworkStatus
User provided listener that contains set of methods that can be used to detect when operations were added to queue
OpenShift specific configuration that provides alternative way to setup http and websocket urls.
[Modifier]
The options to configure how failed offline mutations are retried.
[Modifier]
The storage you want your client to use (Uses window.localStorage by default)
The URL of websocket endpoint
Generated using TypeDoc
Contains all configuration options required to initialize Voyager Client Options marked with [Modifier] flag are used to modify behavior of client. SDK provides default values for all [Modifier] flags. Users do not need to pass them for normal initialization of the client. Please refer to documentation for more information about the individual flag and it's side effects.
DefaultOptions for defaults


In this Bite you will swap case all pybites characters (both lower- and upper case) for a given text.
Not much more as for our instruction, just complete convert_pybites_chars
which should work like this:
>>> from convert_chars import convert_pybites_chars >>> text = "Today we added TWO NEW Bites to our Platform, exciting!" >>> convert_pybites_chars(text) 'todaY wE addEd tWO NeW bITES To our plaTform, ExcITIng!'
(Note that all converted chars are in the string pybites)
Have fun and code more Python!


Visual Studio 2019 for Mac version 8.4 Preview 2, now available | Visual Studio Blog Today we released the latest preview, Preview 2, of Visual Studio 2019 for Mac version 8.4. This preview comes with several exciting new features which we would love for you to try out. To get the preview: If you haven’t already,
ASP.NET Blog | ASP.NET Core updates in .NET Core 3.1 Preview 2 .NET Core 3.1 Preview 2 is now available. This release is primarily focused on bug fixes, but it contains a few new features as well. Here’s what’s new in this release for ASP.NET Core: New component tag helper Prevent default actions for events in Blazo...
Your OneNote From your flashes of inspiration at 2:00 AM to the list of funny things your children say, or that brilliant idea you had in the conference room, and your ever-growing list of household chores — OneNote holds the notes to your life to track all the things...
bye: FTP Support Is Going Away Support for the venerable FTP protocol is being removed from Chromium. Standardized in 1971, FTP is not a safe protocol for the modern internet. Its primary defect is lack of support for encryption…
Visual Studio Online - Cloud IDE - Code Editor Online Develop from anywhere with a cloud-powered development environment. Take advantage of real-time collaboration on any device for any project.
Announcing Visual Studio Online Public Preview | Visual Studio Blog Announcing Visual Studio Online's public preview! VS Online provides managed, on-demand development environments that can be used for long-term projects, or for short-term tasks like reviewing pull requests. You can work with environments from VS Code or ...
Announcing .NET Core 3.1 Preview 2 | .NET Blog Today, we’re announcing .NET Core 3.1 Preview 2. .NET Core 3.1 will be a small and short release focused on key improvements in Blazor and Windows desktop, the two big additions in .NET Core 3.0.. It will be a long term support (LTS) release with an expec...
Flush Cache | Public DNS | Google Developers Tip: If you see “Some servers did not respond to flush requests,” wait a minute or two before you try again. This prevents your request from being blocked due to rate limits on flush requests, so that your second or third try is more likely to succeed.
ADefWebserver/FatSecretDataExporter Allows users of FatSecret.com to export their data - ADefWebserver/FatSecretDataExporter
Modern day C# development in Visual Studio 2019 - Kevin Pilch In this demo-filled session from a Microsoft engineer who has worked on VS for over 15 years, you'll get the inside story on improvements across the board: from performance improvements in startup, and solution load, to editing, code style, quick fixes, r...
Minimal Viable Programs -
Joe Armstrong - Erlang and other stuff
 A minimal viable program is the smallest program that solves a particular problem. It is small and beautiful. It has no additional features.
Jon Galloway! | Jesse Liberty Microsoft’s Jon Galloway talks about the .NET Foundation and about .NET Core. As always with him, an amazing conversation.
Audio Fingerprinting using the AudioContext API Audio fingerprinting takes advantage of device performance specs to build up an identifying fingerprint of a user. It uses the AudioContext API for this


Note:all food banks listed are open.
1. Teen Challenge, offers cooked food. opens: Tuesday 9-10pm.
2. St. Leonards church, offers cooked meals. opens: Tuesday 2-4pm and Wednesday.
3. TAB Centre, offers Mixed Cooked And Cold Food.opens: Thursday 8-10pm Friday 9am-12pm.
4.Hackney Foodbank, offers: Non Perishable/Food Parcelopens: Monday 11am-1:45pm in Hoxton, Thursday 12:00pm-2:15pm in Stoke Newington.


