THE LAND BEFORE TIME A film review by Mark R. Leeper Copyright 1988 Mark R. Leeper
Capsule review: Apparently inspired by the art of William Stout, THE LAND BEFORE TIME is short on characterization as well as screen time. The idea of doing a Disney-style cartoon with dinosaurs as characters was a good one, but THE LAND BEFORE TIME does not deliver enough. Rating: low +1.
It is surprising that Disney studios never did "cute" animation of dinosaurs. They have done cute versions of most familiar animals in their various films: dogs, cats, pigs, mice, birds, fish, insects--and those were just "bit parts" in cartoons; that does not count as continuing characters. But the only time Disney ever did dinosaurs was in FANTASIA and there they were just big, meaty blood-and-thunder engines of destruction who crashed into each other to the tune of Stravinsky's "Rite of Spring." As much as children love dinosaurs already, Disney never animated a dinosaur that was even remotely likable. Not that it was a void that really needed filling, but Disney animation department veteran Don Bluth, who specializes in imitating Disney's animation heyday, has made an almost-feature-length animated film, THE LAND BEFORE TIME, about the adventures of some likable baby dinosaurs. And Bluth uses "flat animation" in what may be the only serious attempt of such animation since FANTASIA and a good while before. The field of animating dinosaurs has been very nearly the exclusive province of dimensional animators such as Willis O'Brien, Ray Harryhausen, Jim Danforth, and David Allen. In spite of the principle that flat animation offers the artist greater freedom of expression--admittedly at the cost of some realism--it has very rarely been used for dinosaurs.
But I rather suspect that the real inspiration for THE LAND BEFORE TIME came from the dinosaur art of William Stout, particularly as seen in his 1982 book THE DINOSAURS (Bantam Books, $12.95). Unlike most dinosaur artists who try for a realistic,almost photographic style, Stout's art is more stylized and almost comic-book-like. He tries more for caricature rather than for realism and so is able to put more expression into his dinosaurs. I do not remember if Stout's name appeared in the credits of THE LAND BEFORE TIME, but several of the illustrations from THE DINOSAURS are dramatized in the film, including an appealing camarasaurus baby hatching from an egg. Another illustration shows ultrasauri standing among trees. The top of the painting cuts off the heads but a visual analogy between the necks and the treetrunks is implied. This scene also appeared in the film.
It is difficult to decide when THE LAND BEFORE TIME would have taken place. One needs to identify exactly what dinosaurs we are seeing. I have heard the main character, Littlefoot, described as a "brontosaurus." If that was what was intended then Bluth and company were hopelessly sloppy. Most of the dinosaurs in the film did not live until the late (or Upper) Cretaceous Period--sort of the grand finale of dinosaur life that brought into existence familiar dinosaurs such as triceratops and tyrannosaurus. That is entirely the wrong era for "brontosauri"--more accurately called apatosauri. But then, the dinosaurs of the film had more rounded faces than apatosauri would have had. They had the rounded faces that brontosauri used to be portrayed as having. Also, apatosaurus died out in the late Jurassic era; even camarasaurus, which outlasted them and lived only into the EARLY (or Lower) Cretaceous was not contemporary with late Cretaceous animals. Littlefoot could have been some lesser-known species of sauropod, some of which did live late enough to be contemporary with triceratops. The stegosaurus also is an Upper Jurassic dinosaur, but some of its similar- looking relatives could have been alive in the mid to late Cretaceous. Before you decide that the questions of era are trivial matters, realize that the Upper Cretaceous has been over for 70 million years and you can see how much animal life has changed since then. Go back another 70 million years and you are just about at the end of the Jurassic period with the Cretaceous soon to begin. "Bigmouth," incidentally, is a parasaurolophus who is respectably Upper Cretaceous. And what appeared to be a dimetrodon that should have died out 150 million years before this story was more likely an Upper Cretaceous spinosaurus. Stretching a point, all these dinosaurs could have been contemporaries, but it still seems unlikely.
Even more questionable is the geological background of the film. Yes, the Earth was geologically active at that time, but nowhere near as active as shown. In a short space we see the characters encountering powerful earthquakes and unrelated volcanos, and they struggle with a tar pit. Even in a geologically active era, this is entirely too much activity to be credible.
The story of THE LAND BEFORE TIME is about a baby sauropod of some sort--they call themselves "long-necks"--who is born in a time when food is scarce. His mother wants to take him, along with his grandparents, to the "great valley," where for some reason food is still plentiful in spite of the fact that every dinosaur and his brother is headed there or is already there. Along the way he meets a cute, pugnacious little girl triceratops, but the parents of both know that mixed species relationships rarely work out. Each gets the sort of reaction you would get if you told your parents you were really good friends with a sheep.
Through circumstances I will not describe, Littlefoot finds he must make the journey without his elders. Instead, a set of lost young dinosaurs band together to make the trip as group. The story could be reasonable but the characterization is not. The characters are not well-developed. Littlefoot goes through all kinds of tribulations without giving up, then suddenly at some point, gives up entirely: possible, but unlikely behavior. And if you doubt that real characterization can be done in an animated film, see WATERSHIP DOWN or THE PLAGUE DOGS.
THE LAND BEFORE TIME is a short film, even by animated film standards. It is just 66 minutes long--so short, in fact, that it is always shown with a cartoon, "Family Dog." The cartoon, by the way, is the first half of "Family Dog," the February 16, 1987, episode of Steven Spielberg's AMAZING STORIES television show. If it seems like it ends a trifle abruptly, that is why. If it means anything, they have chosen the best piece of the original broadcast. Apparently they could get the cartoon cheaply since Spielberg was one of the executive producers of THE LAND BEFORE TIME.
THE LAND BEFORE TIME does boast a nice score by James Horner and an okay song sung by Diana Ross. But it still does not show the effort of other animated feature films. And padding the length with part of an episode of AMAZING STORIES is no substitute for the kind of story-telling that Disney Studios or even Bluth himself have done in the past. Rate THE LAND BEFORE TIME a low +1 on the -4 to +4 scale.
Mark R. Leeper att!mtgzz!leeper<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
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Related links: index of all rec.arts.movies.reviews reviews


Why git_remote_branch?
git_remote_branch is a simple command-line tool that makes it very easy to manipulate branches published in shared repositories.
It achieves this goal by sticking to a few principles:
- keep grb’s commands extremely regular (they all look alike)
- support aliases for commands
- print all commands it runs on your behalf in red, so you eventually learn them
Another nice thing about git_remote_branch is that it can simply explain a command (print out all the corresponding git commands) instead of running them on your behalf.
Note: git_remote_branch assumes that the local and remote branches have the same name. Multiple remote repositories (or origins) are supported.
Installation
gem install git_remote_branch
If you’re on Windows, you can optionally install the following gems, to get color output:
gem install windows-pr win32console
Usage
Notes:
- parts between brackets are optional
- When ‘origin_server’ is not specified, the name ‘origin’ is assumed.
Available commands (with aliases):
Help
$ grb [-h|help] #=> Displays help
create (alias: new)
Create a new local branch as well as a corresponding remote branch based on the branch you currently have checked out. Track the new remote branch. Checkout the new branch.
$ grb create branch_name [origin_server]
publish (aliases: remotize, share)
Publish an existing local branch to the remote server. Set up the local branch to track the new remote branch.
$ grb publish branch_name [origin_server]
delete (aliases: destroy, kill, remove, rm)
Delete the remote branch then delete the local branch. The local branch is not deleted if there are pending changes.
$ grb delete branch_name [origin_server]
track (aliases: follow grab fetch)
Track an existing remote branch locally and checkout the branch.
$ grb track branch_name [origin_server]
rename (aliases: rn mv move)
Rename a remote branch and its local tracking branch. The branch you want to rename must be checked out.
# On branch to be renamed $ grb rename new_branch_name [origin_server]
explain
All commands can be prepended by the word ‘explain’. Instead of executing the command, git_remote_branch will simply output the list of commands you need to run to accomplish that goal. Examples:
$ grb explain create git_remote_branch version 0.3.0 List of operations to do to create a new remote branch and track it locally: git push origin master:refs/heads/branch_to_create git fetch origin git branch --track branch_to_create origin/branch_to_create git checkout branch_to_create
Explain your specific case:
$ grb explain create my_branch github git_remote_branch version 0.3.0 List of operations to do to create a new remote branch and track it locally: git push github master:refs/heads/my_branch git fetch github git branch --track my_branch github/my_branch git checkout my_branch
This, of course, works for each of the grb commands.
More on git_remote_branch
- Documentation: github.com/webmat/git_remote_branch#readme
- Code: github.com/webmat/git_remote_branch
- Bugs: github.com/webmat/git_remote_branch/issues
- News: programblings.com/category/git/git_remote_branch/
History
git_remote_branch in its current form was inspired by a script created by Carl Mercier and made public on his blog: No nonsense GIT, part 1: git-remote-branch
Contributors
- Mathieu Martin<EMAIL_ADDRESS>- Caio Chassot<EMAIL_ADDRESS>- Axelson github.com/axelson
- Carl Mercier github.com/cmer
Legalese
git_remote_branch is licensed under the MIT License. See the file LICENSE for details.
Supported platforms
This version of git_remote_branch has been tested with
- OS X Lion
- past versions were known to work on Linux and Windows
- Ruby 1.8.7, 1.9.2, 1.9.3
- git 1.7+
Let me know if you encounter problems running git_remote_branch with your platform.


