Summer Film Reviews A film review by Stephen J. Truog Copyright 1994 Stephen J. Truog
Well, since I see everyone else writing their reviews, I guess I'll add my Midwestern perspective to the list since I saw an awful lot of movies this summer (for me--at least). So here are the summer reviews (in more or less descending order) according to SJT:
SPEED (****+) ============= This movie surprised and awed me. It surprised me because I am far from Keanu Reaves's biggest fan and I usually detest the intellectual level of action films. However, it awed me because it was able to overcome all of my preliminary judgements and thoroughly entertain me. This was a movie that I was strapped into my seat to and could not move or breathe until it was over. It brilliantly strung together three tense and gripping action sequences along with a decent story. Dennis Hopper plays one of the most memorable villains in the movies this year with a roll full of hilarious one-liners and maniacal laughter. The humor is also spread throughout with Sandra Bullock and Reaves. The action scenes are magnified by tense and enthralling music as well. All in all, one of the best action movies ever made and the year's best entertainment.
THE LION KING (****+) ===================== Another Disney classic--far better than ALADDIN and even BEAUTY AND THE BEAST. The animation is incredible and the music is spellbinding--but that's to be expected from Disney. What makes this movie better is that, unlike ALADDIN where one character's voice made the movie, The Lion King provides a wide array of memorable and captivating characters. Kids can name at least seven or eight characters in this flick where as in ALADDIN or BEAUTY AND THE BEAST, there were fewer. The second thing is that this isn't a classic story like the other Disney movies. This is a relatively new tale and educates about the circle of life--i.e., it is more realistic than the fairy tales and is still every bit as entertaining. A true classic.
THE CLIENT (****) ================= This is, by far, the closest and best adaptation of Grisham's books. The three main characters in this story are spectacular. Susan Sarandon should (but probably won't) receive an Oscar nomination for a role that she played with passion and heart. Tommy Lee was every bit as entertaining as in the Fugitive, but the true star of this flick is young Brad Renfro. He is absolutely amazing as Mark Sway and it is unbelievable that this is his acting debut. The acting truly makes this movie special. The story is pretty good until the end, where (like most Grisham books) the story wears thin because it simply can not keep pace with the breakneck beginnings), but the characters save it. This is a rich adaptation with perfect casting. Grisham beats Clancy this year.
FORREST GUMP (****) =================== An outstanding movie with one flaw--it wears on a bit at the end. The running sequence could have been cut by 5 minutes and still been effective (when it got to the smiley face "Have a Nice Day" part, it went too far--I gagged). But overall, it is a wonderful recap of America's transition from the idealistic 50's into the "me first" 90's. Tom Hanks is good, but is overshadowed by Sinese as his lieutenant--who should win an Oscar for supporting actor. Again, great music and a wonderful job of weaving in America's main events in the declining road of history with the simple and innocent life of Forrest Gump. An original and brilliant movie.
THE MASK (****) =============== This was the second surprise of the summer (after SPEED) because, while I admired Carrey's work from the early "In Living Color" days, I found ACE VENTURA, PET DETECTIVE atrocious. THE MASK, however, had a much better story, music and supporting cast. No, it will not win any Oscars for acting or writing (but maybe for special effects), but it will make you laugh. The summer's funniest film.
IT COULD HAPPEN TO YOU (*** 1/2) ================================ A sorta-true fairy tale comes to life in this magical and sweet film. It's not as mushy as SLEEPLESS IN SEATTLE and makes more important points--such as you can simply enjoy a person's company as a companion. Nicholas Cage and Bridget Fonda are enchanting and match up perfectly. Rosie Perez is hilarious and wicked at the same time. It's too bad this film didn't do so well because it was a great one.
TRUE LIES (*** 1/2) =================== Another shock because I don't like Aaahnold much either. He had too many lines in this movie (i.e., more than one) and it took a major detour for the middle third of the film, yet it still came out well. The main reason: Tom Arnold. He was the jester in this film but was perfect as it--a true entertainer. Roseanne who? Jamie Lee Curtis also provides Aaahnold with his toughest female counterpart--although she doesn't hit full stride until the end. Oh yes, the part where the gun fell down the stairs and kills all hundred bad guys in the vicinity made me gag--but I still liked this film.
CLEAR AND PRESENT DANGER (***) ============================== A disappointment. This movie gets long, rambly and loses its focus. The ending is also a bit far fetched (I'm sure every top CIA desk man goes right into the danger zone and slugs it out with the bad guy). The action pales in comparison to PATRIOT GAMES and, unfortunately, so does the directing. Tom Clancy's core is there, and that along with Harrison Ford save most of this film, but the director tore this apart and added long-winded and pointless dribble between the basic skeleton of a story by Clancy. Some fine moments, but there is far too much in between.
I LOVE TROUBLE (*** or *--depending on end)
THE FLINTSTONES (** 1/2) ======================== This film has remarkable scenery and it's cute how they fit our modern contraptions (like a garbage disposal or airplane) into dino times--but it lacks acting and plot.  Kyle McLaughlin is the sole exception, turning in a devilish performance as the villain, but overall it was overhyped.
BLOWN AWAY (**) =============== This one was a dud. So much potential wasted. This movie started out well, with a good storyline and some fine actors. However, they soon became lost in a series of explosions that continued to decrease in point and effect.  Puhleeze!) and leaves the audience wondering--where did this great start of a movie go???
OTHER QUICK BYTES:
CITY SLICKERS II (***)--Not as funny or well written as the original, but Billy Crystal can still draw a laugh (unlike Eddie Murphy)
WHEN A MAN LOVES A WOMAN (*** 1/2)--A great movie and a superb acting job by Meg Ryan. If she doesn't get an Oscar nomination, then life's not fair.
FOUR WEDDINGS AND A FUNERAL (****)--A delightful and original movie that will have you in stitches and crying as well. A treat!
Now for the first Fall classic:
QUIZ SHOW (****)--This movie started a bit slow for me, but when it caught on, it was captivating. Ralph Fiennes should have another Oscar nomination coming his way and the film will also get nominated. John Turturro is humorous as the former champ who takes the dive and once you get by the annoying accent, Rob Morrow does a fine job as the investigator. Both may have supporting Oscar nominations. Maybe it's just that I'm a sucker for true stories on the screen, but I was thoroughly enthralled by this film. I did not carry as big of a "America loses its innocence" or "WASPs are evil" theme out of it as a sound message that TV is entertainment and business--all that matters is money and pleasing people. The last few lines of the movie are haunting and leave you thinking with a chilling sense of skepticism. Overall, a spectacular film. Richly presented with an outstanding cast and an important message.
- Stephen J. Truog Northwestern University
.
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


--- layout: project categories: projects client: Liftoff client_url: http://liftoff.io client_color: 7EBB22 video_title: "Dynamic Ads" video_id: 214577934 video_thumb: liftoff-dynamic-ads.png featured: true ---
Director — Cole Mueller
Producer — Vitascope Arts
Cinematographer — Derrick Cohan
1st AC — Will Turner
2nd AC — Chess Pettengill
Editor — Cole Mueller
VFX — Richard Cabrera