# The office hour will start in 5 minutes.
# During the office hour, you can clarify and ask questions about course content.
# You can also talk about problems regarding your reading, code, etc.
# I am glad to explain and help. Let us begin soon!
A quantifier metacharacter immediately follows a portion of a
import re
test_string = "What stores are open (Labor Day)? (Lowe's, Walmart, Target, \
Kohl's and JCPenney) are, but (Costco) is closed Monday."
# re.findall
# re.finditer
re.findall('\w+[t]', test_string)
['What', 'st', 'Walmart', 'Target', 'but', 'Cost']
for item in re.finditer('\w+[t]', test_string):
print(item.group())
What st Walmart Target but Cost
re.findall('([A-Z])([\w]*)', test_string)
[('W', 'hat'), ('L', 'abor'), ('D', 'ay'), ('L', 'owe'), ('W', 'almart'), ('T', 'arget'), ('K', 'ohl'), ('J', 'CPenney'), ('C', 'ostco'), ('M', 'onday')]
for item in re.finditer('([A-Z])([\w]*)', test_string):
print(item.group(0))
What Labor Day Lowe Walmart Target Kohl JCPenney Costco Monday
The backslash \ removes the special meaning of a metacharacter.
print(r"In Son Zeng\n")
In Son Zeng\n
print("In Son Zeng\n")
In Son Zeng
To capture the parenthetical text, we’ll use some special regex, so the first step is to describe the pattern for which we are searching:
text after an opening paren
text before a closing paren
“text” can be any character
# We can specify a look-behind characters in the following pattern: (?<= <Pattern>)
look_behind = "(?<=\()
# We can specify a look-behind characters in the following pattern: (?= <Pattern>)
look_ahead = "(?=\))"
# We will use a . here because in regular expressions, the period ( . , also called "dot")
# is the wildcard pattern which matches any single character.
pattern = "(?<=\()(.+)(?=\))"
for item in re.finditer(pattern, test_string):
print(item.group())
Labor Day)? (Lowe's, Walmart, Target, Kohl's and JCPenney) are, but (Costco
pattern = "(?<=\()(.+?)(?=\))"
for item in re.finditer(pattern, test_string):
print(item.group())
Labor Day Lowe's, Walmart, Target, Kohl's and JCPenney Costco
By default, regular expressions are greedy, meaning they will match as much data as possible whenever it satisfies the pattern condition.
A non-greedy match means that the regex engine matches as few characters as possible whenever it can match the pattern in the given string.
To make the quantifier non-greedy you will follow the pattern with a '?'
test_string = "What are the 5 most popular places in Ann Arbor, Michigan? They are (Michigan Stadium), \
(University of Michigan), (Matthaei Botanical Gardens), (Law Quadrangle) and (Gallup Park)."
pattern = "(?<=\()(.+)(?=\))"
for item in re.finditer(pattern, test_string):
print(item.group())
Michigan Stadium), (University of Michigan), (Matthaei Botanical Gardens), (Law Quadrangle) and (Gallup Park
pattern = "(?<=\()(.+?)(?=\))"
for item in re.finditer(pattern, test_string):
print(item.group())
Michigan Stadium University of Michigan Matthaei Botanical Gardens Law Quadrangle Gallup Park
re.findall(pattern, test_string)
['Michigan Stadium', 'University of Michigan', 'Matthaei Botanical Gardens', 'Law Quadrangle', 'Gallup Park']
import pandas as pd
cdc_2017_data = pd.read_csv('NISPUF17.csv', index_col = 0)
cdc_2017_data.head()
| SEQNUMC | SEQNUMHH | PDAT | PROVWT_D | RDDWT_D | STRATUM | YEAR | AGECPOXR | HAD_CPOX | AGEGRP | ... | XVRCTY2 | XVRCTY3 | XVRCTY4 | XVRCTY5 | XVRCTY6 | XVRCTY7 | XVRCTY8 | XVRCTY9 | INS_STAT2_I | INS_BREAK_I | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 128521 | 12852 | 2 | NaN | 235.916956 | 1031 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 2 | 10741 | 1074 | 2 | NaN | 957.353840 | 1068 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 3 | 220011 | 22001 | 2 | NaN | 189.611299 | 1050 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 4 | 86131 | 8613 | 1 | 675.430817 | 333.447418 | 1040 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 5 | 227141 | 22714 | 1 | 482.617748 | 278.768063 | 1008 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 1.0 |
5 rows × 453 columns
cdc_2017_data['HAD_CPOX'].unique()
array([ 2, 1, 77, 99])
cdc_2017_data[(cdc_2017_data['P_NUMVRC'] == 1.0) | (cdc_2017_data['P_NUMVRC'] == 2.0) | (cdc_2017_data['P_NUMVRC'] == 3.0)]