{% extends "base.html" %} {% block title %}Page unavailable{% endblock title %} {% block content %}
Sorry, but the requested page is unavailable due to a server hiccup. This issue will be resolved asap.


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 starSign: Sagittarius height: 6'10" eyeColour: Brownish/Hazalish hairColour: Purpley/Pinky thinkLotrRules: Without a doubt ---
yes, lord of the rings does rule my friend, without a friggen doubt in my mind (can't wait till da third one comes out!) jaza mate, can't believe u've put so much work into this site....this is my first visit and i can honestly say that i am VERY impressed buddy - shit! looking forward to travelling with u next year man, gonna be awesome! if i could make one comment on ur site, u need a catogory for "he-bitch" on ur "status" title - coz hey, that's me! lol. all the best son of brian epstein, the famous beetles manager (lol - remember yr 8 with mr forrest?) - Davo.


Today we learned about Arrays and Hashes. I've programmed in python before, so I was familar with how to store information in both of them. However, the syntax is a little different from Python, and I had to learn the differences through trial and error.
Arrays and Hashes are basically just ways to store information. Arrays pair the information to a specific number (starting from 0, then going upwards to infinity from there), and Hashes pair the information to a keyword that the user assigns. It's great if you're trying to store password information or account information.
We actually use and call arrays for special characters. Every time you press alt + (a 3 digit number) in order to make a special character, you're accessing a specific id number for an array in order for your program to return a special character (like a copywrite symbol) to you.
Hashes are more for storing information that you want to remember. Specifically, they're easier to understand than arrays because rather than having information tied to a number, it's instead tied to a word-pair. You can keep a list of personal achievements, or perhaps list your favorite greeting and your favorite goodbye. Both hashes and arrays are quite useful!


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| Asymmetric Laplace Distribution {cubfits} | R Documentation |
Density, probability, quantile, random number generation, and MLE functions for the asymmetric Laplace distribution with parameters either in ASL(theta, mu, sigma) or the alternative ASL*(theta, kappa, sigma).
dasl(x, theta = 0, mu = 0, sigma = 1, log = FALSE) dasla(x, theta = 0, kappa = 1, sigma = 1, log = FALSE) pasl(q, theta = 0, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE) pasla(q, theta = 0, kappa = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) qasl(p, theta = 0, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE) qasla(p, theta = 0, kappa = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) rasl(n, theta = 0, mu = 0, sigma = 1) rasla(n, theta = 0, kappa = 1, sigma = 1) asl.optim(x)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
theta |
center parameter. |
mu, kappa |
location parameters. |
sigma |
shape parameter. |
log, log.p |
logical; if |
lower.tail |
logical; if |
The density f(x) of ASL*(theta, kappa, sigma) is given as sqrt(2) / sigma kappa / (1 + κ^2) exp(- sqrt(2) kappa / sigma |x - θ|) if x >= theta, and sqrt(2) / sigma kappa / (1 + κ^2) exp(- sqrt(2) / (sigma kappa) |x - θ|) if x < theta.
The parameter domains of ASL and ASL* are theta in real, sigma > 0, kappa > 0, and mu in real. The relation of mu and kappa are kappa = (sqrt(2 sigma^2 + mu^2) - mu) / sqrt(2 sigma) or mu = sigma / sqrt(2) (1 / kappa - kappa).
“dasl” and “dasla” give the densities, “pasl” and “pasla” give the distribution functions, “qasl” and “qasla” give the quantile functions, and “rasl” and “rasls” give the random numbers.
asl.optim
returns the MLE of data x
including
theta
, mu
, kappa
, and sigma
.
Wei-Chen Chen<EMAIL_ADDRESS>Kotz S, Kozubowski TJ, Podgorski K. (2001) “The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance.” Boston: Birkhauser.
## Not run: suppressMessages(library(cubfits, quietly = TRUE)) set.seed(1234) dasl(-2:2) dasla(-2:2) pasl(-2:2) pasla(-2:2) qasl(seq(0, 1, length = 5)) qasla(seq(0, 1, length = 5)) dasl(-2:2, log = TRUE) dasla(-2:2, log = TRUE) pasl(-2:2, log.p = TRUE) pasla(-2:2, log.p = TRUE) qasl(log(seq(0, 1, length = 5)), log.p = TRUE) qasla(log(seq(0, 1, length = 5)), log.p = TRUE) set.seed(123) rasl(5) rasla(5) asl.optim(rasl(5000)) ## End(Not run)


| create_loop {later} | R Documentation |
Normally, later uses a global event loop for scheduling and running functions. However, in some cases, it is useful to create a private event loop to schedule and execute tasks without disturbing the global event loop. For example, you might have asynchronous code that queries a remote data source, but want to wait for a full back-and-forth communication to complete before continuing in your code – from the caller's perspective, it should behave like synchronous code, and not do anything with the global event loop (which could run code unrelated to your operation). To do this, you would run your asynchronous code using a private event loop.
create_loop(parent = current_loop(), autorun = NULL) destroy_loop(loop) exists_loop(loop) current_loop() with_temp_loop(expr) with_loop(loop, expr) global_loop()
parent |
The parent event loop for the one being created. Whenever the
parent loop runs, this loop will also automatically run, without having to
manually call |
autorun |
This exists only for backward compatibility. If set to
|
loop |
A handle to an event loop. |
expr |
An expression to evaluate. |
create_loop
creates and returns a handle to a private event loop,
which is useful when for scheduling tasks when you do not want to interfere
with the global event loop.
destroy_loop
destroys a private event loop.
exists_loop
reports whether an event loop exists – that is, that it
has not been destroyed.
current_loop
returns the currently-active event loop. Any calls to
later()
or run_now()
will use the current loop by
default.
with_loop
evaluates an expression with a given event loop as the
currently-active loop.
with_temp_loop
creates an event loop, makes it the current loop, then
evaluates the given expression. Afterwards, the new event loop is destroyed.
global_loop
returns a handle to the global event loop.


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Represents a JDBC connection to a Microsoft SQL Server database.
Package: com.microsoft.sqlserver.jdbc
Implements: ISQLServerConnection, java.io.Serializable
Syntax
public class SQLServerConnection
Remarks
SQLServerConnection supports JDBC connection pooling and can be either a physical JDBC connection or a logical JDBC connection. SQLServerConnection manages transaction control for all statements that were created from it, and it can participate in XA distributed transactions managed via a XAResource adapter.
SQLServerConnection manages a pool of prepared statement handles. Prepared statements are prepared once and are typically run many times with different data values for their parameters. Prepared statements are also maintained across logical (pooled) connection closes.
This class supports unwrapping to SQLServerConnection class, java.sql.connection interface, and ISQLServerConnection interface. For more information, see Wrappers and Interfaces.


