When you give someone flowers, you expect her to talk about how beautiful they are or how good they smell.  Yet, that's what happening here. Maybe she's some kind of Magical Native American or just very much Closer to Earth, but whatever her reason she's feeling sorry for the poor flowers or even see them as something bad in a necromantic sort of way. If the complainer is a third party, they may criticize the receiver for enjoying the gift.
Examples:
Comic Books
- Poison Ivy of Batman is known to react this way.
- The EC Comics story "Gee, Dad... It's a Daisy!" (Shock SuspenStories #2): "Flowers and plants are alive! Just because they don't cry out doesn't mean they don't feel pain!" This leads to a Space Whale Aesop with Plant Aliens picking apart a human.
- Rogue Trooper. In the "Remembrance Day" retelling of the Quartz Zone Massacre, Rogue finds a single flower that has somehow managed to bloom on Nu Earth. In picking it, he realises that he's killed it too. On cutting to Tor Cyan watching the history logs, we find that after the war finally ended, the blue flowers have flourished on Nu Earth.
- The ghost Talia from Brody's Ghost has a conversation with Brody about a vase of flowers she saw in her actually her father's hospital room when she was still alive. She tearfully compares the flowers in a vase of water to human beings on life support, and wonders why anyone thinks that it will stop the flowers/person from dying.
- In Runaways, Klara's Green Thumb powers only work on plants that are still connected to their roots. She gets particularly upset when people cut roses, as she can hear the roses speaking to her.
Film
- Ellen at the start of Nosferatu mourns the flowers which she receives from Hutter at the beginning.
- In Alien Nation: Dark Horizon, one of the aliens is sick. Her human boyfriend brings a bouquet of flowers as he visits her in the hospital, but is not allowed to deliver them to her.  In the alien culture, dying flowers are seen as a bad omen when it comes to recovering health.
Literature
- Roald Dahl:
- In The BFG, the Big Friendly Giant comments that since he can hear the voices of plants, anytime somebody picks a flower he can hear the plant screaming as though somebody were having their arm twisted off. Sophie wonders whether she'll ever be able to pick flowers again.
- In one of Dahl's short stories for adults, "The Sound Machine", the titular machine reveals to its inventor that plants make some kind of noise when they're cut, plucked, etc. Whether it's an expression of pain or not is unknown, but it's enough to severely unnerve the inventor.
- In The Naked Sun, Gladia picks a flower and Elijah remarks that she killed it.
- In She Lover of Death, Columbina feeds the flowers that Fandorin gives her on their first Not-Date to a horse, saying that she has no need for "flower corpses".
- In one of the Tales of the City books, Anna mentions that prostitutes consider cut flowers unlucky, as they are beauty cut down in their prime. This is foreshadowing of the revelation that Anna's mother was a madam, and she was raised in a brothel.
Live-Action TV
- Buffy the Vampire Slayer: "Out Of My Mind".
Harmony: You know what it means that he can't hurt any living thing? It means he can't even pick flowers.Spike: What? Yes I can!
- In Star Trek: Deep Space Nine, Odo includes this in some early-season grumbling to Quark about how absurd the rituals of humanoid romance are, saying that writing bad poetry and "sacrificing plant life" are silly ways to entice a mate, which he doesn't see the point of in the first place.
- In Stefan's second appearance in Family Matters, by Laura's request, Steve made him more sensitive and caring since in his first appearance, he was too self-centered and shallow. In one scene, Stefan ends up giving Laura a potted plant, explaining he couldn't bear to harm a flower by plucking it.
- Friends:
- "The One Where Rachel Quits": Free-spirited Phoebe gets sad when she realizes Joey sells Christmas trees. Joey tries to convince her it's their life purpose to make people happy at Christmas. She sort of accepts that but is horrified when she sees trees getting destroyed even before they're sold. Monica and Rachel's apartment then gets filled with all of the old Christmas trees from Joey's work. Phoebe is excited the trees are saved. Chandler snarks it's like Night of the Living Dead Christmas Trees.
Joey: And I gotta go sell some Christmas trees.
Phoebe: Have fun. Oh, wait, no, dont! I forgot I am totally against that now.
Joey: What? Me having a job?
Phoebe: No, no, I am against innocent trees being cut down in their prime, and their, their corpses grotesquely dressed in like tinsel and twinkly lights. Hey, how do you sleep at night? - "The One with the Ball": Phoebe's boyfriend Gary wants to ask Phoebe to move in with him. The friends think it might be too soon, but he thinks it's the right step. Monica gets excited but advises him not to bring her flowers because Phoebe cries when they die.
- "The One Where Rachel Quits": Free-spirited Phoebe gets sad when she realizes Joey sells Christmas trees. Joey tries to convince her it's their life purpose to make people happy at Christmas. She sort of accepts that but is horrified when she sees trees getting destroyed even before they're sold. Monica and Rachel's apartment then gets filled with all of the old Christmas trees from Joey's work. Phoebe is excited the trees are saved. Chandler snarks it's like Night of the Living Dead Christmas Trees.
Roleplay
- Infinity's Row: Ren is always seeing the negative side of things, never trying to see the good in the world.
Video Games
- In Disgaea 5: Alliance of Vengeance during a Flashback Killia recalls when he picked a flower for Lieze. Lieze yelled at him saying that the flower was dead now, and not even Killia freezing the flower using Alma Ice Sculpture could help her mood in the slightest. The flower does serve a different narrative purpose after that; it reminds Killia of his biggest failure in Lieze's death.
- In Quest for Glory V, you can give flowers to various ladies and eventually marry one of the four most prominent ones. One of them will feel sorry for the flowers, but giving them to her earns you romantic points anyway. She will use their seeds to make more living flowers.
- Return to Zork features this with the appropriately-titled "bonding plant." If you pull the plant straight out of the ground, it instantly dies. The correct solution is to dig it up by the roots so that it likes you.
Visual Novels
- Little Busters!: In Kurugaya's route, she describes an event from her childhood when she picked some beautiful flowers from the side of the road just because they looked pretty and was horrified and distressed later on when she realised that in doing so she had killed them.
Webcomics
- This comic from Saturday Morning Breakfast Cereal explains the human ritual of flower-giving, as understood by aliens. Supposedly human males mutilate the hated plant-life to show off their prowess... and it goes downhill from there.
What are "hippies"?
The most violent humans in history.
Western Animation
- A sympathetic Plant Person Aladdin: The Series villain of the week mentioned picking flowers and condemning them to a slow death in a vase as one of humanity's crimes against nature.
- In the Tiny Toon Adventures episode, "Buster and Babs Go Hawaii", when their plane lands, they're greeted by Shirley who puts flower necklaces on them and says, "Like, welcome to Hawaii. Enjoy these ultra-rare flowers that, like, died for you...