| SEQNUMC | SEQNUMHH | PDAT | PROVWT_D | RDDWT_D | STRATUM | YEAR | AGECPOXR | HAD_CPOX | AGEGRP | ... | XVRCTY2 | XVRCTY3 | XVRCTY4 | XVRCTY5 | XVRCTY6 | XVRCTY7 | XVRCTY8 | XVRCTY9 | INS_STAT2_I | INS_BREAK_I | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 86131 | 8613 | 1 | 675.430817 | 333.447418 | 1040 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 7 | 221381 | 22138 | 1 | 516.427723 | 248.916038 | 1008 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 11 | 128211 | 12821 | 1 | 335.398714 | 198.123294 | 1057 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 13 | 137041 | 13704 | 1 | 82.582116 | 45.544717 | 1051 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 17 | 188611 | 18861 | 1 | 345.352030 | 196.111859 | 1057 | 2017 | NaN | 2 | 1 | ... | VO | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 28447 | 193801 | 19380 | 1 | 321.058203 | 110.009181 | 2047 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | ||
| 28448 | 136841 | 13684 | 1 | 593.426753 | 337.255316 | 2031 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28450 | 132081 | 13208 | 1 | 166.681363 | 96.330007 | 2059 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28453 | 82151 | 8215 | 1 | 335.456813 | 159.895243 | 2056 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28455 | 191451 | 19145 | 1 | 59.626969 | 29.187316 | 2074 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 2.0 |
13995 rows × 453 columns
# For example, we want to choose the observations where people received 1, 2 or 3 vaccines.
# We can create a list for the valid values
num_vaccines = [1.0, 2.0, 3.0]
# And then use isin() function to return a boolean True or False for value matching
cdc_2017_data.P_NUMVRC.isin(num_vaccines)
1 False 2 False 3 False 4 True 5 False ... 28461 False 28462 False 28463 False 28464 False 28465 False Name: P_NUMVRC, Length: 28465, dtype: bool
# Remember we can put the boolean condition inside the brackets [condition]
# to select those who took 1 to 3 doses of varicella vaccines
cdc_2017_data[cdc_2017_data.P_NUMVRC.isin(num_vaccines)]
| SEQNUMC | SEQNUMHH | PDAT | PROVWT_D | RDDWT_D | STRATUM | YEAR | AGECPOXR | HAD_CPOX | AGEGRP | ... | XVRCTY2 | XVRCTY3 | XVRCTY4 | XVRCTY5 | XVRCTY6 | XVRCTY7 | XVRCTY8 | XVRCTY9 | INS_STAT2_I | INS_BREAK_I | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 86131 | 8613 | 1 | 675.430817 | 333.447418 | 1040 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 7 | 221381 | 22138 | 1 | 516.427723 | 248.916038 | 1008 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 11 | 128211 | 12821 | 1 | 335.398714 | 198.123294 | 1057 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 13 | 137041 | 13704 | 1 | 82.582116 | 45.544717 | 1051 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 17 | 188611 | 18861 | 1 | 345.352030 | 196.111859 | 1057 | 2017 | NaN | 2 | 1 | ... | VO | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 28447 | 193801 | 19380 | 1 | 321.058203 | 110.009181 | 2047 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | ||
| 28448 | 136841 | 13684 | 1 | 593.426753 | 337.255316 | 2031 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28450 | 132081 | 13208 | 1 | 166.681363 | 96.330007 | 2059 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28453 | 82151 | 8215 | 1 | 335.456813 | 159.895243 | 2056 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28455 | 191451 | 19145 | 1 | 59.626969 | 29.187316 | 2074 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 2.0 |
13995 rows × 453 columns
# Student solution: We can use a ~ (tilda) to specify a negation in the filtering condition
cdc_2017_data[~cdc_2017_data.P_NUMVRC.isin(num_vaccines)]
| SEQNUMC | SEQNUMHH | PDAT | PROVWT_D | RDDWT_D | STRATUM | YEAR | AGECPOXR | HAD_CPOX | AGEGRP | ... | XVRCTY2 | XVRCTY3 | XVRCTY4 | XVRCTY5 | XVRCTY6 | XVRCTY7 | XVRCTY8 | XVRCTY9 | INS_STAT2_I | INS_BREAK_I | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 128521 | 12852 | 2 | NaN | 235.916956 | 1031 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 2 | 10741 | 1074 | 2 | NaN | 957.353840 | 1068 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 3 | 220011 | 22001 | 2 | NaN | 189.611299 | 1050 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 5 | 227141 | 22714 | 1 | 482.617748 | 278.768063 | 1008 | 2017 | NaN | 2 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 1.0 | ||
| 6 | 207941 | 20794 | 2 | NaN | 35.995367 | 1019 | 2017 | NaN | 2 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 28461 | 19141 | 1914 | 2 | NaN | 21.188088 | 2062 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 28462 | 121401 | 12140 | 2 | NaN | 26.503010 | 2004 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 28463 | 128831 | 12883 | 2 | NaN | 27.520652 | 2004 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 28464 | 82921 | 8292 | 2 | NaN | 22.390587 | 2062 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 28465 | 244621 | 24462 | 2 | NaN | 27.520652 | 2004 | 2017 | NaN | 2 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