Hello,
always improving our products and services so that the
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I am very innovative, whenever I use an application, game, app, etc.,
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LOOKING FOR RICHARD A film review by Edwin Jahiel Copyright 1996 Edwin Jahiel
LOOKING FOR RICHARD *** 1/2 .Directed by Al Pacino. Narration written by Pacino & Frederic Kimball. Photography, Robert Leacock. Editing, Pasquale Buba, William A. Anderson & Ned Bastille. Music, Howard Shore. Produced by Michael Hadge and Al Pacino. Cast: Alec Baldwin (Clarence), Al Pacino (Richard III), Kevin Conway (Hastings), Kevin Spacey (Buckingham), Estelle Parsons (Margaret), Winona Ryder (Lady Anne) and Aidan Quinn (Richmond); interviews with Kenneth Branagh, Kevin Kline, James Earl Jones, Derek Jacobi, John Gielgud,Vanessa Redgrave and many others.A Fox Searchlight release.109 minutes.PG-13
Al Pacino's first movie as a director is an original, mesmerizing exploration. The Richard of the title is Shakespeare's character Richard III as well as the eponymous play. Pacino has made a documentary about a stage production-in-progress, one that involves himself as director and as actor in the title role, plus a host of other people: performers in this production as well as performers not in it, but who have experience with Shakespeare and opinions to share; writers; producers; consultants; scholars... the list is long. It even includes people casually met on the street and who are queried about Shakespeare or their lack of knowledge of the Bard. (Many amusing reactions occur).
You don't have to look closely to realize that this labor of love is a busman's holiday. It must have occupied free snatches of time over a period of years. There are clues to this, such as the changing looks of some participants, the presence of actress Viveca Lindfors who died in 1995, or the Pacino's hirsuteness that can differ from one scene to the next.
What Pacino and Company try to do is to unscramble Richard III by approaching it with as much understanding and as many insights as possible. The result is a crazy quilt that explains, elucidates, discusses, simplifies or sometimes complicates matters as they crop up. This may have been like haphazard patchwork so far as the filming goes, but the excellent and very hard editing has given hidden logic and relatively coherent structure to this fascinating chaos.
>From the start, the method in the madness becomes apparent. "Now is the Winter of our discontent" is analyzed and explained in terms of the War of the Roses, the Yorks and Lancasters. Later the iambic pentameter is illustrated (as a response to Pacino's falsely naive question) via amusing da-da-dee-dums.
A refrain pops up often, to the effect that "this is a very complicated play," which it is, not only for the director and the actors but for audiences, especially lay, what with the many characters in a dedalus of ambitions, real or false alliances, overt or covert enmities, and the maze of history as Shakespeare saw it. So far as I remember, the word "complex" is used either little or not at all, and rightly so: the play which is there are either few or no mentions of "complex," in the discussions, and rightly so.
The text is submitted to deconstruction, from which hang the interpretations ,so that Shakespeare becomes attainable and more "enjoyable" from artistic,esthetic,poetic,psychological and historical points of view. The result is that staggering genius of the Bard is better felt and comprehended yet, in a paradoxical way, even more mysterious. And even though sometimes scholars are pitted against players, there is a common ground of admiration for the playwright.
As Pacino says ( I imagine this applied to both actors and audiences), "You don't have to understand every word. You get the gist of it." This brings me to "Looking for Richard" as a film for non-native-English speaking audiences. It was shown at Cannes last spring,where it was complimented, but I have my doubts as to the comprehension of it via subtitles. The language of Shakespeare plus the vernacular of the discussions require linguistic familiarity. They make of "Looking" the opposite of silent movies and their universality through images and simple intertitles.
On the other hand, thanks to the back-and-forth shifts between stage, backstage and real life, the mechanisms of stagecraft are so unveiled that the effect is one pure Brechtian distanciation, reinforced (for some viewers) by Richard becoming a precursor of the villain in the parable by Bertolt Brecht "The Resistible Rise of Arturo Ui," in which a gangster gains power in Chicago.
There are several views of what the purpose of "Looking" is. One opinion is that Pacino, who has distinguished himself on the stage (including many performances as Richard), was anxious to prove his theatrical skills to the wide public that knows only the movie side of him. This may be the case, but would that such polite vanity were more common and result in many more "intellectual" and literate movies!
Another belief holds that "Looking" is an introduction to Shakespeare which, by stressing the humanity of the performers and demystifying "difficulties" makes the play accessible to neophytes. I cannot share this perception. It seems to me that a beginner, the street-person as it were, or a young student, may identify with or at least recognize certain universals in the Bard, but will not get the full impact. I believe that, willy-nilly, Pacino is talking to the already converted -- though not necessarily the specialists -- whose eyes will open more both to Shakespeare's work and to the actors'.
I also see "Looking" as a direct-approach (and subsequent choices in editing) work of the cinema-verite school, a documentary about a play that, in its day, was, in its action and plot (though not its language) a precursor of a "film noir," with its dark moods, twists, murders and treasons.
What we see of the "finished" Richard III is only a fraction of the stage-play. It comes to us in fragments, yet cleverly shows a number of telling, full episodes, such as the murders of Clarence and of Hastings or the killing of the Princes in the Tower -- all riveting, all powerfully suspenseful and touching.
Among the many bonuses of the film is its healthy discussion of the American feeling of inferiority vis-a-vis the British when it comes to doing Shakespeare, a feeling that lies a great deal in differences of accents and intonations. I only wish someone had mentioned that the performers of Shakespeare's day did not speak BBC or Oxbridgian English!
Here, not one actor falls into the trap of trying to sound ersatz-British, nor, happily, does anyone have a regional accent, whether from Brooklyn, the South, the West or New England. It all comes down to a convincing demonstration that Americans can deal with Shakespeare without having to tend apologies.
If anyone had been told that Pacino would employ familiar movie people (Kevin Spacey,Adan Quinn,Alec Baldwin, Winona Ryder and others), the first reaction would have been "How incongruous!" if not "How dumb!" Yet, in spite of this novelty of casting, those performers are surprisingly good and convincing. If nothing else, "Looking" revolutionizes our preconceptions, our expectations and widely-held beliefs that our own movie actors cannot shift from screen to stage the way the British have been doing for close to one century, to their advantage and to the benefit of both media.
The experienced Pacino plays with a great deal of nuances as he searches for "his" Richard. Sometimes, like the movie Pacino (say, the son of Godfather Brando), he is hollow-eyed, contained and menacingly quiet. But often too he reclaims his Italian-American ancestry and the passion that goes into flamboyant outbursts.
The atmosphere of the film is one of fascinating peeps into the collaborative nature of plays. This is not devoid of humor. For example, Kevin Spacey, whether as a civilian or in costume, maintains a delightful look of skepticism or irony. Pacino shows a big, heavy book, "The Annotated Shakespeare" and puns on "The Anointed Shakespeare." And when King Edward finally dies, he lies in state with coins over his eyes, like a Mafioso Capo.
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


Name your cohort
Give your group of apprentices (your cohort) a name or reference, so you can identify it in the future.
Your training provider will also be able to see this.
Give your group of apprentices (your cohort) a name or reference, so you can identify it in the future.
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Nyzo version 588 (commit on GitHub) removes the Micropay run modes. It also updates the initialization process of BlockManager.
This version affects the verifier, sentinel, and client. It also removes two run modes.
This is not a critical update. It contains one optimized method that reduces memory usage, especially if the block-file consolidator is in use. However, its overall importance is more in preparation for future improvements in the block manager. Updating to this version is unnecessary if you do not find it interesting.
In BlacklistManager, the check of whether BlockManager has completed initialization has been renamed. Previously, there was a single field in BlockManager that was used both as a guard for entering the initialization process and a flag to indicate that initialization had completed. That field has been split into two separate fields for those two purposes.
In BlockManager, the initialized field was replaced with two separate fields: startedInitialization and completedInitialization. This was done to allow method synchronization to be removed from the initialize() method.
Also, the static block has been removed. This is a significant, far-reaching change. This change has been fully tested for the verifier, sentinel, and client, but it may still have some unintended effects on scripts. This will continue to be reviewed.
The isInitialized() method has been renamed to completedInitialization() for consistency with the new field name.
The writeBlocksToFile() method has been rewritten to reduce peak memory usage. Instead of assembling the file in memory and writing it in a single pass, a RandomAccessFile is used to write individual blocks and balance lists as they are serialized. The method still sorts blocks ascending on height, and it still makes a map of provided balance lists for lookup.
The number of blocks is still written as a short (16-bit integer), followed by the blocks. Each block that requires a balance list is followed by the appropriate balance list.
The file is written to a temporary location. After the file is written successfully, it is moved to the expected location, replacing the existing file. This atomic replacement ensures that invalid block files will never be present at their expected locations.
A minor appearance improvement, Arrays.asList() calls were replaced with Collections.singletonList() calls in BlockManager.freezeBlock(). The completedInitialization field is now set instead of the initialized field when the Genesis block is frozen. This case only happens
The BlockManager.initialize() method is no longer synchronized. This method was already mostly thread-safe, and the use of a separate AtomicBoolean field to control entry into the critical section made the method fully thread-safe without method synchronization.
Initialization time is now displayed. The initialization of BlockManager is a significant part of the overall initialization of the 3 run modes that track the blockchain, and it is an important target for future optimization.
In BlockManagerMap, a ConcurrentHashMap is now used to allow synchronization to be removed from the addBlock() method.
In BlockVoteManager, the number of blocks for which votes are retained has been turned into a constant. Other inconsequential cleanup was also performed.
Consideration of the MicropayServer run mode was removed from the Message class. The Micropay server and Micropay client were eliminated so that Micropay efforts can be focused on development of the client and the browser extensions.
Synchronization was removed from NewVerifierQueueManager.updateVote() method. It was already thread-safe.
The MicropayClient and MicropayServer run modes were deleted.
The BlockManager.initialize() method is now called immediately after setting the run mode for the 3 run modes that use the BlockManager.
The Client also initializes the BlockManager explicitly.
The MicropayAuthorization class was removed. A class with similar functionality will be needed for the demonstration of the new browser-extension Micropay functionality, but this will be implemented as a clean, purpose-built solution.
The MicropayClient class was removed. This class was the entry point for the Micropay client application, which ran locally to allow in-browser payments to be made. While this was an exceptionally interesting application, it was never widely used.
The MicropayController class, which supported the MicropayServer, was also removed. The old Micropay server tracked the blockchain, serving as a bridge between the web and the Nyzo mesh. New Micropay functionality will not have any direct communication with the Nyzo mesh. Instead, all Micropay functionality will use a Nyzo client as an intermediary to the Nyzo mesh.
The MicropayEndpoint class was also removed. This class managed Micropay authorizations and delivered web content.
The MicropayServer class, the entry point to the Micropay server application, was removed.
The TransactionTracker followed the blockchain to monitor incorporation of Micropay transactions.
The Sentinel class must now explicitly initialize BlockManager.
In WebListener, consideration of the Micropay run modes has been removed.