JavaScript (JS) is a lightweight, interpreted, or just-in-time compiled programming language with first-class functions. While it is most well-known as the scripting language for Web pages, many non-browser environments also use it, such as Node.js, Apache CouchDB and Adobe Acrobat. JavaScript is a prototype-b````ased, multi-paradigm, single-threaded, dynamic language, supporting object-oriented, imperative, and declarative (e.g. functional programming) styles.
The standard for JavaScript is ECMAScript. As of 2012, all modern browsers fully support ECMAScript 5.1. Older browsers support at least ECMAScript 3. On June 17, 2015, ECMA International published the sixth major version of ECMAScript, which is officially called ECMAScript 2015, and was initially referred to as ECMAScript 6 or ES6. Since then, ECMAScript standards are on yearly release cycles. This documentation refers to the latest draft version, which is currently ECMAScript 2020.
Do not confuse JavaScript with the Java programming language. Both "Java" and "JavaScript" are trademarks or registered trademarks of Oracle in the U.S. and other countries. However, the two programming languages have a very different syntax, semantic, and use.
An operator performs some operation on single or multiple operands (data value) and produces a result. For example 1 + 2, where + sign is an operator and 1 is left operand and 2 is right operand. + operator adds two numeric values and produces a result which is 3 in this case.
A variable is a named reference to a value. That way an unpredictable value can be accessed through a predetermined name.
A function is a code snippet that can be called by other code or by itself, or a variable that refers to the function. When a function is called, arguments are passed to the function as input, and the function can optionally return a value. A function in JavaScript is also an object.
A function name is an identifier included as part of a function declaration or function expression. The function name's scope depends on whether the function name is a declaration or expression.
A method is a function which is a property of an object. There are two kind of methods: Instance Methods which are built-in tasks performed by an object instance, or Static Methods which are tasks that are called directly on an object constructor.
An argument is a value (primitive or object) passed as input to a function.
A parameter is a named variable passed into a function. Parameter variables are used to import arguments into functions.
Note the difference between parameters and arguments:
Two kinds of parameters:
input parameters
the most common kind; they pass values into functions. Depending on programming language, input parameters can be passed several ways (e.g., call-by-value, call-by-address, call-by-reference).
output/return parameters
primarily return multiple values from a function, but not recommended since they cause confusion
The return statement ends function execution and specifies a value to be returned to the function caller.
Method chaining eliminates an extra variable for each intermediate step. The developer is saved from the cognitive burden of naming the variable and keeping the variable in mind.
Method chaining has been referred to as producing a "train wreck" due to the increase in the number of methods that come one after another in the same line that occurs as more methods are chained together.
A JavaScript string stores a series of characters like "John Doe". String indexes are zero-based: The first character is in position 0, the second in 1, and so on.
A JavaScript Boolean represents one of two values: true or false.
The undefined property indicates that a variable has not been assigned a value, or not declared at all.
The global NaN property is a value representing Not-A-Number.
The value null represents the intentional absence of any object value. It is one of JavaScript's primitive values and is treated as falsy for boolean operations.
The alert() method displays an alert box with a specified message and an OK button.
An alert box is often used if you want to make sure information comes through to the user.
Note: The alert box takes the focus away from the current window, and forces the browser to read the message. Do not overuse this method, as it prevents the user from accessing other parts of the page until the box is closed.
Single-line comments are used to comment a part of a line or a full line of code in JavaScript. You can use it for either explaining the code or debugging (commenting out to prevent the execution by the browser). For a single-line comment, you have to write two forward slashes: // .
jQuery is a JavaScript library that allows web developers to add extra functionality to their websites. It is open source and provided for free under the MIT license. In recent years, jQuery has become the most popular JavaScript library used in web development.
An attribute extends an element, changing its behavior or providing metadata.
An attribute always has the form name="value" (the attribute's identifier followed by its associated value).