14470 rows × 453 columns
cdc_2017_data.P_NUMVRC.unique()
array([nan, 1., 0., 2., 3.])
# Student solution 1: We can use paranthesis to enclose each condition, then add a logical operator
# & when all conditions should be satisfied, or | (pipe) when either condition should be satisfied
L = [0, 1.0, 2.0]
cdc_2017_data[(cdc_2017_data['HAD_CPOX']==1) & (cdc_2017_data['P_NUMVRC'].isin(L))]
| SEQNUMC | SEQNUMHH | PDAT | PROVWT_D | RDDWT_D | STRATUM | YEAR | AGECPOXR | HAD_CPOX | AGEGRP | ... | XVRCTY2 | XVRCTY3 | XVRCTY4 | XVRCTY5 | XVRCTY6 | XVRCTY7 | XVRCTY8 | XVRCTY9 | INS_STAT2_I | INS_BREAK_I | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 161121 | 16112 | 1 | 978.042448 | 718.616990 | 1016 | 2017 | 3.0 | 1 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 4.0 | ||
| 462 | 255961 | 25596 | 1 | 523.277248 | 275.034692 | 1002 | 2017 | 3.0 | 1 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | ||
| 613 | 58421 | 5842 | 1 | 449.563808 | 449.563808 | 1040 | 2017 | 4.0 | 1 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | ||
| 688 | 72351 | 7235 | 1 | 1849.038009 | 466.803981 | 2011 | 2017 | 3.0 | 1 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 722 | 176421 | 17642 | 1 | 1643.816096 | 665.365790 | 2011 | 2017 | 2.0 | 1 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 27808 | 75011 | 7501 | 1 | 220.272839 | 114.949177 | 2036 | 2017 | 2.0 | 1 | 2 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | ||
| 28107 | 53322 | 5332 | 1 | 30.347802 | 25.141769 | 2058 | 2017 | 4.0 | 1 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28108 | 53321 | 5332 | 1 | 36.803372 | 27.792652 | 2058 | 2017 | 3.0 | 1 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28315 | 233711 | 23371 | 1 | 22.125887 | 13.888073 | 2007 | 2017 | 3.0 | 1 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 2.0 | ||
| 28405 | 177661 | 17766 | 1 | 128.133719 | 105.089644 | 2020 | 2017 | 1.0 | 1 | 3 | ... | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 |
173 rows × 453 columns
#df[(~df['num vacc'] == 3) & (df[had vacc]) == 1)]
mean_vaccine = cdc_2017_data[(cdc_2017_data['P_NUMVRC'] !=3) & (cdc_2017_data['HAD_CPOX'] ==1)]['P_NUMVRC'].mean()
Short answers:
# Impute missing values by 0
cdc_2017_data['P_NUMVRC'].fillna(0).head()
#mean_vaccine
1 0.0 2 0.0 3 0.0 4 1.0 5 0.0 Name: P_NUMVRC, dtype: float64
# Impute missing values by mean
cdc_2017_data['P_NUMVRC'].fillna(mean_vaccine).head()
1 0.722543 2 0.722543 3 0.722543 4 1.000000 5 0.000000 Name: P_NUMVRC, dtype: float64
# methods inside fillna function
# Forward filling: Fill every NAs with the previous valid values
cdc_2017_data['P_NUMVRC'].fillna(method = 'ffill')#.isna().sum()
1 NaN 2 NaN 3 NaN 4 1.0 5 0.0 ... 28461 1.0 28462 1.0 28463 1.0 28464 1.0 28465 1.0 Name: P_NUMVRC, Length: 28465, dtype: float64
# Backward filling: Fill every NAs with the next valid values
%%timeit -n 5
cdc_2017_data['P_NUMVRC'].fillna(method= 'bfill')#.fillna(method = 'ffill').isna().sum()
UsageError: Line magic function `%%timeit` not found.
cdc_2017_data['P_NUMVRC'].bfill().isna().sum()
10
vaccines = ['P_NUMDTP', 'P_NUMVRC', 'EDUC1']
cdc_2017_data[vaccines]
| P_NUMDTP | P_NUMVRC | EDUC1 | |
|---|---|---|---|
| 1 | NaN | NaN | 4 |
| 2 | NaN | NaN | 3 |
| 3 | NaN | NaN | 3 |
| 4 | 4.0 | 1.0 | 4 |
| 5 | 0.0 | 0.0 | 1 |
| ... | ... | ... | ... |
| 28461 | NaN | NaN | 3 |
| 28462 | NaN | NaN | 2 |
| 28463 | NaN | NaN | 3 |
| 28464 | NaN | NaN | 2 |
| 28465 | NaN | NaN | 4 |
28465 rows × 3 columns
# The office hour will start in 5 minutes.
# During the office hour, you can clarify and ask questions about course content.
# You can also talk about problems regarding your reading, code, etc.
# I am glad to explain and help. Let us begin soon!
# We can use help function to see what package and dependencies of re.findall locates at
# We can see what arguments re.findall takes and say it returns a list of matched patterns
help(re.findall)
Help on function findall in module re: findall(pattern, string, flags=0) Return a list of all non-overlapping matches in the string. If one or more capturing groups are present in the pattern, return a list of groups; this will be a list of tuples if the pattern has more than one group. Empty matches are included in the result.
# We can also see that the class Series is stored under the module pandas.core.series
# It takes some data, index, and options such as datatype, name, etc.
# And Series object is an one-dimensional ndarray with axis labels
help(pd.Series)