Problem: With the growing interest around a potential Mars colony it seems clear that there is a high chance one is established in the near future. Given that, how can we model the population growth of a Martian colony?
Assumptions:
Let's rule out exponential for our model because we can assume that there will be a limit on the size of a Mars colony just due to habitable space and technological reasons.
from __future__ import print_function, division
import numpy as np
import math
import random
import matplotlib.pyplot as plt
from decimal import Decimal as D
from scipy.interpolate import CubicSpline
def logistic(r,K,t):
''' This is the standard logistic function, with a fixed carrying capacity'''
num = np.zeros(t+1)
num[0] = 1
for i in range(t):
num[i+1] = num[i]+r*num[i]*(1-num[i]/K)
return num
#Define our logistic model parameters for our population
r = .1 # growth rate / yr
K = 100 # carrying capacity
t = 100 # number of years
plotData = logistic(r,K,t)
plt.plot(range(t+1),plotData, 'b')
plt.xlabel('Year')
plt.ylabel('Number')
plt.title('Logistic Population Growth\n')
plt.show()
Well, that seems reasonable enough.
Except for one issue.
Given that Mars is not actually habitable at all right now, and the number of people who can inhabit Mars will be hard capped by the size of artificial habitats and eventually the extent to which it is terraformed.
That is to say the carrying capacity will actually be dynamically influenced by technology.
As Meyer and Ausubel conclude, we actually need our K to start at some initial potential for technology.
Analytically solving yields:
def KLogistic(rK,k1,kMax,t,tMid):
'''
This is the adjusted logistic function for dynamic carying rate which gives us the sigmoidal
carrying capacity between k1 and kMax
'''
num = np.zeros(t+1)
num[0] = k1
rK = round(rK,7)
tMid = round(tMid,7)
for i in range(int(t)):
num[i+1] = k1+(kMax/(1+math.exp(-rK*(i-(t/tMid)))))
return num
def logisticK(r,numK,t):
'''
A revised logistic function that will take into account a dynamic carrying capacity
Of note, it does not care how the numK array is produced, but here we are primarly using
a mofidied logistic that acts sigmoidally between an intial K carrying capacity and a KMax
'''
r = round(r,7)
num = np.zeros(t+1)
num[0] = 1
for i in range(t):
num[i+1] = num[i]+r*num[i]*(1-num[i]/numK[i])
return num
#Define our logistic model parameters for our population
r = .25 # growth rate / yr
K = 100 # carrying capacity
t = 100 # number of years
#Define our parameters for the logisticlly modeled
#dynamic carrying capacity
rK = .30 #adoption/diffusion rate of the technology
k1 = 50 #the initial level of carrying capacity provided
kMax = 100 #the max possible carrying capacity provided by new tech
kLog = KLogistic(rK,k1,kMax,t,2)
plotDataK = logisticK(r,kLog,t)
plt.plot(range(t+1),plotDataK, 'b')
plt.plot(range(t+1),kLog, 'r')
plt.xlabel('Year')
plt.ylabel('Number')
plt.title('Logistic Population growth with a logisticlly modeled Carrying Capacity \n')
plt.show()
earlyHistDates = np.arange(0,1700,100)
earlyPopData = np.array([188,195,202,205,209,210,213,226,240,
269,295,353,393,392,390,461,554])
earlyPopData = earlyPopData*1000
midHistDates = np.arange(1700,1900,50)
midPopData = np.array([603,814,989,1263])
midPopData = midPopData*1000
curHistDates = np.arange(1900,1950,10)
curPopData = np.array([1654,1777,1912,2092,2307])
curPopData = curPopData*1000
modHistDates = np.arange(1950,2016,1)
modPopData = np.array([2525149,2571868,2617940,2664029,2710678,2758315,2807246,
2857663,2909651,2963216,3018344,3075073,3133554,3194075,
3256989,3322495,3390686,3461343,3533967,3607866,3682488,
3757735,3833595,3909722,3985734,4061399,4136542,4211322,
4286282,4362190,4439632,4518602,4599003,4681211,4765658,
4852541,4942056,5033805,5126633,5218978,5309668,5398329,
5485115,5570045,5653316,5735123,5815392,5894155,5971883,
6049205,6126622,6204311,6282302,6360765,6439842,6519636,
6600220,6681607,6763733,6846480,6929725,7013427,7097500,
7181715,7265786,7349472
])
histData = np.concatenate((earlyPopData,midPopData,curPopData,modPopData))
histRange = np.concatenate((earlyHistDates,midHistDates,curHistDates,modHistDates))
plt.plot(histRange,histData, 'bo')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 0-2016 \n')
plt.show()
Well, this looks awfully exponential.
plt.plot(histRange,histData, 'bo')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 0-2016 \n')
plt.yscale('symlog', linthreshy=0.5)
plt.show()
This is looking better, we now can see what looks like the first pulse and then the second explosion of population growth.
Let's zoom in a bit
plt.plot(histRange[0:19:1],histData[0:19:1], 'b')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 0-1800\n')
plt.show()
plt.plot(histRange[19:histRange.size:1],histData[19:histData.size:1], 'b')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 1800-2016\n')
plt.show()
plt.plot(histRange,histData, 'bo')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 0-2016 \n')
plt.yscale('symlog', linthreshy=0.5)
plt.show()
But what about the future?
It is reasonable to take the assumption that the world population will be limited by the planet's resources despite technology.
And the UN population predictions for future population growth backs this.
midFutureDates = np.arange(2015,2101,1)
midFutureData = np.array([7349472,7432663,7515284,7597176,7678175,7758157,
7837029,7914764,7991397,8067008,8141661,8215349,
8288055,8359824,8430712,8500766,8570000,8638417,
8706032,8772860,8838908,8904177,8968662,9032348,
9095213,9157234,9218401,9278694,9338073,9396486,
9453892,9510268,9565600,9619870,9673057,9725148,
9776138,9826020,9874772,9922372,9968809,10014085,
10058223,10101266,10143272,10184290,10224341,10263447,
10301668,10339074,10375719,10411635,10446830,10481298,
10515022,10547989,10580207,10611689,10642429,10672418,
10701653,10730139,10757884,10784884,10811134,10836635,
10861387,10885409,10908740,10931432,10953525,10975033,
10995954,11016299,11036070,11055270,11073895,11091939,
11109392,11126238,11142461,11158037,11172942,11187147,
11200617,11213317
])
histData = np.concatenate((earlyPopData,midPopData,curPopData,modPopData,midFutureData))
histRange = np.concatenate((earlyHistDates,midHistDates,curHistDates,modHistDates,midFutureDates))
plt.plot(histRange,histData, 'bo')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population 0-2016 \n')
plt.yscale('symlog', linthreshy=0.5)
plt.show()
earlyJpDate = np.arange(400,1800,100)
earlyJpPop = np.array([1500000,2000000,4000000,5000000,6000000,7000000,7000000,7000000,
6000000,7000000,8000000,8000000,12000000,28000000]) #braben
mejJpDate = np.array([1721,1726,1732,1744,1750,1756,1762,1768,1774,1780,1786,1792,1798,1804,1822,
1828,1834,1840,1846,1870])
mejJpPop = np.array([30496900,31104400,31498500,30599500,30323900,30502700,30328100,
30714900,30408800,30432400,29351200,29123000,29801100,29977690,
31124500,31825600,31664800,31102100,31481900,34620000
]) #braben
impJpDate = np.arange(1872,1921,1)
impJpPop = np.array([
33110796,33300644,33625646,33997415,34338367,34628328,34898540,35768547,
35929023,36358955,36700079,37017262,37451727,37868949,38151217,38507177,
39069691,39607234,40072020,40453461,40718677,41089940,41388313,41813215,
42270620,42708264,43228863,43763855,44270495,44825597,45446369,46041768,
46732876,47219566,47678396,48164761,48819630,49588804,50254471,50984844,
51753934,52522753,53362682,54142441,54935755,55637431,56335971,56667711,
57233906
])
midJpDate = np.arange(1920,1955,5)
midJpPop = np.array([55963,59737,64450,69254,71933,72147,82200])
midJpPop = midJpPop*1000
modHistDates = np.arange(1951,2016,1)
modJPop = np.array([83794,85175,86378,87439,88390,89262,90085,
90883,91682,92501,93357,94264,95228,96253,97342,
98495,99711,100989,102324,103708,105143,106617,
108086,109495,110805,111993,113068,114055,114993,
115912,116822,117709,118552,119319,119989,120551,
121022,121433,121831,122249,122703,123180,123659,
124102,124483,124795,125048,125266,125481,125715,
125974,126250,126524,126773,126979,127137,127250,
127318,127341,127320,127253,127140,126985,126795,
126573
])
modJPop = modJPop*1000
jpData = np.concatenate((earlyJpPop,mejJpPop,impJpPop,midJpPop,modJPop))
jpRange = np.concatenate((earlyJpDate,mejJpDate,impJpDate,midJpDate,modHistDates))
plt.plot(jpRange,jpData, 'go')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Japan Population 400-2015\n')
plt.show()
Okay, this looks more promising to our initial hypothesis! Let's tune our parameters to the data set and then we can do some extrapolation as to how a Mars Population might grow!
#Define our logistic model parameters for our population
r = .05 # growth rate / yr
t = 815 # number of years
#Define our parameters for the logisticlly modeled
#dynamic carrying capacity
rK = .05 #adoption/diffusion rate of the technology
k1 = 41000000 #the initial level of carrying capacity provided
kMax = 130000000-k1 #the max possible carrying capacity provided by new tech
kLog = KLogistic(rK,k1,kMax,t,1.2)
plotDataK = logisticK(r,kLog,t)
x_values = np.linspace(400, 2015, 816)
plt.plot(jpRange,jpData, 'go',label="JP Pop")
plt.plot(x_values,plotDataK, 'b',label="LogK Function")
plt.plot(x_values,kLog, '--r',label="K")
plt.legend(loc='upper left')
#set axes correctly
axes = plt.gca()
axes.set_xlim([400,2016])
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Japan Population 400-2015\n')
plt.show()
Okay, We are getting the right shape here, but it looks like we have a chunk of leading data we don't need Let's try from say 1200-Present
#Define our logistic model parameters for our population
r = .04 # growth rate / yr
t = 815 # number of years
#Define our parameters for the logisticlly modeled
#dynamic carrying capacity
rK = .05 #adoption/diffusion rate of the technology
k1 = 27000000 #the initial level of carrying capacity provided