An "Event Bus" can provide a loosely coupled way to communicate between objects.
Services can expose a Subject/BehaviorSubject/ReplaySubject that other services/components can subscribe to for changes.
State management solutions can also be used.
Notes:
This example shows how services and subjects can be used to provide a communication mechanism for
components. Click one of the buttons below and note how the customers count number in the upper-right corner
changes (event bus is used). Note that when a customer is selected in the "Customers" presentation component the details display
AND the customer details display below the navbar (BehaviorSubject is used).
The "Add Customer (push only)" button will call a service to add a customer but the returned customers data will not display properly and will not trigger ngOnChanges in the presentation component since the customers array itself doesn't change.
The "Add Customer (push a clone)" will call a service to add a customer but the service will return a clone of the customers data. The presentation component's ngOnChanges will fire and the data will display correctly as a result.


- id - Variable in class scrame.entity.FacultyMember
-
The id as a string of the faculty member.
- IllegalCourseTypeException - Exception in scrame.exception
-
The exception when you are doing something that is not permitted
for that type of course.
- IllegalCourseTypeException(String) - Constructor for exception scrame.exception.IllegalCourseTypeException
-
Constructor of the exception.
- IllegalStudentArgumentException - Exception in scrame.exception
-
The exception when someting wrong in the student details while registering.
- IllegalStudentArgumentException(String) - Constructor for exception scrame.exception.IllegalStudentArgumentException
-
The constructor of the exception
- IllegalVacancyException - Exception in scrame.exception
-
The exeption when the sum of the group vacancies does not
tally up correctly to the course total vacancy.
- IllegalVacancyException(String) - Constructor for exception scrame.exception.IllegalVacancyException
-
The constructor of the exception.
- IllegalWeightageException - Exception in scrame.exception
-
Exception thrown when the total weightage component does not
tally up correctly.
- IllegalWeightageException(String) - Constructor for exception scrame.exception.IllegalWeightageException
-
The constructor of the exception.
- inputToFile() - Static method in class scrame.manager.CourseManager
-
Writes list of all courses to file.
- inputToFile() - Static method in class scrame.manager.RecordManager
-
Inputs record list to file.
- inputToFile() - Static method in class scrame.manager.StudentManager
-
Inputs student list to file.
- isCourseCoordinator - Variable in class scrame.entity.FacultyMember
-
Indicates if this faculty member is a course coordinator.
- isCourseInList(String) - Static method in class scrame.manager.CourseManager
-
Checks whether a course is in the list.
- isStudentInList(String) - Static method in class scrame.manager.StudentManager
-
Checks if a student is registered.
- isStudentRegisteredOnCourse(String, String) - Static method in class scrame.manager.RecordManager
-
Checks if student is registered in a course, given the student's matric number and course name.


Welcome!
If you are seeing this, it's because you've successfully built
klange's Simple CGI Server
!


GODZILLA (1998) (Tri-Star) Starring: Matthew Broderick, Jean Reno, Maria Pitillo, Hank Azaria. Screenplay: Dean Devlin and Roland Emmerich. Producer: Dean Devlin. Director: Roland Emmerich. MPAA Rating: PG-13 (intense situations, profanity, some violence) Running Time: 135 minutes. Reviewed by Scott Renshaw.
Roland Emmerich and Dean Devlin have thrown down the gauntlet to film critics...or, at the very least, to two film critics in particular. In GODZILLA, the latest summer spectacle-cum-marketing assault from the creators of INDEPENDENCE DAY, a mutant reptile from the South Pacific comes to New York City on a rampage, much to the consternation of the city's blustering mayor (Michael Lerner). A corpulent fellow with familiar graying hair and glasses, the mayor noshes on Hershey's kisses between campaign stops where he gives citizens his "Thumbs Up for New York" signal. Oh, and his name is Mayor Ebert. Oh, and he has an ineffectual, balding assistant named Gene.