@section("templates.default.bodyindex")
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BufTab
Describes an array of Buffer objects (of all types, see Buffer_Type). All buffers in a BufTab have the same size and are created with the same attributes. For this reason, one can access the first Buffer in the BufTab to learn about the characteristics of all Buffers in the BufTab. Typical usage below to allocate an array of 10 basic Buffers of size 1KB (no error checking):
More...
Detailed Description
Describes an array of Buffer objects (of all types, see Buffer_Type). All buffers in a BufTab have the same size and are created with the same attributes. For this reason, one can access the first Buffer in the BufTab to learn about the characteristics of all Buffers in the BufTab. Typical usage below to allocate an array of 10 basic Buffers of size 1KB (no error checking):
Typedef Documentation
Handle through which to reference a BufTab.
Function Documentation
Creates a BufTab and the buffers belonging to it.
- Parameters:
-
| [in] | numBufs | Number of buffers to allocate. |
| [in] | size | Size in bytes of buffers to create. |
| [in] | attrs | Buffer_Attrs to use for creating the buffers in the BufTab. |
- Return values:
-
Deletes a BufTab.
- Parameters:
-
- Return values:
-
| Dmai_EOK | for success. |
| Negative value | for failure, see Dmai.h. |
| Int BufTab_chunk |
( |
BufTab_Handle |
hBufTab, |
|
|
Int |
numBufs, |
|
|
Int32 |
bufSize | |
|
) |
| | |
Chunk a BufTab in to smaller buffer sizes. This will not change the original allocation during creation, it merely tries to fit many smaller Buffers inside the already allocated Buffers.
Each buffer in a BufTab is chunked separately; i.e., if the initial buffer size is not an exact multiple of the new bufSize, the remaining space in the initial buffer becomes unused and does not count when chunking the next buffer in the BufTab.
- Parameters:
-
| [in] | hBufTab | The BufTab_Handle to chunk. |
| [in] | numBufs | The desired # of buffers to chunk the BufTab into |
| [in] | bufSize | The size in bytes of the buffers to chunk in to. |
- Return values:
-
| The | difference between numBufs and the actual number of buffers in the newly modified BufTab. 0 if the number of buffers matches numBufs. |
| Negative value | for failure, see Dmai.h. |
Restore the BufTab to the state before BufTab_chunk was called on it. Note that this will undo any calls to BufTab_expand after the BufTab_chunk call.
- Parameters:
-
- Return values:
-
| Dmai_EOK | for success. |
| Negative value | for failure, see Dmai.h. |
Expand a BufTab with more buffers.
- Parameters:
-
| [in] | hBufTab | The BufTab_Handle to expand. |
| [in] | numBufs | The number of buffers to expand the BufTab with. |
- Return values:
-
| Dmai_EOK | for success. |
| Negative value | for failure, see Dmai.h. |
Return a buffer with a certain index in the BufTab.
- Parameters:
-
| [in] | hBufTab | The BufTab_Handle to which the buffer belongs. |
| [in] | bufIdx | The index of the buffer in the BufTab. |
- Return values:
-
| Handle | to the buffer matching the supplied index (see BufTab_Handle). |
Get the number of Buffers in a BufTab.
- Parameters:
-
| [in] | hBufTab | The BufTab of which to get the number of buffers. |
- Return values:
-
| Number of buffers | in the BufTab. |
Print information about the buffers in the BufTab.
- Parameters:
-
Mark all buffers in a BufTab as free.
- Parameters:
-
Mark a Buffer as free in a BufTab.
- Parameters:
-
Return a free buffer from the BufTab and mark it as used.
- Parameters:
-
- Return values:
-


Our aim is to extinct the hunger from this planet
In India one third of the population suffers from the hunger and this is not only the problem of india also it is the problem of many developing countries like India Africa etc.
The End of Hunger is Here
Food scarcity, hunger, malnutrition is not just a challenge but it is a “War” that we can collectively win otherwise defeat leads in the loss of millions of lives. Food is our common ground, a universal experience, but do you know? Millions of people don’t even get to experience it. Approximately 25 lakh people and 5 lakh animals die due to hunger in India. Millions don’t even know whether their next meal is coming from and most of them are children. But the main issue is not hunger, it is the unwillingness of our own people to fight against it. This work is not difficult all it takes is little efforts from each individual.
Who are we?
We are more than an organization. We are a family fighting for survival of every member. Hunger has become an acute disease in India, but we aim to find solutions not excuses. Together we can bring this change if we believe in it. Our Vision We as a family seek support of our members, be it from India or any corner of the world. Our fight is to create a community that for a food sufficient world where nobody sleeps hungry, nobody dies of hunger. We aim to connect compassionate and willing people with the people/animals who are in need, also, to recycle and regulate the wastage of food. The Idea We tend to provide food to the needy ones as well as to end the wastage of food that takes place in nearly every household with brutal ignorance. Our idea is to collect the food, that would have been otherwise wasted, at some “Food Points” (That will be created in required localities depending upon the needs) and make it available to children, men, women, street animals. Fresh food that would have been wasted goes to people and leftover food (but still edible) goes to animals.
How It Works
It is very simple to become a Hunger Warrior with us. Your very willingness is enough for us. To create “food points” near you, you need to fill a basic form on our website with the eligibility that from your locality at least 5 people are willing to donate food (if not regular then very often at least). You will be registered on our website and here you'll get daily update of number of food recipients and needy ones near you to feed with the help of our very own AI classification model to enhance the accuracy of the upcoming demand for the food near you and able to predict the need of creating another food point at specific location with the help of google maps and location API's. Then also we enabled the method of donating the money to help in buying the goods like-Refrigerator, Disposable plates, water outlets and much more in a very transparent mode to ensure that your money is getting invested in the right place. In order to optimize the expenses, we'll create a polling system based on different locations (4 locations) of the greater need based on the Data and inputs that we are getting through various sources and the donated money well be spent in the priority order based on the polling. You can also register as an independent active volunteer where you don’t necessarily need 5+ people to donate but willing to provide 4 hours/week for serving people at food points.
Message for our Hunger Warriors
“Khao aur Khilao” (Eat and feed the ones in need)
We found compassion, we found love, and then we found you.
Thank you for bringing the change you want to see in the world and bringing smiles on millions of faces.
We are more than happy and grateful for serving you and humanity.
Contact Form


Stereotype threat refers to the experience of anxiety in a situation in which a person has the potential to confirm a negative stereotype about his or her social group. An example of this has been extensively researched by Claude Steele, in which he tested groups of mixed race teenagers using standardized tests that gave directions which highlighted negative stereotypes. The results of the exams showed a significant difference in test scores from the teenagers that took into account their race when taking the exams vs just taking the exam with no thought to their skin color.
I believe that all of us have experienced a stereotype threat in some way or another. I for sure have felt the pressure of my stereotype. I am a 22 year old girl. At this time in my life, all of my friends are either getting married, or getting crazy drunk every night of the week. At family events, everyone is constantly asking me about my love life or my social life or why I don't seem to have any of the above. How has this led to negative effects you may ask? I sometimes feel like as a 22 year old girl, I am failing. If I'm not drinking at a social event, or going on a date every friday night, something must be wrong with me! Not to mention, the anxiety that comes when I do go out and feel like I have to act a certain way or do certain things just because that is what 22 year old girls are supposed to do.
All of us have this desire to fit in and belong and we look to those in our same social group to show us how to achieve this. But those social groups come with stereotypes that our social media and environment tends to push on us. Stereotype threat is personnal, it's not about everyone pushing the stereotype on you, it's about you punishing yourself because of a specific stereotype that you are linked too.
I believe that the best way to combat stereotype threat is to gain a strong sense of self.