kMax = 129300000-k1 #the max possible carrying capacity provided by new tech
tM = 1.1
kLog = KLogistic(rK,k1,kMax,t,tM)
plotDataK = logisticK(r,kLog,t)
#set axes correctly
axes = plt.gca()
axes.set_xlim([1200,2016])
axes.set_ylim([7000000,130000000])
#adjust our values for our offset axes
plotDataK = plotDataK+7000000
kLog = kLog+7000000
x_values = np.linspace(1200, 2015, 816)
plt.plot(jpRange,jpData, 'g',label="JP Pop")
plt.plot(x_values,plotDataK, 'b',label="LogisticK")
plt.plot(x_values,kLog, '--r',label="K")
plt.legend(loc='upper left')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Japan Population 1200-2015\n')
plt.show()
Now let's run some Monte Carlo Simulation on it to see how it can be improved with some better parameters!
So in order to do any least squares regression on our data and tune our parameters, we need to rectify some gaps in our data.
For instance, we only have population data every 100 years from 400-17000.
Sounds like a pretty decent spot to use Cubic Splines for interpolation. In this case, we want natural boundary conditions as well for the most part.
cubicRangeJpEarly = np.arange(400,1720,1)
cs = CubicSpline(earlyJpDate,earlyJpPop,bc_type='natural')
plt.plot(earlyJpDate, earlyJpPop, 'o', label='data')
plt.plot(cubicRangeJpEarly, cs(cubicRangeJpEarly), label="S")
plt.legend(loc='upper left')
plt.show()
cubicFitJpEarly = np.empty(cubicRangeJpEarly.size)
for i in range(cubicRangeJpEarly.size):
cubicFitJpEarly[i] = cs(cubicRangeJpEarly[i])
#ok, a little more work required here as we don't have a set interval in this period, but we can work with it
cubicRangeMej = np.arange(1720,1872,1)
csMej = CubicSpline(mejJpDate,mejJpPop,bc_type=('not-a-knot','natural'))
plt.plot(mejJpDate, mejJpPop, 'o', label='data')
plt.plot(cubicRangeMej, csMej(cubicRangeMej), label="S")
plt.legend(loc='upper left')
plt.show()
cubicFitJpMej = np.empty(cubicRangeMej.size)
for i in range(cubicFitJpMej.size):
cubicFitJpMej[i] = csMej(cubicRangeMej[i])
cubicRangeJpMid = np.arange(1921,1951,1)
csJpMid = CubicSpline(midJpDate,midJpPop,bc_type='natural')
plt.plot(midJpDate, midJpPop, 'o', label='data')
plt.plot(cubicRangeJpMid, csJpMid(cubicRangeJpMid), label="S")
plt.legend(loc='upper left')
plt.show()
cubicFitJpMid = np.empty(cubicRangeJpMid.size)
for i in range(cubicFitJpMid.size):
cubicFitJpMid[i] = csJpMid(cubicRangeJpMid[i])
np.set_printoptions(threshold=np.inf)
jpData = np.concatenate((cubicFitJpEarly,cubicFitJpMej,impJpPop,cubicFitJpMid,modJPop))
jpRange = np.concatenate((cubicRangeJpEarly,cubicRangeMej,impJpDate,cubicRangeJpMid,modHistDates))
x_values = np.linspace(1200, 2015, 816)
plt.plot(jpRange,jpData, 'b', label='Data')
plt.plot(x_values,plotDataK, 'g', label='LogisticK')
plt.plot(x_values,kLog, '--r', label='K')
plt.legend(loc='upper left')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Japan Population 400-2015\n')
axes = plt.gca()
axes.set_xlim([1200,2050])
axes.set_ylim([7000000,140000000])
plt.show()
def r2(y_fit,y):
res = np.sum((y-y_fit)**2)
sumSq = np.sum((y-np.mean(y))**2)
rSq = 1-(res/sumSq)
return rSq
print(r2(plotDataK[0:816],jpData[800:1616]))
0.944364149406
Well how about that! It looks like our function matches fairly well! Though we still have a fair bit of room for improvement, to Monte Carlo we go!
We're actually going to use Simulated Annealing, which is a type of Monte Carlo that allows for finding Min/Max values of a function, in this case we want to minimize 1-r2
In our monte carlo simulation, we want to tune the parameters r, rK, and tM and our fitness function will be r2.
Note: We ran our Simulated Annealing algorithm for each of r, rK, and tM. Below only shows the result after running the final SA for tM after obtaining good r and rK values in prior sims.
def cost(y_fit,y):
res = np.sum((y-y_fit)**2)
sumSq = np.sum((y-np.mean(y))**2)
rSq = 1-(res/sumSq)
return 1-rSq
#Define our starting parameters for our population
r = 37/1000 # growth rate / yr
rK = 5/100 #adoption/diffusion rate of the technology
tM = 1115/1000
#some neccesary constants
k1 = 26000000 #the initial level of carrying capacity provided
kMax = 129300000-k1 #the max possible carrying capacity provided by new tech
t = 815 # number of years
numSims = 1000 #number of simulations to test
#our baseline to compare against
baseline = jpData[800:1616]
#Monte Carlo/Simulated Annealing functions
def AcceptNeighbor(current, neighbor, curTemp):
curFit = cost(current,baseline)
nFit = cost(neighbor,baseline)
if(nFit<curFit):
return True
else:
#pAccept = math.exp((1-(r2(neighbor,baseline)))-(1-r2(current,baseline))/curTemp)
pAccept = math.exp(-(nFit-curFit)/curTemp)
if pAccept>1:
pAccept = 1
if random.uniform(0, 1) < pAccept:
return True
return False
def CalculateTemp(step,nSteps):
initialTemp = 1
gamma = 5.0
return initialTemp*(1-gamma/nSteps)**step
def GenerateNeighbor(currentParms,modParm):
#we will randomly adjust our tM
#up or down by a small amount
neighbor = currentParms[:]
plusMin = random.randint(0,1)
scale = random.randint(1,100)
if(plusMin == 1):
neighbor[modParm] += 0.001*scale
else:
neighbor[modParm] -= 0.001*scale
return neighbor
def generateFromParms(parms):
r = parms[0]
rK = parms[1]
tM = parms[2]
kLog = KLogistic(rK,k1,kMax,t,tM)
return logisticK(r,kLog,t)
def simulatedAnnealling(currentParms,nSteps,mod):
mvCount = 0
bestParms = 0;
bestR2 = 0;
for i in range(1,nSteps):
neighborParms = GenerateNeighbor(currentParms,mod)
currentTemp = CalculateTemp(i,nSteps)
current = generateFromParms(currentParms)
neighbor = generateFromParms(neighborParms)
if AcceptNeighbor(current,neighbor,currentTemp):
mvCount +=1
currentParms = neighborParms
return currentParms
initParms = [r,rK,tM]
fitParms = simulatedAnnealling(initParms,numSims,2)
newR = fitParms[0]
newRK = fitParms[1]
newTM = fitParms[2]
kLog = KLogistic(newRK,k1,kMax,t,newTM)
newFit = logisticK(newR,kLog,t)
x_values = np.linspace(1200, 2015, 816)
#adjust our values for our offset axes
newFit = newFit+7000000
kLog = kLog+7000000
plt.plot(jpRange,jpData, 'b', label='Data')
plt.plot(x_values,newFit, 'g', label='NewFit')
plt.plot(x_values,kLog, '--r', label='K')
plt.legend(loc='upper left')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Japan Population 400-2015\n')
axes = plt.gca()
axes.set_xlim([1200,2050])
axes.set_ylim([7000000,140000000])
plt.show()
print(fitParms)
print(r2(newFit[0:816],jpData[800:1616]))
[0.037, 0.05, 1.148000000000003] 0.992364508719
Well, It looks like this is about as good of a function as we can get. Now that we have an understanding of what the growth of a confined, technology dependent population would be, we can take this and extrapolate to a Martian colony.
For the Martian Colony let's take 1000 years of growth to start.
marsT = 1000
newR = 0.037
newRK = 0.05
newTM = 1.148
kLog = KLogistic(newRK,k1,kMax,marsT,newTM)
newFit = logisticK(newR,kLog,marsT)
plt.plot(range(marsT+1),newFit, 'rx')
plt.plot(range(marsT+1),kLog, '--b', label='K')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Mars Colony Population 0-1000 MA\n')
plt.show()
So what could this possible be taken to represent? Let's focus on the two pulses in the logistic curve that we see. The first pulse can be understood as the intial population boom on Mars once the early pioneers have established a self sustaining colony
Then, what of the second pulse? Well, clearly it should be taken as a huge technology leap. The obvious technology here - terraforming
Terraforming would probably take much longer to take effect, so let's adjust our parameters and time scale a bit
marsT = 10000
kLog = KLogistic(newRK*.2,k1*.2,kMax,marsT,newTM)
newFit = logisticK(newR*.2,kLog,marsT)
plt.plot(range(marsT+1),newFit, 'r')
plt.plot(range(marsT+1),kLog, '--b', label='K')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Mars Colony Population 0-1000 MA\n')
plt.show()
Ok, that seems a bit more like it!
We don't have any real idea of how long terraforming mars will take, but as you can see, once we have a better idea of that timeframe, we can adjust some of our parameters and then we can get a picture of the population growth.
But what of the carrying capacity post terraform?
Well, Mars has roughly the surface area of earth. But we should also consider the emergence of oceans once terraformed. Rough estimates place the ancient Martian Ocean at about a third of the planet's surface.
Let's combine that, with the assumption that Mars will be less densely populated that Earth, and mark the carrying capacity at one third of Earth's.
marsT = 10000
marsKMax =<PHONE_NUMBER>*(1/3)
kLog = KLogistic(newRK*.2,k1*.2,marsKMax,marsT,newTM)
newFit = logisticK(newR*.2,kLog,marsT)
plt.plot(range(marsT+1),newFit, 'r')
plt.plot(range(marsT+1),kLog, '--b', label='K')
#plt.yscale('log')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Mars Colony Population 0-10000 MA\n')
plt.show()
Let's zoom in on the first portion here for some clarity
x_values = np.linspace(0, 8000, 8000)
plt.subplot()
plt.plot(x_values,newFit[0:8000], 'r')
plt.plot(x_values,kLog[0:8000], '--b', label='K')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Mars Colony Population 0-8000 MA\n')
plt.show()
x_values = np.linspace(8000, 10000, 2000)
print(newFit.size)
plt.plot(x_values,newFit[8000:10000], 'r')
plt.plot(x_values,kLog[8000:10000], '--b', label='K')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Mars Colony Population 8000-10000 MA\n')
plt.show()
10001
marsT = 10000
marsKMax =<PHONE_NUMBER>*(1/3)
kLog = KLogistic(newRK*.2,k1*.2,marsKMax,marsT,newTM)
newFit = logisticK(newR*.2,kLog,marsT)
plt.plot(range(marsT+1),newFit, 'r')
plt.plot(range(marsT+1),kLog, '--b', label='K')
plt.xlabel('Year')
plt.ylabel('Population')
plt.xscale('log')
plt.yscale('log')
plt.title('Mars Colony Population 0-10000 MA\n')
plt.show()