I don't know why Emmerich and Devlin found this snarky fit of retaliatory pique necessary. Certainly the negative reviews of Gene Siskel and Roger Ebert didn't hurt the box office take of "ID4," which somehow overcame two thumbs down to gross $300 million in North America. GODZILLA is certain to be similarly critic-proof, pitting a massive CGI beast against intrepid biologist Nick Tatopoulos (Matthew Broderick), his former girlfriend and aspiring reporter Audrey Timmonds (Maria Pitillo) a mysterious Frenchman (Jean Reno), the entire U.S. Army and the fortunes of every other summer film standing in its path. Is critical acceptance so important to the film-makers that doing well isn't good enough as the best revenge?
If such is the case, here's a simple bit of advice for Emmerich and Devlin: make better movies. GODZILLA is a baby step in the right direction, if for no other reason than that it's more honest in its big-budget intentions than the dopey and grandiose INDEPENDENCE DAY. Using ALIENS and JURASSIC PARK as its models rather than the adventures of its rubber-suited predecessor, GODZILLA takes only half an hour before the scaly behemoth hits the Big Apple and turns midtown Manhattan into a Jurassic parking lot. This sequence is followed by a dizzying helicopter chase through the man-made canyons, which is followed by an underwater encounter with submarines, which is followed by a search for what may be the creature's nest, and so on. The sheer variety of situations insures a consistent level of interest, while simultaneously insuring several unique levels for the inevitable Sony PlayStation version of the game.
Whenever the film rests on human shoulders, however, you could sprain your eyeballs from rolling them. Continuing their uncanny knack for inane back-story, Emmerich and Devlin create a mayo-on-white-bread relationship dynamic between Broderick and Pitillo, and a mission for Reno which makes less and less sense every moment I think about it. The dialogue is hopelessly feeble, the attempts at humor strained even by action movie standards, and the characters so caricatured that Al Hirschfeld should have drawn them. Most unforgivably, GODZILLA wastes the immense comic talents of Hank Azaria and Harry Shearer, restricting Azaria to loyal sidekick duty as Pitillo's cameraman and leaving Shearer to do a half-hearted (and less animated) version of his "The Simpsons" anchorman Kent Brockman. Even the always-appealing Broderick looks baffled by how to be charming in such a suffocating role.
At least Emmerich sends the audience home on a high note, closing GODZILLA with a superb sequence in which Godzilla chases a cab through the city, eventually onto the Brooklyn Bridge. It's a sharp piece of action film-making, and not the only one. Yet GODZILLA can't get by on its big-ness alone; in fact, the visuals sometimes seem decidedly less than state-of-the-art, particularly when our heroes are threatened inside Madison Square Garden. When a blockbuster exists solely to impress you with "you gotta see this" tricks, it's tough not to be disappointed when you've seen the tricks before, and seen them done better. There are ways around that disappointment, though, which Emmerich and Devlin should ponder: Write better scripts. Create real characters. Make better movies. Either that, or continue blaming the messengers for the way they hold their thumbs.
On the Renshaw scale of 0 to 10 monster islands: 5.
Visit Scott Renshaw's MoviePage http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the MoviePage for details, or reply to this message with subject line "Subscribe".
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.
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Related links: index of all rec.arts.movies.reviews reviews