In our exam project, we have developed an interactive visualization tool of dynamic graph data to be used by researchers at SODAS in order to make more informed design decisions in their research on dynamic social networks among students at DTU.
The Social Fabric research project has collected two years of behavioral data from 1000 DTU students, who started their studies in the fall 2013 and was followed until the summer 2015. The behavioral data was collected through handed-out smart phones. Among other things, the phone of each participant was set to make a bluetooth scan every 5 minute, and if the phone of another participant was registered, the timestamp and rssi signal strength of this bluetooth link between the two participants can now be found in a dataset like this:
| user | user2 | ts | rssi |
|---|---|---|---|
| 780 | 325 | 4.0 | -93 |
| 600 | 632 | 9.0 | -97 |
| 47 | 448 | 12.0 | -97 |
| 461 | 492 | 13.0 | -87 |
Some of the research projects at SODAS try to use this bluetooth scan data to study questions related to the dynamic social networks between the participants. To do this, they first use the data to build a discrete time sequence of graphs, whereafter they use this graph sequence as input to, for instance, algorithms for detecting dynamic community structures. The output of these algorithms is then used to explore different social network related research questions. Each graph in the graph sequence has all the participants in the experiment as nodes, and links somehow defined from the bluetooth scan dataset, as it is seen above. Our visualization tool will hopefully help the researchers inform their decisions on how to construct the links between the participants from the bluetooth scans.
A quite simple construction is to have the researchers choose the value of three parameters, namely the amount of time t of each timebin, a threshold signal strength x and some number of scans n. Given the values of t, x and n, the graph sequence will be constructed as follows:


Class
AnnotationStoreElementRepresents an annotation of a genomic feature.
| Defined in | <seqan/store.h> |
|---|---|
| Signature |
template <typename TPos[, typename TSpec]>
struct AnnotationStoreElement;
|
Member Function Overview
-
AnnotationStoreElement::AnnotationStoreElement();
Constructor.
Member Typedef Overview
-
typedef (...) TAnnotationStoreElement::TId;
Type of annotationId and contigId. -
typedef (...) TAnnotationStoreElement::TPos;
-
typedef (...) TAnnotationStoreElement::TValues;
Member Variable Overview
-
TPos AnnotationStoreElement::beginPos
Begin position of the annotation in gap space. -
TId AnnotationStoreElement::contigId
Refers to the contig in contigStore that the annotation is part of. -
TPos AnnotationStoreElement::endPos
End position of the annotation in gap space. -
TId AnnotationStoreElement::INVALID_ID
Constant to represent an invalid id. -
TId AnnotationStoreElement::INVALID_POS
Constant to represent an invalid position. -
TId AnnotationStoreElement::lastChildId
The id of the parent annotation. -
TId AnnotationStoreElement::nextSiblingId
The id of the right sibling annotation. -
TId AnnotationStoreElement::parentId
The id of the parent annotation. -
TId AnnotationStoreElement::typeId
Refers to an entry in the annotationTypeStore. -
TValues AnnotationStoreElement::values
StringSet that stores additional values addressed by keyId.
Detailed Description
Value type of the annotationStore stirng. The id of an annotation (aka annotationId) is not stored explicitly as it is implicitly given byt he position in the annotationStore.
Member Functions Detail
AnnotationStoreElement::AnnotationStoreElement();
The default constructor sets all members to INVALID_ID and beginPos and endPos to INVALID_POS.
Data Races
Member Typedef Detail
typedef (...) TAnnotationStoreElement::TId;
TId is the result of Id<AnnotationStoreElement<> >::Type, see Id.
typedef (...) TAnnotationStoreElement::TPos;
typedef (...) TAnnotationStoreElement::TValues;
Member Variables Detail
TPos AnnotationStoreElement::beginPos
If endPos < beginPos then the annotation feature is located on the reverse strand where beginPos and endPos are the corresponding positions on the forward strand.
TId AnnotationStoreElement::contigId
TPos AnnotationStoreElement::endPos
If endPos < beginPos then the annotation feature is located on the reverse strand where beginPos and endPos are the corresponding positions on the forward strand.
TId AnnotationStoreElement::INVALID_ID
TId AnnotationStoreElement::INVALID_POS
TId AnnotationStoreElement::lastChildId
TId AnnotationStoreElement::nextSiblingId
TId AnnotationStoreElement::parentId
TId AnnotationStoreElement::typeId
There are some type ids predefined for commonly used types, e.g. $ANNO_GENE$. See FragmentStore::PredefinedAnnotationTypes.
TValues AnnotationStoreElement::values
The GFF and FTF file formats allow to define user-specific key-value pairs. The set of all keys addressed by keyId are stored in annotationKeyStore.