| Title | Author | Author l-f | Additonal Authors | ISBN | ISBN13 | ACM Rating | Average Rating | Publisher | Binding | Number of Pages | Year Published | Original Publication Year | Date Added | Private Notes | Recommended For | Owned Copies |
| In Search of Clusters: The Coming Battle in Lowly Parallel Computing | Gregory F. Pfister | Pfister, Gregory F. |<PHONE_NUMBER> |<PHONE_NUMBER>257 | 0 | 0 | Prentice Hall | Paperback | 415 | 1995 | 1995 | 4/11/16 | 1 |


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CRASHR
Stop looking. Start finding.
Find events to crash. And host yours.
End the lonely life.


My Farms
Here is a list of your farms and the main crop/p>
| ID | Location | Main Crop |
|---|---|---|
| {{ farm.id }} | {{ farm.location }} | {{ farm.crop }} |
{% extends 'base.html' %} {% load static %} {% block title %}Home Page{% endblock %} {% block content %} {% if farms %}
Here is a list of your farms and the main crop/p>
| ID | Location | Main Crop |
|---|---|---|
| {{ farm.id }} | {{ farm.location }} | {{ farm.crop }} |
You have no farms registered yet!
Click Here to Register a Farm {% endif %} {% endblock %}


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Easy questions like what you do for a living... where you are from... where you went to school...
Fun questions like who is your favorite sports team — what's the best restaurant in town — or what music you would have on a desert island.
Tough questions like how are you going to meet your production quotas with less budget, staff, and without new equipment?
Arming yourself with more information gives you more options. You can see new possibilities and find new ways to get where you want to be.
Sometimes you just need some moral support. We have been there... Knowing someone else has been down the same road is sometimes all you need to push into new territory.
If you had a group of experts at your disposal, available to answer any question you might have, would you step up to the plate and ask?
It doesn't even have to be manufacturing related.
You are welcome to any of the information we are allowed to share, our ideas, our opinions and advice about any issues you may be facing.
Anything — from our unbiased take on software you may be considering — a second opinion on an estimate — help with a design. Pretty much anything you can throw at us.*
*Sure, there are some limits, obviously we can't do an entire project for you out of the goodness of our hearts. What we can offer is some of our time to help you. Think of it as our version of Office Hours.
Answers are free of charge, hassle, and pressure
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HorseFly is a a delivery system where a moving truck (with packages) and a drone are autonomously coordinating to deliver the packages to the customer homes in approximately minimum time.
The algorithm uses OPTICS density clustering and kdTree based nearest neighbour search.
● Click on Draw Path to draw a delivery path.
● Click on Draw Homes to draw homes for package delivery.
● Click on Run! to run the algorithm and see the animation.
Draw Path Draw Homes Run! Reset
Computational Geometry project, by Ankit Aggarwal


imdecode Reads an image from a buffer in memory
img = cv.imdecode(buf)
img = cv.imdecode(buf, 'OptionName',optionValue, ...)
Input
- buf Input byte array of an encoded image (
uint8
vector).
Output
Options
- Unchanged If set, return the loaded image as is (with alpha channel,
otherwise it gets cropped). Both the depth and number of channels are
unchanged as determined by the decoder. default false
- AnyDepth If set, return 16-bit/32-bit image when the input has the
corresponding depth, otherwise convert it to 8-bit. default false
- AnyColor If set, the image is read in any possible color format.
default false
- Color If set, always convert image to the 3 channel BGR color image.
default true
- Grayscale If set, always convert image to the single channel grayscale
image. default false
- Flags Advanced option to directly set the flag specifying the depth
and color type of a loaded image. Note that setting this integer flag
overrides all the other flag options. Not set by default:
>0
: Return a 3-channel color image. Note that in the current
implementation the alpha channel, if any, is stripped from the
output image. For example, a 4-channel RGBA image is loaded as
RGB if Flags >= 0
.
=0
: Return a grayscale image
<0
: Return the loaded image as is (with alpha channel if present).
- FlipChannels in case the output is color image, flips the color order
from OpenCV's BGR/BGRA to MATLAB's RGB/RGBA order. default true
The function reads an image from the specified buffer in the memory. If
the buffer is too short or contains invalid data, an error is thrown.
See cv.imread for the list of supported formats and flags description.
Note
In the case of color images, the decoded images will have the channels
stored in BGR order. If FlipChannels
is true, the order is flipped to
RGB.