Tosca is an opera in three acts by Giacomo Puccini to an Italian libretto by Luigi Illica and Giuseppe Giacosa. It premiered at the Teatro Costanzi in Rome on 14 January 1900. The work, based on Victorien Sardou's 1887 French-language dramatic play, La Tosca, is a melodramatic piece set in Rome in June 1800, with the Kingdom of Naples's control of Rome threatened by Napoleon's invasion of Italy. Musically, Tosca is structured as a through-composed work, with arias, recitative, choruses and other elements musically woven into a seamless whole. Puccini used Wagnerian leitmotifs to identify characters, objects and ideas. -Wikipedia


|
ChainAMPS
1.0.0
Code for working with chain array matrix product states.
|
Initial input from command line can be given in the form of a spectrum and matrix elements A model still needs to be defined though for the Hamiltonian to be formed correctly. More...
#include <iostream>
#include <string>
#include <sstream>
#include <limits>
#include <vector>
#include "optionparser/optionparser.h"
Go to the source code of this file.
Classes | |
| struct | ajaj::Arg |
| class | ajaj::Base_Args |
| class | ajaj::iDMRG_Args |
| class | ajaj::fDMRG_Args |
| class | ajaj::iTEBD_Args |
| class | ajaj::TEBD_Args |
| class | ajaj::iMEAS_Args |
| class | ajaj::Store_Args |
Namespaces | |
| ajaj | |
| Namespace to prevent clashes between names used by AJAJ and possibly any user included files e.g. | |
Typedefs | |
| typedef std::vector< std::pair< std::string, unsigned long > > | ajaj::StringIndexPairs |
Functions | |
| std::istream & | ajaj::operator>> (std::istream &is, std::vector< short > &v) |
| std::istream & | ajaj::operator>> (std::istream &is, StringIndexPairs &l) |
Initial input from command line can be given in the form of a spectrum and matrix elements A model still needs to be defined though for the Hamiltonian to be formed correctly.