JURASSIC PARK (1993) Part of the A LOOK BACK series July 25th, 2001 Reviewed by Jerry Saravia
Steven Spielberg is one of the few directors that can produce real movie magic on screen. Consider the magic wand he waved in such classics as "Raiders of the Lost Ark", "E.T.", "Jaws" and "Close Encounters of the Third Kind." "Jurassic Park" is no exception and though it is no classic by any means, it is a thrill-happy, terrifically exciting action picture bringing back Spielberg's whiz-bang intensity that the otherwise middling "Always" and the overblown "Hook" lacked.
Based loosely on Michael Crichton's novel, "Jurassic Park" focuses on a group of scientists (Sam Neill, Jeff Goldblum and Laura Dern) visiting a fantastical park in a South American island populated by live-breathing dinosaurs, including a vicious Tyrannosaurus Rex, quick-tempered, meat-eating velociraptors, and cuddly Brontosaurus specimens. According to John Hammond (Richard Attenborough), the owner of the park, the dinosaurs were genetically engineered by dinosaur blood found in mosquitoes, which were preserved in amber! Essentially, Hammond is like Frankenstein, bringing back dinosaurs from the dead!
"Jurassic Park" is a thrill ride from start to finish. Spielberg and writers Michael Crichton and David Koepp are not interested in character development - they simply know that audiences are too interested in seeing dinosaurs. You have to remember that the year 1993 was a revolutionary year for cinema - the term CGI was born. This meant that creating believable creatures and landscapes with the use of computer-generated images was a true possibility. Thus, this film was the first to ever show the most realistic dinosaurs ever seen in film history and that was the draw for audiences. Today, CGI seems like an afterthought but back then, it was an astounding achievement.
Spielberg uses CGI to his advantage. There are two exceptional Spielberg sequences: one is a scary, horrifying sequence in which the T-Rex attacks two kids trapped in a car, and the other is when the velociraptors trap the same kids in a kitchen. There are lots of dino chomping scenes but then there are some purely amazing moments that evoke a sense of wonder and joy. The first glimpse of a dinosaur is the brontosaurus as it tries to reach a tree branch - Spielberg shoots the scene from a low-angle so that we get the feeling we are witnessing a majestic, prehistoric creature too grand and mysterious for our eye level. The T-Rex itself is astounding, and the impressive soundtrack (recorded in DTS, digital sound, for the first time ever) enhances the credibility and realism of these creatures.
So much talk about the dinos, what about the humans? Well, they recede in the background because any movie with creatures, especially dinos, will steal scenes from others, no holds barred. Still, this is a helluva good cast. Attenborough is appropriately and equally gleeful and stubborn as the gullible owner, unaware of what he has created. Sam Neill, an actor I dislike, gives a pleasing performance as Dr. Grant who seems to have a dislike for children. Laura Dern is wasted as Grant's wife, capable of much more than screaming fits. The actor who upstages them all is Jeff Goldblum as Ian Malcolm, a chaos theorist and mathematician who wears funny sunglasses and a rock n'roll leather jacket - he seems to have stepped out of "American Graffiti." His one-liners and his ridicule of this dangerous theme park bring a level of irony that Spielberg could have carried further than he does.
As a story, "Jurassic Park" lacks much thrust or significance, though it can be construed as a modern-day Frankenstein tale where the act of creating rests solely on whether something should be created, particularly when you meddle with nature. The characters are thin but they remain watchable personalities (including Samuel L. Jackson in a small role as a computer expert and Wayne Knight as another computer expert who decides to steal some specimens). But for undeniable thrills and escapist entertainment, nothing can beat Spielberg's exciting dinos and kinetic action sequences. As Pauline Kael once remarked, Spielberg doesn't just thrill you, he spooks you with giddiness and exhilaration.
For more reviews, check out JERRY AT THE MOVIES at http://moviething.com/members/movies/faust/JATMindex.shtml
E-mail me with any questions, comments or general complaints at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>========== X-RAMR-ID: 28974 X-Language: en X-RT-ReviewID: 236665 X-RT-TitleID: 1108705 X-RT-SourceID: 875 X-RT-AuthorID: 1314
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


NABU 2017/015
|
transliteration |
normalization |
translation |
|---|---|---|---|
|
a-na {d}nin-urta |
ana ninurta |
For Ninurta, the great lord, his lord, did Kadašman-Enlil, king of Babylon, son of Kadašman-Turgu, the king, for the lengthening of his reign offer (this seal). |


[all functions]
HP_Delete
Must be used to free memory allocated by HP MIDIFILE
HP_MIDIFILE_API void
HP_Delete
(
void*
pointer
,
);
Parameters
pointer
Any pointer to a memory-area, allocated by HP_MIDIFILE.
Return Value
Remarks
See Also
Samples


{% extends 'private.html' %} {% block title %}Delete Asset{% endblock %} {% block body %}
You're about to delete {{ asset.name }}.Please confirm.


This dialog makes it straightforward to export data from PeptideShaker to tab separated text files at six levels:
For the first five you can decide with features you want to export.
Note that you can only export
features for one level at the time, i.e., proteins or peptides, not proteins and peptides. However,
some of the export options includes information from more than one level.
The export Project option is an advanced export and recommended for expert use only. In most cases
you should be able to obtain the wanted export via the other export options.
Note that you can also export the content of most tables and all plots via the contextual export options
available in the upper right corner of each table/plot.


--- Day 15: Dueling Generators ---
Here, you encounter a pair of dueling generators. The generators, called generator A and generator B,
are trying to agree on a sequence of numbers. However, one of them is malfunctioning, and so the sequences don't always match.
As they do this, a judge waits for each of them to generate its next value, compares the lowest 16 bits of both values, and keeps track of the
number of times those parts of the values match.
The generators both work on the same principle. To create its next value, a generator will take the previous value it produced, multiply it by a factor
(generator A uses 16807
; generator B uses 48271
), and then keep the remainder of dividing that resulting product by<PHONE_NUMBER>
.
That final remainder is the value it produces next.
To calculate each generator's first value, it instead uses a specific starting value as its "previous value" (as listed in your puzzle input).
For example, suppose that for starting values, generator A uses 65
, while generator B uses 8921
. Then, the first five pairs of
generated values are:
--Gen. A-- --Gen. B--
1092455 430625591
1181022009<PHONE_NUMBER>
245556042<PHONE_NUMBER>
1744312007 137874439
1352636452 285222916
In binary, these pairs are (with generator A's value first in each pair):
00000000000100001010101101100111
00011001101010101101001100110111
01000110011001001111011100111001
01001001100010001000010110001000
00001110101000101110001101001010
01010101010100101110001101001010
01100111111110000001011011000111
00001000001101111100110000000111
01010000100111111001100000100100
00010001000000000010100000000100
Here, you can see that the lowest (here, rightmost) 16 bits of the third value match:<PHONE_NUMBER>001010
. Because of this one match, after
processing these five pairs, the judge would have added only 1
to its total.
To get a significant sample, the judge would like to consider 40 million pairs. (In the example above, the judge would eventually find a total
of 588
pairs that match in their lowest 16 bits.)
After 40 million pairs, what is the judge's final count?
Your puzzle answer was 626
.
--- Part Two ---
In the interest of trying to align a little better, the generators get more picky about the
numbers they actually give to the judge.
They still generate values in the same way, but now they only hand a value to the judge when it meets their criteria:
- Generator A looks for values that are multiples of
4
.
- Generator B looks for values that are multiples of
8
.
Each generator functions completely independently: they both go through values entirely on their own, only occasionally handing an acceptable
value to the judge, and otherwise working through the same sequence of values as before until they find one.
The judge still waits for each generator to provide it with a value before comparing them (using the same comparison method as before). It keeps track of
the order it receives values; the first values from each generator are compared, then the second values from each generator, then the third values, and so
on.
Using the example starting values given above, the generators now produce the following first five values each:
--Gen. A-- --Gen. B--
1352636452<PHONE_NUMBER>
1992081072 862516352
530830436<PHONE_NUMBER>
1980017072<PHONE_NUMBER>
740335192 412269392
These values have the following corresponding binary values:
01010000100111111001100000100100
01001001100010001000010110001000
01110110101111001011111010110000
00110011011010001111010010000000
00011111101000111101010001100100
01000101001000001110100001111000
01110110000001001010100110110000
01100000010100110001010101001000
00101100001000001001111001011000
00011000100100101011101101010000
Unfortunately, even though this change makes more bits similar on average, none of these values' lowest 16 bits match. Now, it's not until the 1056th
pair that the judge finds the first match:
--Gen. A-- --Gen. B--
1023762912 896885216
00111101000001010110000111100000
00110101011101010110000111100000
This change makes the generators much slower, and the judge is getting impatient; it is now only willing to consider 5 million pairs. (Using the
values from the example above, after five million pairs, the judge would eventually find a total of 309
pairs that match in their lowest 16
bits.)
After 5 million pairs, but using this new generator logic, what is the judge's final count?
Your puzzle answer was 306
.
Both parts of this puzzle are complete! They provide two gold stars: **
At this point, you should return to your advent calendar and try another puzzle.
If you still want to see it, you can get your puzzle input.
You can also [Shareon
Twitter
Reddit] this puzzle.


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LUKeychainAccess 1.0.1 Reference
LUKeychainAccess
A wrapper for iOS Keychain Services that behaves just like NSUserDefaults
.
Usage
Import the following files into your project, then #import
the LUKeychainAccess
.h
file.
LUKeychainAccess
.h
LUKeychainAccess
.m
Add Security.framework
under “Link Binary With Libraries” in “Build Phases” of your build target.
Use LUKeychainAccess
just as you would use NSUserDefaults
:
[[LUKeychainAccess standardKeychainAccess] setBool:NO forKey:@"authorized"];
BOOL authorized = [[LUKeychainAccess standardKeychainAccess] boolForKey:@"authorized"];
There are methods for getting and setting BOOL, double, float and NSInteger types, as well as NSString
, NSData
and generic NSObject
objects. You may use any object that can be encoded with NSKeyedArchiver
.
Requirements
LUKeychainAccess
requires iOS 5.0+. The tests are written using Kiwi.
License
LUKeychainAccess
is written by Costa Walcott, and is Copyright 2012 SCVNGR, Inc. It is free software, and may be redistributed under the terms specified in the MIT-LICENSE file.