Date: April 12, 2018
Time: 2:00pm ET / 11:00am PT
Like many other SaaS companies, Pendo has been actively working to ensure GDPR compliance. Over the last six months, a dedicated team at Pendo has put processes in place that adhere to the new regulations. As GDPR is set to take effect on May 25, 2018, we want to give you an update on our efforts and answer questions you have about Pendo data and the new regulations.
If you have questions about GDPR and how it may affect your organization, please check out our help article and blog on the topic. If you would like to learn more outside of the webinar, please reach out to your Customer success manager at CSM@pendo.io.


Toolkits > SampleDNSPacketDPDKSource 2.0.0 > sample > LiveDNSPacketDPDKSourceBasic.spl
composite LiveDNSPacketDPDKSourceBasic { param expression<int32> $nicPort: (int32)getSubmissionTimeValue("nicPort", "0" ); expression<int32> $nicQueue: (int32)getSubmissionTimeValue("nicQueue", "0" ); expression<float64> $timeoutInterval: (float64)getSubmissionTimeValue("timeoutInterval", "10.0" ); type DNSMessageType = uint64 packetNumber, // sequence number of packet float64 captureTime, // time that packet was captured, in seconds since Unix epoch rstring ipSourceAddress, // IP source address rstring ipDestinationAddress, // IP destination address boolean isResponse, rstring responseCode, rstring questionName, rstring questionType, list<rstring> answerNames, list<uint32> answerTTLs, list<rstring> answerData, list<rstring> nameserverNames; FlattenedDNSMessageType = uint64 packetNumber, // sequence number of packet rstring flattenedDNSMessage; graph ( stream<DNSMessageType> DNSMessageStream ; stream<FlattenedDNSMessageType> FlattenedDNSMessageStream )= DNSPacketDPDKSource() { logic state: { map<uint16,rstring> dnsTypes = { 1: "A", 2: "NS", 3: "MD", 4: "MF", 5: "CNAME", 6: "SOA", 7: "MB", 8: "MG", 9: "MR", 10: "NULL", 11: "WKS", 12: "PTR", 13: "HINFO", 14: "MINFO", 15: "MX", 16: "TXT", 28: "AAAA" }; map<uint8,rstring> dnsResponseCodes = { 0: "OK", 1: "Format Error", 2: "Server Failure", 3: "Name Error", 4: "[not implemented]", 5: "Server Refused" }; } param lcoreMaster: 0; // run the master thread on core 0. lcore: 1; // run this ingest thread on core 1. nicPort: $nicPort; // bind to specified NIC port nicQueue: $nicQueue; // bind to specified NIC queue promiscuous: true; metricsInterval: 0.0; output DNSMessageStream: packetNumber = packetsProcessed(), captureTime = (float64)CAPTURE_SECONDS() + (float64)CAPTURE_MICROSECONDS() / 1000000.0, ipSourceAddress = convertIPV4AddressNumericToString(IPV4_SRC_ADDRESS()), ipDestinationAddress = convertIPV4AddressNumericToString(IPV4_DST_ADDRESS()), isResponse = DNS_RESPONSE_FLAG(), responseCode = DNS_RESPONSE_CODE() in dnsResponseCodes ? dnsResponseCodes[DNS_RESPONSE_CODE()] : (rstring)DNS_RESPONSE_CODE(), questionName = DNS_QUESTION_NAME(), questionType = DNS_QUESTION_TYPE() in dnsTypes ? dnsTypes[DNS_QUESTION_TYPE()] : (rstring)DNS_QUESTION_TYPE(), answerNames = DNS_ANSWER_NAMES(), answerTTLs = DNS_ANSWER_TTLS(), answerData = DNS_ANSWER_DATA(), nameserverNames = DNS_NAMESERVER_NAMES(); FlattenedDNSMessageStream: packetNumber = packetsProcessed(), flattenedDNSMessage = DNS_ALL_FIELDS("\n", "|", "^"); } () as DNSMessageSink = FileSink(DNSMessageStream) { param file: "debug.LiveDNSPacketDPDKSourceBasic.DNSMessageStream.out"; format: txt; hasDelayField: true; flush: 1u; } () as FlattenedDNSMessageSink = FileSink(FlattenedDNSMessageStream) { param file: "debug.LiveDNSPacketDPDKSourceBasic.FlattenedDNSMessageStream.out"; format: csv; hasDelayField: false; quoteStrings: false; flush: 1u; } () as Timeout = Custom() { logic onProcess: { block($timeoutInterval); log(Sys.info, "shutting down after " + (rstring)$timeoutInterval + " second timeout"); shutdownPE(); } } }


public class ArtistsAPI
extends SpotifyEndpoint
Endpoints for retrieving information about one or more artists from the Spotify catalog.
| Constructor and Description |
|---|
ArtistsAPI(SpotifyAPI api)
Endpoints for retrieving information about one or more artists from the Spotify catalog.
|
| Modifier and Type | Method and Description |
|---|---|
SpotifyRestAction<com.adamratzman.spotify.models.Artist> |
getArtist(java.lang.String artist)
Get Spotify catalog information for a single artist identified by their unique Spotify ID.
|
SpotifyRestActionPaging<com.adamratzman.spotify.models.SimpleAlbum,com.adamratzman.spotify.models.PagingObject> |
getArtistAlbums(java.lang.String artist,
java.lang.Integer limit,
java.lang.Integer offset,
com.neovisionaries.i18n.CountryCode market,
com.adamratzman.spotify.endpoints.public.ArtistsAPI.AlbumInclusionStrategy include)
Get Spotify catalog information about an artist’s albums.
|
SpotifyRestAction<java.util.List> |
getArtistTopTracks(java.lang.String artist,
com.neovisionaries.i18n.CountryCode market)
Get Spotify catalog information about an artist’s top tracks by country.
|
SpotifyRestAction<java.util.List> |
getArtists(java.lang.String artists)
Get Spotify catalog information for several artists based on their Spotify IDs. Artists not found are returned as null inside the ordered list
|
SpotifyRestAction<java.util.List> |
getRelatedArtists(java.lang.String artist)
Get Spotify catalog information about artists similar to a given artist.
Similarity is based on analysis of the Spotify community’s listening history.
|
getApi, getCache
public ArtistsAPI(SpotifyAPI api)
Endpoints for retrieving information about one or more artists from the Spotify catalog.
public SpotifyRestAction<com.adamratzman.spotify.models.Artist> getArtist(java.lang.String artist)
Get Spotify catalog information for a single artist identified by their unique Spotify ID.
artist
- The spotify id or uri for the artist.class Artist
if valid artist id is provided, otherwise nullpublic SpotifyRestAction<java.util.List> getArtists(java.lang.String artists)
Get Spotify catalog information for several artists based on their Spotify IDs. Artists not found are returned as null inside the ordered list
artists
- The spotify ids or uris representing the artists.class Artist
objects or null if the artist could not be found, in the order requestedpublic SpotifyRestActionPaging<com.adamratzman.spotify.models.SimpleAlbum,com.adamratzman.spotify.models.PagingObject> getArtistAlbums(java.lang.String artist, java.lang.Integer limit, java.lang.Integer offset, com.neovisionaries.i18n.CountryCode market, com.adamratzman.spotify.endpoints.public.ArtistsAPI.AlbumInclusionStrategy include)
Get Spotify catalog information about an artist’s albums.
artist
- The artist id or urimarket
- Supply this parameter to limit the response to one particular geographical market.limit
- The number of objects to return. Default: 20. Minimum: 1. Maximum: 50.offset
- The index of the first item to return. Default: 0. Use with limit to get the next set of itemsinclude
- List of keywords that will be used to filter the response. If not supplied, all album groups will be returned.class PagingObject
of class SimpleAlbum
objectspublic SpotifyRestAction<java.util.List> getArtistTopTracks(java.lang.String artist, com.neovisionaries.i18n.CountryCode market)
Get Spotify catalog information about an artist’s top tracks by country.
Contains only up to 10 tracks with no class CursorBasedPagingObject
to go between top track pages. Thus, only the top
10 are exposed
artist
- The spotify id or uri for the artist.market
- The country (Market) to search. Unlike endpoints with optional Track Relinking, the Market is not optional.class Track
s of an artist in the given marketclass CursorBasedPagingObject
public SpotifyRestAction<java.util.List> getRelatedArtists(java.lang.String artist)
Get Spotify catalog information about artists similar to a given artist. Similarity is based on analysis of the Spotify community’s listening history.
artist
- The spotify id or uri for the artist.class Artist
s representing similar artists