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Build a quote machine that generates new quotes at the press of a button.
Click on a button to generate a random new quote, and click on a button to tweet out a quote. The tweet should automatically have the quote in the tweet.
Thank you to free code camp for creating these challanges, I've waited way too long to do this one, although it was a lot easier after spending time doing other projects.


v0.4 documentation
Calloway’s settings includes bundles of applications that are either dependent upon each other, or work well together. Some application bundles have suggestions for others that can augment their functionality, but are not required.
Application bundles are only for convenience. Feel free to mix and match applications.
Warning
As of version 0.4 this application bundle is deprecated. In order to better handle core Django apps in different versions, this bundle has been broken into calloway.settings.APPS_DJANGO_BASE, calloway.settings.APPS_DJANGO13_BASE, and calloway.settings.APPS_DJANGO_TEMPLATE_UTILS.
These applications are considered core to any project and consist of Django contrib applictions.
Suggested application bundles: APPS_TINYMCE, APPS_REVERSION (for flatpages)
Note
New in version 0.4
These are the core contrib apps included in the default settings for Django 1.0 - 1.2.
Suggested application bundles: APPS_TINYMCE, APPS_REVERSION (for flatpages)
Note
New in version 0.4
These are the core contrib apps included in the default settings for Django 1.3.
Suggested application bundles: APPS_TINYMCE, APPS_REVERSION (for flatpages)
Note
New in version 0.4
When you want special help with templates, these contrib apps can help out.
The admin area of the project requires several apps. The livevalidation application provides real-time validation of forms, including the admin forms. django-admin-tools provides the custom skin.
Warning
In version 0.4, livevalidation has been removed from this bundle.
Calloway includes no real applications, except django_ext which are small extensions to Django. Various utilities are included within this, including a case-insensitive authorization, requirements generator and a few others.
Django MPTT is an application that enhances other applications’ ability to manage hierarchical data. It is required in APPS_CATEGORIES and APPS_COMMENT_UTILS.
A specialized profile for staff members.
Suggested application bundles: APPS_TINYMCE
Django Reversion is an app that enhances other applications. It allows you to view and revert to previous versions of records. Django Stories and Django Flatpages are currently configured to use it.
Warning
Changed in version 0.4: Django Viewpoint (a blogging app) and Django Pullquote (for storing quotations) were removed.
A bundle of applications for creating news.
Suggested application bundles: APPS_TINYMCE, APPS_REVERSION
Note
New in version 0.4
A blogging platform for one blog or many blogs.
Suggested application bundles: APPS_TINYMCE, APPS_REVERSION
A hierarchical category manager. Requires :ref:`APPS_MPTT <apps_mptt>`.
Utilities for adding threaded comments to the default Django comments app and management of offensive comments. Requires APPS_MPTT
Allows using your admin forms in a regular template. Requires livevalidation in APPS_ADMIN.
Warning
Changed in version 0.4: Django Tagging was moved to apps_tagging.
calloway.settings.APPS_TAGGING * tagging


Low Effort Slides
… if you don't have time for LaTeX
Made without much consideration by Andreas Kröpelin
How to use
Modify this template file (get it from GitHub):
$ git clone https://github.com/andreasKroepelin/loweffortslides.git
One

per slide:

 slide title

 some content

Slide numbers are automatically inserted.
Opt out using

.
How to navigate
- next slide: press PgDn or →, or scroll down
- previous slide: press PgUp or ←, or scroll up
- set anchor part in URL to slide number (
…/…/index.html#42
)
Behind the scenes: CSS and JavaScript
59 lines of CSS, 47 lines of JavaScript.