This appendix describes the features of the awk scripting language.
The syntax for invoking awk has two basic forms:
awk [-v var=value] [-Fre] [--] 'pattern { action }' var=value datafile(s)
awk [-v var=value] [-Fre] -f scriptfile [--] var=value datafile(s)
An awk command line consists of the command, the script and the input filename. Input is read from the file specified on the command line. If there is no input file or "-" is specified, then standard input is read. The -F option sets the field separator (FS) to re.
The -v option sets the variable var to value before the script is executed. This happens even before the BEGIN procedure is run. (See the discussion below on command-line parameters.)
Following POSIX argument parsing conventions, the "--" option marks the end of command-line options. Using this option, for instance, you could specify a datafile that begins with "-", which would otherwise be confused with a command-line option.
You can specify a script consisting of pattern and action on the command line, surrounded by single quotes. Alternatively, you can place the script in a separate file and specify the name of the scriptfile on the command line with the -f option.
Parameters can be passed into awk by specifying them on the command line after the script. This includes setting system variables such as FS, OFS, and RS. The value can be a literal, a shell variable ($var) or the result of a command (`cmd`); it must be quoted if it contains spaces or tabs. Any number of parameters can be specified.
Command-line parameters are not available until the first line of input is read, and thus cannot be accessed in the BEGIN procedure. (Older implementations of awk and nawk would process leading command-line assignments before running the BEGIN procedure. This was contrary to how things were documented in The AWK Programming Language, which says that they are processed when awk would go to open them as filenames, i.e., after the BEGIN procedure. The Bell Labs awk was changed to correct this, and the -v option was added at the same time, in early 1989. It is now part of POSIX awk.) Parameters are evaluated in the order in which they appear on the command line up until a filename is recognized. Parameters appearing after that filename will be available when the next filename is recognized.
Typing a script at the system prompt is only practical for simple, one-line scripts. Any script that you might invoke as a command and reuse can be put inside a shell script. Using a shell script to invoke awk makes the script easy for others to use.
You can put the command line that invokes awk in a file, giving it a name that identifies what the script does. Make that file executable (using the chmod command) and put it in a directory where local commands are kept. The name of the shell script can be typed on the command line to execute the awk script. This is preferred for easily used and reused scripts.
On modern UNIX systems, including Linux, you can use the #! syntax to create self-contained awk scripts:
#! /usr/bin/awk -f
script
Awk parameters and the input filename can be specified on the command line that invokes the shell script. Note that the pathname to use is system-dependent.


--- layout: post title: Video of iPhone 4 Launch Day - Shot and Edited on iPhone 4 date: 2010-06-24 14:34:00.000000000 -07:00 type: post published: true status: publish categories: [] tags: [] meta: {} author: ---
This was shot and edited on an iPhone 4 using iMovie for iPhone. I was in line accompanying my friend Spencer when he went to go get an iPhone 4 at Lenox Mall in Atlanta, GA. I had already received mine a day earlier since I had pre-ordered online, so I was free to just record and snap pics the whole time.


Light and Sound Memory Game!
Welcome to the game!


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About
Joda-Money provides a library of classes to store amounts of money.
The JDK provides a standard currency class, but not a standard representation of money. Joda-Money fills this gap, providing the value types to represent money.
Joda-Money is licensed under the business-friendly Apache 2.0 licence.
Features
A selection of key features:
- CurrencyUnit - representing a currency
- Money - a fixed precision monetary value type
- BigMoney - a variable precision monetary type
- A customizable formatter
Documentation
Various documentation is available:
- The helpful user guide
- The Javadoc
- The change notes for each release
- The GitHub source repository
Why Joda Money?
Joda-Money provides simple value types, representing currency and money.
The project does not provide, nor is it intended to provide, monetary algorithms beyond the most basic and obvious. This is because the requirements for these algorithms vary widely between domains. This library is intended to act as the base layer, providing classes that should be in the JDK.
As a flavour of Joda-Money, here is some example code:
// create a monetary value Money money = Money.parse("USD 23.87"); // add another amount with safe double conversion CurrencyUnit usd = CurrencyUnit.of("USD"); money = money.plus(Money.of(usd, 12.43d)); // subtracts an amount in dollars money = money.minusMajor(2); // multiplies by 3.5 with rounding money = money.multipliedBy(3.5d, RoundingMode.DOWN); // compare two amounts boolean bigAmount = money.isGreaterThan(dailyWage); // convert to GBP using a supplied rate BigDecimal conversionRate = ...; // obtained from code outside Joda-Money Money moneyGBP = money.convertedTo(CurrencyUnit.GBP, conversionRate, RoundingMode.HALF_UP); // use a BigMoney for more complex calculations where scale matters BigMoney moneyCalc = money.toBigMoney();
Releases
Release 1.0.1 is the current release. This release is considered stable and worthy of the 1.x tag.
Joda-Money requires Java SE 8 or later and has no dependencies. There is a compile-time dependency on Joda-Convert, but this is not required at runtime thanks to the magic of annotations.
Available in Maven Central.
<dependency> <groupId>org.joda</groupId> <artifactId>joda-money</artifactId> <version>1.0.1</version> </dependency>
Java module name: org.joda.money.


---
start: 2017-12-06 13:00
edition: 33
participate: "https://www.eventbrite.com/e/serverless-computing-tickets-39947879182"
talk: "https://goo.gl/forms/m9aPSXemMLPBj35k1"
subheader: "Serverless Computing"
subnote: ""
agenda:
-
time: "6:30 pm - 7:00 pm"
title: "Serverless Computing"
subtitle: "Serverless computing is a cloud computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity
There are several use cases where Serverless Computing adds the advantage in terms of time to delivery and operational costs. Amazon AWS Lambda, Google Cloud Functions are the popular provides in the market today."
speaker_profile: "By: Karthik Divi, is a Full Stack developer. He build high throughput, low latency and infinitely scalable applications."
image: "karthik_divi.jpg"
-
time: "7:00 pm - 7:30 pm"
title: "Live Coding"
-
time: "7:30 pm - 7:45 pm"
title: "Break & Networking"
-
time: "7:45 pm - 8:30 pm"
title: "Continued..."
---