Photo credit: @jwcarrol
“Frameworks are an abstraction. They make a subset of tasks easier to achieve, in a way that's familiar to other users of the framework. The cost is performance – the overhead of the framework. The key is making the cost worth it.” - Jake Archibald
| Technology | Positions | Stars |
|---|---|---|
| React | 511 | 81,042 |
| Angular | 257 | 30,000 |
| Vuejs | 68 | 73,650 |
| Polymer | 17 | 18,717 |
| Ember | 52 | 18,423 |
1. Draw out your structure with pen and paper or ...
2. Write your html first WRITE YOUR HTML FIRST
3. Github and Stackoverflow
4. TeamWork
“Most good programmers do programming not because they expect to get paid or get adulation by the public, but because it is fun to program.” - Linus Torvalds
<picture-frame>

</picture-frame>
<link rel=”import” href="https://polygit2.appspot.com/components/polymer/polymer.html">
<dom-module id=”picture-frame”>
<template>

Hello VarnaLab

<content></content>
</template>

</dom-module>
Photo credit: Polymer project
Photo credit: Polymer summit 2016
// https://w3c.github.io/webcomponents/spec/custom/
// https://www.html5rocks.com/en/tutorials/webcomponents/customelements/
class RadioButton extends HTMLElement {
constructor() {
super();
}
connectedCallback() {
this.setAttribute('role', 'radio');
this.setAttribute('tabindex', 0);
this.setAttribute('aria-checked', false);
}
}
// define method has three properties: 1. tag name, 2. Class Name, 3. options
window.customElements.define('radio-button', RadioButton, { 'extends': 'radio' });
2014 - Polymer 0.5 (Developer preview)
2015 - Polymer 1.0
2016 - Polymer 2.0
2017 - Polymer 3.0


We propose an imperative sparse-MVS leaderboard and call for the community’s attention on the general sparse MVS problem with large range of baseline angles. The proposed sparse-MVS leader-board is built on the large-scale DTU dataset and Tanks-and-Temples dataset with sparsely sampled camera views. The sparse-MVS setting selects one view from every n consecutive camera index, i.e., {1, n + 1, 2n + 1,...}, where n is termed as Sparsity positively related with baseline angle θ. The poor performance of the state-of-the-art MVS algorithms on the proposed leader-board demonstrates the necessity of further effort and attention from the community on the achieving MVS with various degrees of sparsity.
If you find the Sparse-MVS benchmark useful, please consider citing
@ARTICLE{ji2020surfacenet_plus, title={SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis}, author={M. {Ji} and J. {Zhang} and Q. {Dai} and L. {Fang}}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2020}, volume={}, number={}, pages={1-1}, }
The experiment settings and evaluation methods is in description. To submit the results, check the instructions in submission. The leaderboard and the data visualization are shown in leaderboard.


|
SUBJECT INDEX Anthropology Architecture Art Black Studies Business Ecology Economics Education Film Healthcare History Law Linguistics Literature Mass Media Medicine Music Philosophy Political Science Psychology Religion Science Shakespeare Sociology Sports Recreation Urban Studies Women's Studies |
Political Science: RussiaTo order a specific paper, click the Catalog Number button next to the Paper Title of interest. Send comments to<EMAIL_ADDRESS>Last Updated: 12/21/96 Copyright ©1996 ASM Communications, Inc. All Rights Reserved. |