{% extends "layout.html" %} {% load tz %} {% get_current_timezone as TIME_ZONE %} {% block body %}
{{book.detail}}
There were no results matching your search: "{{query}}"
Your dayline has expired! Refund book now.


one
two
three
315
content with nbsp
content with ignored spaces around
content with ignored spaces in
content with nbsp and spaces around
1. bla 2. bla


Survey Questions
About you
Name (required)
Link to Photo (required)
Question 1
You have a strong sense of justice
Select an Option
Question 2
You love thinking outside of the box
Select an Option
Question 3
You have a sketchy history
Select an Option
Question 4
You have a magical hammer
Select an Option
Question 5
You have an anger problem
Select an Option
Question 6
You are adept at aiming, specifically with a bow and arrow
Select an Option
Question 7
You have the ability to manipulate people with your mind
Select an Option
Question 8
You have a really, really strong cybernetic arm
Select an Option
Question 9
You are super sexy
Select an Option
Question 10
You are Russian but don't have an accent
Select an Option


Blogs
A Medium to Code.
How to install Flutter on Mac & Windows
Learn about Flutter and how to set it up on Windows and Mac systems
Connecting Cloud Firestore Database to Flutter
Learn how to connect your Flutter app with Cloud Firestore and update the database in real-time
Building a Flutter DateTime Picker in just 15 minutes
Learn implementation of an iOS-style DateTime Picker in Flutter
Adding Firebase to your Flutter App
Learn how to easily integrate Firebase in Flutter with a step by step guide.
Building a Flutter AlertDialog in just 15 minutes
Learn how to implement a customized Alert Dialog / Popup in Flutter.
What’s new with Flutter 1.9 & how to upgrade to the latest version
Learn about what Google has introduced in the latest Flutter release.
How to add Facebook Authentication to your Flutter App
Learn how to add Facebook OAuth to your Flutter App with easy step by step guide
Building an iOS-style Shopping App with a minimalist design using Flutter
Learn how to use Cupertino style (iOS) to make apps in Flutter
Building Flutter PullToRefresh in just 15 minutes
Learn easy implementation of a PullToRefresh gesture in Flutter.
What’s new with Flutter 1.7 & how to upgrade to the latest version
Learn about what Google has introduced in the latest Flutter release.
Understanding Version Control Systems: GitHub Simplified with SourceTree
Learn about how to get Git up and running on your system along with the best industry practices using SourceTree.
How to install Go Lang on Windows, macOS & Linux
Learn about what is Go Lang & how to get it up and running on your Windows, Mac or Linux systems.
Guide to integrate Google Maps with Flutter
Learn how to build a mobile app featuring a Google Map using the Flutter SDK.


Preparation time
over 2 hours
Cooking time
30 mins to 1 hour
Serves
Makes 1 loaf
Dietary
This is a deliciously moist, soft bread, which is perfect with white asparagus soup. It really is a multi-purpose bread and great for sandwiches or fried.
You need exactly 300g/10½oz to make the bread so check the weight once they are peeled. Place the potatoes in a large saucepan and cover with cold water, bring to the boil, then reduce the heat slightly and cook for 15–20 minutes until they are tender but not falling apart.
Drain the potatoes in a colander over a bowl and reserve the cooking liquid. Return the potatoes to the pan and toss over a very low heat for 2-3 minutes until any excess liquid has evaporated.
Pour 75ml/5 tbsp of the warm cooking liquid into a large bowl and leave to cool for a few minutes. When it’s lukewarm, sprinkle in the yeast. Stir in the sugar and leave in a warm place for about 10 minutes until a light foam appears on the surface. If you’re using a mixture of wholemeal and white flour, add an extra tablespoon of the cooking liquor.
Mash the potatoes with the oil in the saucepan until they’re as smooth as possible, then stir in the yeast mixture and salt.
Mix well with a wooden spoon and gradually add the flour, a few tablespoons at a time, stirring well before adding more. When the dough becomes too stiff to stir in the flour, turn it out on to the work surface and knead the remaining flour into the dough. Don’t be tempted to add more water or the dough will become too sticky to work with.
Knead the dough for 10 minutes until soft and pliable. Place it in a lightly oiled bowl, cover loosely with lightly oiled cling film and leave to rise in a warm place for 45–60 minutes, or until well-risen and spongy to touch.
Knock back the dough with your knuckles and shape it into a rough ball.  Pinch lightly to seal and sprinkle on some wholemeal flour, for additional texture, on the outside of the loaf. Place it on a lightly oiled and floured baking sheet, rough-side up, and leave to prove in a warm place for a further 30 minutes.
Preheat the oven to 220°C/Fan 200°C/Gas 7. Score the dough with a knife along the pinched join and sprinkle the top with the onion seeds. Bake the loaf in the centre of the oven for 35 minutes until well risen and crusty on top. Cool on a wire rack.


← Back to the list of projects
Posted by Andy Brennan on 4 Jan, 2006
Category: the misc
Over break Eli set up a project name for me. Since I’m missing all the in-person interactions you guys are working on in Seattown, I’m not going to, like, make huge alterations to your work. But maybe I can be helpful as a third-party objective reader—a third-party who is yet intimately familiar with our past work. So, you might see a comment or two here or there from the andyman. Titties!
Comments
Eli Duke on 4 Jan, 2006:
it’s great to have you andy, but as i posted in another comment, we need to have a picture of you on here, or else i have no idea who you are. i only work in visuals when it comes to identifying people.


PWPointOfInterest Class Reference
| Inherits from | NSObject |
|---|---|
| Conforms to | PWMapPoint |
| Declared in | PWPointOfInterest.h |
Overview
This class represents a point-of-interest defined on the MaaS portal and contains data associated with that point-of-interest.
summary
A description of the point-of-interest. May be NULL.
@property (nonatomic, readonly, nullable) NSString *summary
Declared In
PWPointOfInterest.h
floor
The PWFloor
object that the point-of-interest belongs to.
@property (nonatomic, readonly, weak, nullable) PWFloor *floor
Declared In
PWPointOfInterest.h
pointOfInterestType
The PWPointOfInterestType
object that the point-of-interest is a member of.
@property (nonatomic, readonly, nullable) PWPointOfInterestType *pointOfInterestType
Declared In
PWPointOfInterest.h
image
The representative UIImage of the point-of-interest. Will be NULL if image has not yet been loaded.
@property (nonatomic, readonly, nullable) UIImage *image
Declared In
PWPointOfInterest.h
metaData
Metadata associated with the point-of-interest. May be NULL.
@property (nonatomic, readonly, nullable) NSDictionary *metaData
Declared In
PWPointOfInterest.h
minZoomLevel
The minimum zoom level that the point-of-interest is visible on, and the below are the possible values: -1 - always visible. 1 - subtract 4 from the maximum zoom level that current iOS map supports, it’s 18. 2 - subtract 3 from the maximum zoom level that current iOS map supports, it’s 19. 3 - subtract 2 from the maximum zoom level that current iOS map supports, it’s 20. 4 - subtract 1 from the maximum zoom level that current iOS map supports, it’s 21. 5 - the maximum zoom level that current iOS map supports, it’s 22.
@property (nonatomic, readonly) NSInteger minZoomLevel
Discussion
The point-of-interest is only visble when the map zoom level is between minZoomLevel
and maxZoomLevel
.
Declared In
PWPointOfInterest.h
maxZoomLevel
The maximum zoom level that the point-of-interest is visible on, and the below are the possible values: -1 - always visible. 1 - subtract 4 from the maximum zoom level that current iOS map supports, it’s 18. 2 - subtract 3 from the maximum zoom level that current iOS map supports, it’s 19. 3 - subtract 2 from the maximum zoom level that current iOS map supports, it’s 20. 4 - subtract 1 from the maximum zoom level that current iOS map supports, it’s 21. 5 - the maximum zoom level that current iOS map supports, it’s 22.
@property (nonatomic, readonly) NSInteger maxZoomLevel
Discussion
The point-of-interest is only visble when the map zoom level is between minZoomLevel
and maxZoomLevel
.
Declared In
PWPointOfInterest.h
title
The title of the point-of-interest. May be NULL.
@property (nonatomic, copy, readwrite, nullable) NSString *title
Declared In
PWPointOfInterest.h
subtitle
The subtitle of the point-of-interest. May be NULL.
@property (nonatomic, copy, readwrite, nullable) NSString *subtitle
Declared In
PWPointOfInterest.h
portalID
The portal identifier of the point-of-interest. May be NULL.
@property (readonly, nullable) NSString *portalID
Declared In
PWPointOfInterest.h