pybilt.com_trajectory package¶
Submodules¶
pybilt.com_trajectory.COMTraj module¶
Center of Mass based bilayer analysis tools
This module defines various classes and functions used to process and analyze a lipid bilayer trajectory. This module assumes the structure and trajectory are initiallaly stored in MDAnalysis objects and therefore processes MDAnalysis objects. The lipids constituting the bilayer are read in from the MDAnalysis objects and are converted to center of mass (COM) representations. Lipids are partitioned into an ‘upper’ and a ‘lower’ leaflet based on the z-position of the COM. The built-in analysis functions then operate on the COM representations to compute quantities such as the lateral mean squared displacement. Many analysis functions allow specification of the leaflet and type of lipid to perform the the analysis on. The primary (parent class) is the MemSys class. The analysis functions are members of the MemSys class.
Example
>> import MemSys as ms >> mem_sys = ms.MemSys(mda_universe.trajectory,mda_selection_of_bilayer_lipids)
-
class
pybilt.com_trajectory.COMTraj.
COMTraj
(mda_traj, mem_sel, plane='xy', fstart=0, fend=-1, fskip=1, frame_path='Default', frame_save=False, nprocs=1)[source]¶ Bases:
future.types.newobject.newobject
This is the main class object. An instance of this class reads in the trajectory and a selection (both MDAnalysis objects) and creates/reduces the lipids to center of mass (COM) representations; i.e. a center of mass trajectory. There are several member functions to perform various types of analyses based on the COM representations.
COMTraj initialization.
Each lipid in the system is assumed to be its own residue. It is also assumed that the coordinates in the MDAnalysis trajectory are wrapped, so when this object is initialized it computes COMs using the wrapped coordinates. It then unwraps the raw coordinates and removes the system COM motion before recomputing another set of lipid COMs (now based on the unwrapped coordinates). Both representations are stored and are used in the various analyses.
Parameters: - mda_traj (MDAnalysi.Universe.trajectory) – The MDAnalysis trajectory object containg the the molecular dynamics trajectory data for the system.
- mem_sel (MDAnalysis.AtomGroup) – The MDAnalysis atom selection (AtomGroup) containing the bilayer lipids.
- plane (str, optional) – Defines the lateral plane of the bilayer. The default is ‘xy’. The other options are ‘yz’ or ‘xz’, or their equivalent ‘zy’ or ‘zx’.
- fstart (int, optional) – The index of the initial frame to use from the MDAnalysis trajectory. The default is 0.
- fstart (int, optional) – The index of the final frame to use from the MDAnalysis trajectory. The default is -1 (i.e. the last frame).
- fskip (int, optional) – Use every fskip frame in mda_traj. The default is 1 (i.e. use all frames in mda_traj).
- frame_path (str, optional) – Path in which to store the Shelf database of the Frame objects created for the COM representations. The default is ‘Default’ which uses the ‘/tmp/’ dir.
- frame_save (bool, optional) – Preserve the Shelf database files of Frame objects for this MemSys after deletion. The default is False (i.e. the Shelf database files are deleted). If True then the Shelf database files are not removed when this MemSys object is deleted.
- nprocs (int, optional) – Define the number of processors to use when unwrapping the coordinates. The default is 1 (i.e. no paralellization). Greater than 1 uses a parallelized version of the unwrap function when uwrapping coordinates. nprocs should not exceed the the number of cores on your machine.
- Atrributes:
- plane (list of int): A two element list of the integer indices corresponding to the bilayer lateral
- coordinate planes (0 for x,1 for y, and 2 for z). Determined by the value of Args:plane.
- norm (int): The integer index corresponding to the bilayer normal coordinate plane
- (0 for x,1 for y, or 2 for z). Determined by the value of Args:plane.
- leaflets (dict of Leaflet): A dict containing the Leaflet instances used to define the upper and lower
- bilayer leaflets for this system. This dict will contain the two keys, ‘upper’ and ‘lower’, upper and lower Leaflet objects respectively.
- com_leaflet (list of str): A list of leaflet name strings that maps the lipid index
- to the Leaflet (‘upper’ or ‘lower’) it has been assigned to. E.g. >>> com_leaf[0] >>> ‘upper’ means lipid index 0 was assigned to the ‘upper’ leaflet.
- nlipids (int): The number of lipids in the system. Each MDAnalysis residue in Args:mem_sel is
- treated as a lipid.
- clusters (list of list of list int): This is initialized as an empty list. It is used by the function
- CheckClustering() as a persistent container to store the list of clusters for each frame. clusters is
- overwritten after each call to CheckClustering().
- frame (obj:frames): An instance of the Frame container object (frames) to hold the COM trajectory data
- for this system.
nframes (int): The total number of frames processed and stored for the MemSys instance.
-
calc_area_per_lipid_box
(leaflet='both')[source]¶ Approximate the area per lipid for select lipid COMs in a select leaflet(s).
This function computes a composite approximation of the area per lipid by simply dividing lateral area of the simulation box by the number lipids in a specific leaflet.
Parameters: - leaflet (str, optional) – A string designating which Leaflet to include in the computation. Default is ‘both’: the area per lipid is averaged over the two leaflets.
- group (str, optional) – A string with the name of a specific LipidGroup to in the computation.
Returns: - This is a nframesx4 numpy array (of floats) containing the
results of this computation across all frames. apl_results[i,0] = simulation time for frame i. apl_results[i,1] = area per lipid at frame f = i. apl_results[i,2] = the current running time average (area per lipid) at frame f = i. apl_results[i,3] = the current standard deviation of the running time average at frame f = i. For i in range( nframes ).
Return type: numpy.array
-
calc_area_per_lipid_closest_neighbor_circle
(leaflet='both', group='all')[source]¶ Approximate the area per lipid for select lipid COMs in a select leaflet(s).
This function computes an approximation of the area per lipid of a selection of lipid COMs by locating the closest neighbor and computing the non-overlapping circular area the two equiradius circles formed by the vector between the two COM points. At each frame this is averaged over the configuration.
Parameters: - leaflet (str, optional) – A string designating which Leaflet to include in the computation.
- group (str, optional) – A string with the name of a specific LipidGroup to in the computation.
Returns: - This is a nframesx4 numpy array (of floats) containing the
results of this computation across all frames. apl_results[i,0] = simulation time for frame i. apl_results[i,1] = configurational average area per lipid at frame f = i. apl_results[i,2] = the current running time average (of the configurational average) at frame f = i. apl_results[i,3] = the current standard deviation of the running time average at frame f = i. For i in range( nframes ).
Return type: numpy.array
-
calc_membrane_thickness
()[source]¶ Compute the bilayer thickness across the trajectory. Computes the thickness of the bilayer (along the normal direction). The algorithm is based on the GridMAT-MD bilayer thickness calculation.
Returns: - This is a two element tuple containing numpy arrays of the computation results.
- tuple[0] => thickness: A nx5 numpy array containing the
results of the thickness computation for the specified frames. Specifically:thickness[i,0] = simulation time for frame f = i + fstart. thickness[i,1] = the configurational average thickness for frame f = i + fstart. thickness[i,2] = the standard deviation of the configurational average thickness for frame f = i + fstart. thickness[i,3] = the running time average of the configurational average thickness for frame f = i + fstart. thickness[i,4] = the running standard deviation of the time averaged configurational average thickness for frame f = i + fstart. For i in range( (fend-fstar) + 1).
tuple[1] => thickness_map: A nxNx6 numpy array containing the thickness data that can be used to generate a 3d thickness map/plot. Specifically:
thickness[i,j,0] = simulation time for frame f = i + fstart and lipid j. thickness[i,j,1] = the average x position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,2] = the average y position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,3] = the lower z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,4] = the upper z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,5] = the difference between the upper and lower z positions for lipid j and its cross leaflet partner at frame f = i + fstart. For i in range((fend-fstar) + 1) and For j in range(nlipids).
Return type: tuple of numpy.array
-
calc_membrane_thickness_parallel
(nprocs=2)[source]¶ Parallelized version of MemSys.CalcMembraneThickness
Computes the thickness of the bilayer (along the normal direction) across the trajectory. The algorithm is based on the GridMAT-MD bilayer thickness calculation. The calculation is parallized over the number of frames in the trejectory using the multiprocessor module. This function passes the thickness_frames fucntion to the multiprocessor threads.
Parameters: nprocs (int) – The number processors (threads) to use in the computation. Returns: - This is a two element tuple containing numpy arrays of the computation results.
- tuple[0] => thickness: A nx5 numpy array containing the
results of the thickness computation for the specified frames. Specifically:thickness[i,0] = simulation time for frame f = i + fstart. thickness[i,1] = the configurational average thickness for frame f = i + fstart. thickness[i,2] = the standard deviation of the configurational average thickness for frame f = i + fstart. thickness[i,3] = the running time average of the configurational average thickness for frame f = i + fstart. thickness[i,4] = the running standard deviation of the time averaged configurational average thickness for frame f = i + fstart. For i in range( (fend-fstar) + 1).
tuple[1] => thickness_map: A nxNx6 numpy array containing the thickness data that can be used to generate a 3d thickness map/plot. Specifically:
thickness[i,j,0] = simulation time for frame f = i + fstart and lipid j. thickness[i,j,1] = the average x position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,2] = the average y position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,3] = the lower z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,4] = the upper z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,5] = the difference between the upper and lower z positions for lipid j and its cross leaflet partner at frame f = i + fstart. For i in range((fend-fstar) + 1) and For j in range(nlipids).
Return type: tuple of numpy.array
-
calc_msd
(leaflet='both', group='all')[source]¶ Compute the configurational average mean squared displacement for select lipids in a select leaflet(s).
This function allows the mean squared displacement (MSD) to be computed for a specified leaflet (‘upper’, ‘lower’, or ‘both’) and for a specified LipidGroup within the chosen Leaflet. This calculation is over the whole trajectory and assumes the first frame is the reference frame for computing the displacement.
Parameters: - leaflet (str) – A string designating which Leaflet to include in the computation.
- group (str) – A string with the name of a specific LipidGroup to in the computation.
Returns: - This is a nframesx2 numpy array (of floats) containing the
results of the MSD computation across all frames. msd_results[i,0] = simulation time for frame i. msd_results[i,1] = the configurational average MSD over the specified LipidCOMs for frame f = i. For i in range( nframes ).
Return type: numpy.array
-
calc_msd_parallel
(leaflet='both', group='all', nprocs=2)[source]¶ Parallelized version of calc_msd
This function allows the mean squared displacement (MSD) to be computed for a specified leaflet (‘upper’, ‘lower’, or ‘both’) and for a specified LipidGroup within the chosen Leaflet. This calculation is over the whole trajectory and assumes the first frame is the reference frame for computing the displacement. The calculation is parallized over the number of frames in the trejectory using the multiprocessor module.
Parameters: - leaflet (str) – A string designating which Leaflet to include in the computation.
- group (str) – A string with the name of a specific LipidGroup to in the computation.
Returns: - This is a nframesx2 numpy array (of floats) containing the
results of the MSD computation across all frames. msd_results[i,0] = simulation time for frame i. msd_results[i,1] = the configurational average MSD over the specified LipidGroup for frame f = i. For i in range( nframes ).
Return type: numpy.array
-
check_clustering
(leaflet='both', group='all', dist=15.0)[source]¶ Determine physical cluster for select lipid COMs in a select leaflet(s).
This function determines physical clusters based on Cartesian distance criteria in a specified leaflet (‘upper’, ‘lower’, or ‘both’) and for a specified LipidGroup within the chosen Leaflet. The routine determines the physical clusters for each frame in the trajectory and computes properties of these clusters, e.g. number of clusters, which are time averaged over the trajectory. The information on the clusters is stored in MemSys.clusters which can then be used by other functions (like MemSys.ExportClustersForPlotting).
Parameters: - leaflet (str, optional) – A string designating which Leaflet to include in the computation.
- group (str, optional) – A string with the name of a specific LipidGroup to in the computation.
- dist (float, optional) – The distance cutoff for determining clusters.
Returns: - This is a nframesx5 numpy array (of floats) containing the
results of this computation across all frames. cluster_results[i,0] = simulation time for frame i. cluster_results[i,1] = the current running time averaged number of clusters at frame f = i. cluster_results[i,2] = the current standard deviation of the running time averaged number of clusters at frame f = i. cluster_results[i,3] = the current running time averaged configurational average lipids per cluster at frame f = i. cluster_results[i,4] = the current standard deviation of the running time averaged