After querying a subset of the full summary statistics, this function converts it into a standardized format that the rest of echolocatoR can work with.
standardize_subset( locus, top_SNPs = NULL, fullSS_genome_build = "hg19", subset_path = "./Data", chrom_col = "CHR", position_col = "POS", snp_col = "SNP", pval_col = "P", effect_col = "Effect", stderr_col = "StdErr", tstat_col = "t_stat", MAF_col = "MAF", freq_col = "Freq", N_cases_col = "N_cases", N_controls_col = "N_controls", N_cases = NULL, N_controls = NULL, proportion_cases = "calculate", sample_size = NULL, A1_col = "A1", A2_col = "A2", gene_col = "Gene", QTL_prefixes = NULL, return_dt = T, nThread = 4, download_method = "axel", verbose = T )
| top_SNPs | A data.frame with the genomic coordinates of the lead SNP for each locus.
The lead SNP will be used as the center of the window when extracting subset from the full GWAS/QTL summary statistics file.
Only one SNP per Locus should be included.
At minimum,
|
|---|---|
| chrom_col | Name of the chromosome column in the full summary stats file. Can be "chr1" or "1" format. (default: ="CHR") |
| position_col | Name of the genomic position column in the full summary stats file. Must be in units of basepairs. (default: ="POS") |
| snp_col | Name of the SNP RSID column in the full summary stats file. (default: ="SNP") |
| pval_col | Name of the p-value column in the full summary stats file. Raw p-values are preferred, but if not available corrected p-values (e.g. FDR) can be used instead. (default: ="P") |
| effect_col | Name of the effect size column in the full summary stats file. Effect size is preferred, but if not available other metrics like Beta for Odds Ratio can be used instead. (default: ="Effect") |
| stderr_col | Name of the standard error column in the full summary stats file.
You can also set |
| tstat_col | Name of the t-statistic column in the full summary stats file.
This column is not necessary unless |
| MAF_col | Name of the minor allele frequency column in the full summary stats file. Can be inferred from freq_col if missing from the dataset. (default: ="MAF") |
| freq_col | Name of the allele frequency column in the full summary stats file.
Effect allele frequency is preferred, but the non-effect allele can be provided instead (though this may be less accurate).
This column is not necessary unless |
| N_cases_col | Name of the column in the full summary stats that has the number of case subjects in the study.
This can either be per SNP sample sizes, or one number repeated across all rows.
Proxy cases (e.g. relatives of people with the disease being investigated) should be included in this estimate if any were used in the study.
This column is not necesssary if |
| N_controls_col | Name of the column in the full summary stats that has the number of control subjects in the study.
This can either be per SNP sample sizes, or one number repeated across all rows.
This column is not necesssary if |
| N_cases | The number of case subjects in the study. Instead of providing a redundant N_cases_col column, you can simply enter one value here. |
| N_controls | The number of control subjects in the study. Instead of providing a redundant N_controls_col column, you can simply enter one value here. |
| proportion_cases | The proportion of total subjects in the study that were cases.
if |
| sample_size | The overall sample size of the study.
If none is given, and N_cases and N_controls columns are present,
then sample_size is inferred to be: |
| A1_col | Name of the effect/risk allele column in the full summary stats. IMPORTANT: Make sure this actually the case for your full summary stats file. Unfortunately, different studies report different kinds of allele information in a non-standardized way. Meaning that A1/A2 can refer to any number of things:
This makes comparing summary stats across GWAS/QTL datasets very confusing for several reasons:
This process of reversing per-SNP valences based on aligning the alleles is known as allele flipping. This is important when comparing individual SNPs, but can also have an impact on colocalization results. |
| gene_col | For QTL studies, the name of the [e]gene column in the full summary stats file (default: "gene").
This column will be used for filtering summary stats if supplying a named list of gene:Locus pairs to |
| verbose | Whether echolocatoR should be verbose or silent. |
Other standardizing functions:
get_UKB_MAF()


{% extends "webca/web/base.html" %} {% load widget_tweaks %} {% block content %}
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The name of the user that will be connecting to TFS/VSO to query history, checkout files, etc.
Team Foundation Server (on-premises)
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For Visual Studio Online, there are also two options:
- Personal access tokens (recommended)
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Click [Create Token] and copy the generated personal access token to the clipboard.
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