configurational average lipids per cluster at frame f = i.
For i in range( nframes ).
Return type: numpy.array
-
export_clusters_for_plotting
()[source]¶ Determine physical cluster for select lipid COMs in a select leaflet(s).
This function determines physical clusters based on Cartesian distance criteria in a specified leaflet (‘upper’, ‘lower’, or ‘both’) and for a specified LipidGroup within the chosen Leaflet. The routine determines the physical clusters for each frame in the trajectory and computes properties of these clusters, e.g. number of clusters, which are time averaged over the trajectory. The information on the clusters is stored in MemSys.clusters which can then be used by other functions (like MemSys.ExportClustersForPlotting).
Parameters: - leaflet (str, optional) – A string designating which Leaflet to include in the computation.
- group (str, optional) – A string with the name of a specific LipidGroup to in the computation.
- dist (float, optional) – The distance cutoff for determining clusters.
Returns: - This is a nframesx5 numpy array (of floats) containing the
results of this computation across all frames. cluster_results[i,0] = simulation time for frame i. cluster_results[i,1] = the current running time averaged number of clusters at frame f = i. cluster_results[i,2] = the current standard deviation of the running time averaged number of clusters at frame f = i. cluster_results[i,3] = the current running time averaged configurational average lipids per cluster at frame f = i. cluster_results[i,4] = the current standard deviation of the running time averaged
configurational average lipids per cluster at frame f = i.
For i in range( nframes ).
Return type: numpy.array
-
number_of_unique_groups
()[source]¶ Get the number of uniquely named LipidGroups within both Leaflet objects.
Returns: int: The number of uniquely named LipidGroup objects.
-
class
pybilt.com_trajectory.COMTraj.
Frame
(nlipids)[source]¶ Bases:
future.types.newobject.newobject
A molecular dynamics style Frame object.
Frame initialization.
Parameters: nlipids (int) – The number of lipids (LipidCOM objects) that this frame will hold - Atrributes:
- lipidcom (list of obj:LipidCOM): A list of the LipidCOM objects assigned to the Frame. box (np.array): A 3 element vector containing the (rectangular) xyz box edge lengths. time (float): The simulation time that this Frame represents. number (int): The frame number of this Frame. mdnumber (int): The corresponding frame number in the original MD trajectory
-
com
(wrapped=True)[source]¶ Computes the center of mass (COM) for the Frame
This member function is used to compute the overall center of mass (COM) of the Frame using the LipidCOM object coordinates and masses.
Parameters: wrapped (bool, optional) – Define which set of coordinates to use in the computation. True (default) - The wrapped LipidCOM coordinates are used to compute the COM of the frame. False - The unwrapped LipidCOM coordinates are used to compute the COM of the frame. Returns: A 3 element vector containing the xyz coordinates of the Frame’s COM Return type: np.array
-
set_box
(box_lengths)[source]¶ Set the rectangular xyz box edge lengths.
Parameters: box_lengths (numpy.array) – A 1d, 3 element numpy.array containing the x,y,z box sizes (or edge lengths)
-
class
pybilt.com_trajectory.COMTraj.
FrameShelve
(prefix='/tmp/', save=False)[source]¶ Bases:
future.types.newobject.newobject
Container for Frame objects This class object serves as a container to store a set of Frame objects corresponding to a molecular dynamics trajectory. This class saves the Frame objects on disk using the shelve module and provides an interface to access instances of those saved Frames. The Frames are saved in the shelve database with integer index keys 0 -> (nframes-1) and can accessed by indexing the instance of the frames object.
Initialization of the frames object.
Parameters: - prefix (str, optional) – The location/path to store the “shelve”d Frame data. ‘/tmp/’ (default) - The data is stored in the unix/linux tmp directory.
- save (bool, optional) – Set whether to save the shelved Frame data after frames object deletion. False (default) - the shelved Frame data is deleted upon calling __del__ True - the shelved Frame data is not deleted when __del__ is called
-
nframes
¶ The number Frame objects being stored.
Type: int
-
pid
¶ The process id that the frames object is created in.
Type: int
-
path
¶ The path to where the shelved Frame data is stored.
Type: str
-
save
¶ Set whether to save the shelved Frame data after frames object deletion.
Type: bool
-
fs_name
¶ The base name of the shelve database file used to store Frame objects.
Type: str
-
frame_shelf
¶ The Shelf that will hold the Frame objects.
Type: shelve.Shelf
-
class
pybilt.com_trajectory.COMTraj.
Leaflet
(name)[source]¶ Bases:
future.types.newobject.newobject
Create a bilayer Leaflet representation. This class object is used to group lipids together according to their bilayer leaflet. It is primarily meant to store the indices of LipidCOMs as they are in a Frame.lipidcom list. This class also creates sub-groups within the Leaflet based on the LipidCOM.type using LipidGroup objects. Instances of Leaflet are created by the MemSys class.
Initializes an instance of a Leaflet object.
Parameters: name (str) – The name of the bilayer leaflet being initialized (‘upper’ and ‘lower’ are used by the MemSys class). -
name
¶ The name of the Leaflet (e.g. ‘upper’ or ‘lower’).
Type: str
-
members
¶ A list containing the integer indices associated with the LipidCOM objects within a Frame that are assigned to the Leaflet instance.
Type: list of int
-
groups (list of obj
LipidGroup): A list of the LipidGroup objects (uniquely named) that are created by the Leaflet instance as new members are added.
-
group_dict
¶ A dictionary keyed according to the names of the LipidGroup objects created, which stores the corresponding index of that LipidGroup in self.groups.
Type: dict
-
add_member
(index, resname)[source]¶ Add new lipids to the Leaflet.
This function is meant to be used to add new lipids according to their Frame.lipidcom index to the Leaflet and to a LipidGroup according resname/type/name. :Parameters: * index (int) – The index of the lipid being added to the Leaflet.
- resname (str) – The resname (or LipidCOM.type) of the lipid being added.
-
get_group_indices
(group_name)[source]¶ Get the indices of lipids in the Leaflet belonging to a specific LipidGroup.
Args: group_name (string): The name of the LipidGroup pull LipidCOM indices from.
Passing the string ‘all’ will return indices of all the lipids assigned to the Leaflet instance. If the group_name is not recognised (i.e. is not in the group_dict) The function defaults to ‘all’.Returns: - A list containing the integer indices of lipids in the Leaflet that
- belong to the specified LipidGroup.
Return type: list of int
-
get_group_names
()[source]¶ Get the names of all the LipidGroup objects in the Leaflet
Returns: list of str: A list of the names of current LipidGroup objects.
-
get_member_indices
()[source]¶ Get the indices of all lipids (LipidCOM) in the Leaflet. This member function Returns: the list of indices for the lipids grouped in the Leaflet instance.
Returns: A list of integer indices of the lipids associated with the Leaflet instance. Return type: list of int
-
-
class
pybilt.com_trajectory.COMTraj.
LipidCOM
[source]¶ Bases:
future.types.newobject.newobject
A lipid center of mass (COM) object.
This object stores the COM coordinates of a lipid (or other molecule or group of atoms) computed from both the wrapped and unwrapped atomic coordinates. This object also stores information about the type of lipid as well as the total mass of the lipid.
LipidCOM initialization
-
type
¶ The lipid type (e.g. the lipid could be typed b resname).
Type: str
-
com
¶ The length three vector holding the wrapped xyz coordinates.
Type: np.array
-
com_unwrap
¶ The length three vector holding the unwrapped xyz coordinates.
Type: np.array
-
mass
¶ The total mass of the atoms used to define LipidCOM.
Type: float
-
extract
(mda_residue, unwrap=False, box=None)[source]¶ Get the center of mass coordinates from an MDAnalysis residue
This function calls the MDAnalysis member function center_of_mass() of the residue to compute the center of mass of the atoms constituting the residue.
Parameters: - mda_residue (MDAnalysis.residue) – An MDAnalysis residue object from which to extract a center of masss coordinates.
- unwrap (bool, optional) – Define which com container to store coordiates in. False (default) - The COM coordinates are stored in the container designated for the wrapped coordinate representation. True - The COM coordinates are stored in the container designated for the unwrapped coordinate representation.
-
-
class
pybilt.com_trajectory.COMTraj.
LipidGroup
(name)[source]¶ Bases:
future.types.newobject.newobject
Object to group lipid indices by type/resname/name. Instances of this object are created by the Leaflet class.
Initializes LipidGroup object.
Parameters: name (str) – The name/type/resname of the lipids being grouped in this object. -
lg_members
¶ A list to hold the indices of lipids added to this this LipidGroup.
Type: list of int
-
lg_name
¶ The name/type/resname of the lipids being grouped in this object.
Type: str
-
-
class
pybilt.com_trajectory.COMTraj.
ParFrames
(nframes, fs_name, frame_shelve)[source]¶ Bases:
future.types.newobject.newobject
Read-Only version of frames object This class is effectively used to generate read-only copies of the frames class, which can be passed to functions that do parallelized computations over the number of frames. Unlike frames ParFrames does not create a new Shelf database. It must be passed an existing Shelf of Frame objects. ParFrames also does not have any functions defined to modify the Shelf (add/remove Frame objects). This is to avoid conflicts from multiple processor accesses to the Shelf database.
Initialize the ParFrames object
Args: nframes (int): The number of Frames stored in the shelve database. fs_name (string): The base name (prefix) of the shelve database files. frame_shelve (shelve.Shelf): The Shelf object containing the Frame objects.
-
pybilt.com_trajectory.COMTraj.
msd_frames
(frames, fstart, fend, indices, refframe, plane)[source]¶ Compute the mean squared displacement for range of Frame objects in frames.
This function allows the mean squared displacement (MSD) to be computed for a specified subset of the Frame objects in a frames (or ParFrames) object. This function was created to be called from the function MemSys.CalcMSD_parallel as a function to be passed to the multiprocessor threads.
Parameters: - frames (obj – frames or obj:ParFrames): The object containing all the Frames of the trajectory.
- fstart (int) – The index of the first frame to start the analysis on.
- fend (int) – The index of the last frame to analyze.
- indices (list of int) – List of integer indices of the LipidCOMs to include in the computation.
- refframe (int) – The index of the frame that is to be taken as the reference for the MSD computation.
- plane (list of int) – The list of the indices corresponding to the coordinate planes (x: 0,y 1,z :2) to be included in the MSD computation.
Returns: - This is a nx2 numpy array (of floats) containing the
results of the MSD computation for the specified frames. msd_results[i,0] = simulation time for frame f = i + fstart. msd_results[i,1] = the configurational average MSD over the specified LipidCOMs for frame f = i + fstart. for i in range( (fend-fstart)+1 ).
Return type: numpy.array
-
pybilt.com_trajectory.COMTraj.
thickness_frames
(frames, fstart, fend, leaflets, nlipids, plane, norm)[source]¶ Compute the bilayer thickness for a range of Frame objects in frames. Computes the thickness of the bilayer (along the normal direction). The algorithm is based on the GridMAT-MD bilayer thickness calculation. This function was created to be called used in MemSys.CalcThickness_parallel as a function to be passed to the multiprocessor threads.
Parameters: - frames (obj – frames or obj:ParFrames): Object containing all the Frame objects of the trajectory.
- fstart (int) – The index of the first frame to start the analysis on.
- fend (int) – The index of the last frame to analyze.
- leaflets (dict of Leaflet) – A dict containing the Leaflet instances used to define the upper and lower bilayer leaflets for this calculation. This dict should contain the two keys, ‘upper’ and ‘lower’, corresponding to instances of the Leaflet class.
- nlipids (int) – The total number of LipidCOMs (or lipids) in the two Leaflet instances.
- plane (list of int) – A list of the integer indices corresponding to the bilayer lateral coordinate planes (0 for x,1 for y, and 2 for z)
- norm (int) – The integer index corresponding to the bilayer normal coordinate plane (0 for x,1 for y, or 2 for z).
Returns: - This is a two element tuple containing numpy arrays of the computation results.
tuple[0] => thickness: A nx3 numpy array containing the results of the thickness computation for the specified frames. Specifically:
thickness[i,0] = simulation time for frame f = i + fstart. thickness[i,1] = the configurational average thickness for frame f = i + fstart. thickness[i,2] = the standard deviation of the configurational average thickness for frame f = i + fstart. For i in range( (fend-fstar) + 1).
tuple[1] => thickness_map: A nxNx6 numpy array containing the thickness data that can be used to generate a 3d thickness map/plot. Specifically:
thickness[i,j,0] = simulation time for frame f = i + fstart and lipid j. thickness[i,j,1] = the average x position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,2] = the average y position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,3] = the lower z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,4] = the upper z position for lipid j and its cross leaflet partner at frame f = i + fstart. thickness[i,j,5] = the difference between the upper and lower z positions for lipid j and its cross leaflet partner at frame f = i + fstart. For i in range((fend-fstar) + 1) and For j in range(nlipids).
Return type: tuple of numpy.array


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WITNESS LIST Public School Finance, Select Committee April 24, 2004-10:00A HB 1 For: Gamkhar, Shama (Self) Olson, Jerome (Self) On: Baker, Bruce (Self) Bryce, Cathy (Self) Casey, Daniel (Self and Fast Growth School Coalition) Colbert, Paul (Self and El Paso ISD) Grusendorf, Bill (Texas Association of Rural Schools) Hollingsworth, Lonnie (Texas Classroom Teachers Ass'n) Keller, Harrison (Speaker Tom Craddick) Meraz, Gloria (Texas Library Association) Moak, Lynn (Moak, Casey Associates LLP) Morton, Dale (Wells Independent School District) O'Hanlon, Kevin (Self) Pena, Martin (South Texas Association of Schools) Pierce, Wayne (Equity Center) Ressel, Betty (Texas Comptroller) Schlomach, Byron (Texas Public Policy Foundation) Wisnoski, Joe (Texas Education Agency)


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layout: post
title: Future Topics for Extreme Programmers Meetup
published: 1
categories:
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We had a good turnout for our first Extreme Programmers London meetup and gathered plenty of ideas for future sessions.
Here's some of the suggestions of topcs came out of group discussion:
- Hiring process
- Experience reports where XP hard to apply
- Pairing with unpairables
- How to accelerate feedback loops
- Internal knowledge share in XP teams
- Quality / Productivity
- Global, distributed teams
- User stories
- Techniques to make Continuous Deployment easier
- Helping product understand technical debt and avoiding it
- Teaching advancing TDD
- XP 101: discuss each practice - still relevant? successes?
- Test driving the hard bits eg, security, resilience
- Object callesthenics
- Show 'n' tell - learnings from retrospectives
- Interconnectedness of things
- Things we used to do in XP
- Modern XP for concurrent f() world
- Embrace Change vs sticking to trad XP
We are probably going to vary the format from month to month with shorter presentation and longer discussion slots. Next time we'll have 2 short talks plus a Fishbowl Book club session on Extreme Programming Explained, first edition (the white book).


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Details: https://github.com/IDPF/epubcheck/issues/777


Bacon ipsum dolor amet brisket bresaola ball tip sirloin. Ribeye flank ham hock, kielbasa t-bone jerky tongue strip steak.  Ham ham hock short loin filet mignon short ribs swine hamburger cow.
Burgdoggen andouille tail pastrami flank porchetta chicken alcatra pancetta boudin. Jerky t-bone shoulder chislic meatloaf chuck short ribs spare ribs. Corned beef cow pastrami rump. Leberkas ham chislic pig, swine tongue shankle sausage pork belly shoulder frankfurter ball tip short loin biltong. Corned beef turducken frankfurter salami chicken.


The remote FS root of the node will be the working directory for the script. If your shell script has no header line like #!/bin/sh —, then the shell configured system-wide will be used, but you can also use the header line to write script in another language (like #!/bin/perl) or control the options that shell uses.
By default, the shell will be invoked with the "-ex" option. So all of the commands are printed before being executed, and the script is considered a failure if any of the commands exits with a non-zero exit code. Again, add the #!/bin/... line to change this behavior.


| Path: | README |
| Last Update: | Mon Dec 14 23:54:16 -0500 2009 |
acts_as_audited is an ActiveRecord extension that logs all changes to your models in an audits table.
The purpose of this fork is to store both the previous values and the changed value, making each audit record selfcontained.
script/plugin install git://github.com/EmFi/acts_as_audited.git
script/generate audited_migration add_audits_table
script/generate audited_migration_update update_audits_table
rake db:migrate
Declare acts_as_audited on your models:
class User < ActiveRecord::Base acts_as_audited :except => [:password, :mistress] end
Within a web request, will automatically record the user that made the change if your controller has a current_user method. Comments can be added to an audit by setting model.audit_comments before create/update/destroy. If the :comment_required option is given to acts_as_audited, the save/update/destroy action will fail with add an error on model.audit_comment and triggering a transaction rollback if model.audit_comment is nil.
To record a user in the audits outside of a web request, you can use as_user:
Audit.as_user(user) do # Perform changes on audited models end
If your model declares attr_accessible after acts_as_audited, you need to set +:protect+ to false. acts_as_audited uses attr_protected internally to prevent malicious users from unassociating your audits, and Rails does not allow both attr_protected and attr_accessible. It will default to false if attr_accessible is called before acts_as_audited, but needs to be explicitly set if it is called after.
class User < ActiveRecord::Base acts_as_audited :protect => false attr_accessible :name end
acts_as_audited works with Rails 2.1 or later.
Join the mailing list for getting help or offering suggestions:
http://groups.google.com/group/acts_as_audited
Contributions are always welcome. Checkout the latest code on GitHub:
http://github.com/collectiveidea/acts_as_audited
Please include tests with your patches. There are a few gems required to run the tests:
$ gem install multi_rails $ gem install thoughtbot-shoulda jnunemaker-matchy --source http://gems.github.com
Make sure the tests pass against all versions of Rails since 2.1:
$ rake test:multi_rails:all
Please report bugs or feature suggestions on GitHub:
http://github.com/collectiveidea/acts_as_audited/issues


Spoilers: Jags in danger from Gray in EastEnders as he gets close to Chantelle
The Panesar clan arrived in EastEnders several weeks back, and — upon their arrival — they were determined to track down their sister, Ash (Guraline Kaur Garcha). However, Jags (Amar Adatia) soon found himself distracted from his mission when he set his sights upon Chantelle (Jessica Plummer) in a scene which implied that he recognised the young woman.
A conversation with Kheerat (Jaz Deol) later clarified matters for the viewers, as it was revealed that Jags had previously went to school with Chantelle — and that he’d once went out on a date with her.
In the coming episodes, Jags decides it’s time that he speak to Chantelle, and thus he approaches her and strikes up a conversation.
It doesn’t take long for Chantelle to remember who he is, and they begin to fondly reminisce about old times.
Later in the week, Chantelle bumps into Jags once more — only this time he’s with Kheerat, and she’s with Gray (Toby-Alexander Smith). Of course, Gray wastes little time in making his presence known.
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With Gray having demonstrated how controlling he is in the past; he’ll no doubt feel threatened by having one of Chantelle’s exes living on the Square.
As a result, could he set about doing something about it?
Is Jags in danger from the solicitor?
If his past behaviour is anything to go by, then it’s certainly a possibility.
Mind you, the Panesar clan have proven that you don’t want to mess with them, so perhaps Gray could’ve met his match?
One to watch: Thursday 28th November at 7:30pm on BBC One.


This section describes the prerequisites and the basic steps to install the RSP installation image using the Management Console.RSP requires at least 2 GB of additional memory on the Steelhead appliance. You must have role-based permission for RSP to install RSP. For details, see Viewing Permissions. Before installing a new RSP image, you must stop the RSP service currently running on the Steelhead appliance. If you have previously installed RSP for RiOS v5.0.x, you must reinstall the RSP image for RiOS v5.5 and later. RSP for RiOS v5.0.x is not compatible with RSP for RiOS v5.5. Installing a new RSP image replaces the previously installed image (the RSP packages and installed VMs are unmodified). You cannot enable RSP when a Management In-Path (MIP) interface is enabled and vice versa. For details, see Modifying In-Path Interfaces. For details on Steelhead appliance RSP support for guest operating systems, see the product specification sheets at: http://www.riverbed.com Note: When you upgrade from Steelhead models 5050M to 5050H, 1050L to 1050_LR, 1050_M to 1050_MR, and 1050H to 1050HR, the upgrade process deletes the RSP partition and data. Before you install the new image, back up the RSP slots and after you install the image restore the slots from the backup. For information on backing up, see the RSP User’s Guide.
Install RSP From:URL - Select this option and specify the URL to the RSP image in the text box next to the drop-down menu. Local File - Select this option and specify the pathname in the text box next to the drop-down menu, or click Browse to go to the local file directory. Existing Image - Select an image that you uploaded to the Steelhead appliance using the Riverbed CLI. For details, see the Riverbed Command-Line Interface Reference Manual.
3. Click Install to download and install RSP on your system.
4. Click Start to start the RSP service.


FP numbers can be displayed in many ways. For example, as we have seen, an FP number may actually represent a date-time. When we display the FP number 366.0000, we may want to show it (in text) as "Jan 1, 1901." The Math Library has a number of system-defined formats for your use. You may also allow users to define their own formats with the Float Format controller.
The underlying structures and routines to use and create float format options are explained in the next section. In most cases, however, the simple inclusion of a Float Format controller (of
FloatFormatClass
) provides all of the UI and functionality to create and apply formats to FP numbers within text objects.


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Junior Engineer
Moving Picture Company
Triage, coordinate and schedule technical support requests and tasks. Machine building and imaging, Manage internal documentation using Confluence and other productivity tools. Maintain logs and implement routines that track asset movement, security events and other operational procedures as necessary. Provide 1st & 2nd level desktop and application support for producer laptops, artist workstations, servers, and networks. Installation and support of telephones and printers. Engage with suppliers during the procurement process.