{% include movilizer/head.html %}
{% include movilizer/header.html %} {% include movilizer/content.html %} {% include movilizer/footer.html %} {% include movilizer/js.html %}


We are presented with 10 images, each of which involve a stick figure with a question mark over their face, followed by 29 pictures of named characters or celebrities. Some of the people in the pictures share a first name; for example, there are four people named Will.
The key realization is that each image, when paired with one of the unique names, depicts a common phrase that can be misinterpreted as being about a named individual. For example, the fourth image, which shows a person being unlocked from a cage, depicts “FREE WILL”.
Indexing the number of pictures with the name into the matching phrase yields BITE UNUSED. This phrase can be applied to the one remaining picture that isn’t matched—that of Mark Zuckerberg—to produce the final answer: BITE MARK.
The phrases in order:
| BABBLING BROOK | 1 | B |
| CHRISTMAS CAROL | 4 | I |
| HEAT RAY | 4 | T |
| FREE WILL | 4 | E |
| JUMPING JACK | 2 | U |
| HANGING CHAD | 3 | N |
| CUT TO THE CHASE | 2 | U |
| SAVING GRACE | 1 | S |
| FREQUENT FLIER MILES | 3 | E |
| GILD THE LILY | 4 | D |
The people/characters listed from left-to-right, top-to-bottom:
Miles Teller, Chad Pennington, Jack Black, Will Smith


Before Change
print(msg.format(iteration, current))
// Compute the ratio of pixels perturbed by the algorithm
percent_perturbed = float(iteration * 2) / float(
FLAGS.img_rows * FLAGS.img_cols)
// Report success when the adversarial example is misclassified in the
// target class
if current == target:
After Change
adv_x = copy.copy(sample)
// count the number of features. For MNIST, 1x28x28 = 784; for
// CIFAR, 3x32x32 = 3072; etc.
nb_features = np.product(adv_x.shape[1:])
// reshape sample for sake of standardization
original_shape = adv_x.shape
adv_x = np.reshape(adv_x, (1, nb_features))
// compute maximum number of iterations
max_iters = np.floor(nb_features * gamma / 2)
print("Maximum number of iterations: {0}".format(max_iters))
// Compute our initial search domain. We optimize the initial search domain
// by removing all features that are already at their maximum values (if
// increasing input features---otherwise, at their minimum value).
if increase:
search_domain = set([i for i in xrange(nb_features)
if adv_x[0, i] < clip_max])
else:
search_domain = set([i for i in xrange(nb_features)
if adv_x[0, i] > clip_min])
// Initialize the loop variables
iteration = 0
current = utils_tf.model_argmax(sess, x, predictions, np.reshape(adv_x, original_shape))
// Repeat this main loop until we have achieved misclassification
while (current != target and iteration < max_iters


{% import 'footer.html' as footer %}
Make an account to rate boba shops and stay in the loop about Boba the app promotions.


MP3 metadata editor
Edit metadata:
Select your file:
Seperate multiple artists using a ";"
Release year:


public class ContingencyTables
extends java.lang.Object
| Constructor and Description |
|---|
ContingencyTables() |
| Modifier and Type | Method and Description |
|---|---|
static double |
chiSquared(double[][] matrix,
boolean yates)
Returns chi-squared probability for a given matrix.
|
static double |
chiVal(double[][] matrix,
boolean useYates)
Computes chi-squared statistic for a contingency table.
|
static boolean |
cochransCriterion(double[][] matrix)
Tests if Cochran's criterion is full-filled for the given contingency
table.
|
static double |
CramersV(double[][] matrix)
Computes Cramer's V for a contingency table.
|
static double |
entropy(double[] array)
Computes the entropy of the given array.
|
static double |
entropyConditionedOnColumns(double[][] matrix)
Computes conditional entropy of the rows given the columns.
|
static double |
entropyConditionedOnRows(double[][] matrix)
Computes conditional entropy of the columns given the rows.
|
static double |
entropyConditionedOnRows(double[][] train,
double[][] test,
double numClasses)
Computes conditional entropy of the columns given the rows of the test
matrix with respect to the train matrix.
|
static double |
entropyOverColumns(double[][] matrix)
Computes the columns' entropy for the given contingency table.
|
static double |
entropyOverRows(double[][] matrix)
Computes the rows' entropy for the given contingency table.
|
static double |
gainRatio(double[][] matrix)
Computes gain ratio for contingency table (split on rows).
|
static double |
log2MultipleHypergeometric(double[][] matrix)
Returns negative base 2 logarithm of multiple hypergeometric probability
for a contingency table.
|
static void |
main(java.lang.String[] ops)
Main method for testing this class.
|
static double[][] |
reduceMatrix(double[][] matrix)
Reduces a matrix by deleting all zero rows and columns.
|
static double |
symmetricalUncertainty(double[][] matrix)
Calculates the symmetrical uncertainty for base 2.
|
static double |
tauVal(double[][] matrix)
Computes Goodman and Kruskal's tau-value for a contingency table.
|
public static double chiSquared(double[][] matrix, boolean yates)
matrix
- the contigency tableyates
- is Yates' correction to be used?public static double chiVal(double[][] matrix, boolean useYates)
matrix
- the contigency tableuseYates
- is Yates' correction to be used?public static boolean cochransCriterion(double[][] matrix)
matrix
- the contingency table to be testedpublic static double CramersV(double[][] matrix)
matrix
- the contingency tablepublic static double entropy(double[] array)
array
- the arraypublic static double entropyConditionedOnColumns(double[][] matrix)
matrix
- the contingency tablepublic static double entropyConditionedOnRows(double[][] matrix)
matrix
- the contingency tablepublic static double entropyConditionedOnRows(double[][] train, double[][] test, double numClasses)
train
- the train matrixtest
- the test matrixnumClasses
- the number of symbols for Laplacepublic static double entropyOverRows(double[][] matrix)
matrix
- the contingency tablepublic static double entropyOverColumns(double[][] matrix)
matrix
- the contingency tablepublic static double gainRatio(double[][] matrix)
matrix
- the contingency tablepublic static double log2MultipleHypergeometric(double[][] matrix)
matrix
- the contingency tablepublic static double[][] reduceMatrix(double[][] matrix)
matrix
- the matrix to be reducedpublic static double symmetricalUncertainty(double[][] matrix)
matrix
- the contingency tablepublic static double tauVal(double[][] matrix)
matrix
- the contingency tablepublic static void main(java.lang.String[] ops)
Copyright © 2006-2014 - Thomas Abeel - All Rights Reserved.


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IS
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MILDRED
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Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Annie I. Anton, Colin Potts
Present a method to identify and abstract goals as part of software systems engineering.
"Stakeholders may forget or not be aware of requirements, and analysts, without the deep domain knowledge of most stakeholders, may not be able to fill in the gaps. In addition, som requirements and constraints my be so obvious to an expert stakeholder that they do not seem worth mentioning, but the thin spread of domain expertise among analyst teams means that these unstated requirements may no the recognized or incorporated into the development process."


Derry man (35) found unanimously not guilty of raping teen during Spanish holiday
A man has been found unanimously not guilty of raping a 17-year-old in the bedroom of a hotel they shared during a holiday in Spain.
Following just 28 minutes of deliberations, a jury at Derry Crown Court acquitted Michael Cregan (35), from Ferguson Street in the city, of a single charge of rape.
The complainant alleged that he raped her as she slept, following a day's drinking, in the room they shared with two other adults in the resort of Torremolinos in August 2014.
The defendant told the jury that they twice had consensual intercourse on the night in question.
It was the first case of its kind in the north in which a UK resident went on trial for an offence allegedly committed outside the jurisdiction.
Following the not guilty verdict, Judge Philip Babington told the defendant he was free to go.
Members of the complainant's family were visibly upset when the jury forewoman announced the not guilty verdict.
Subscribe to our most read headlines in the evening newsletter


Loan info
Loan Schemes
Apply for a loan
All loan applications
All current loans
All repaid loans
All repaid loans
Loan detsils
Loan approve
Debit Loan
All clients
Client details
Verify client
Loan Schemes
Apply for a loan
All loan applications
All current loans
All repaid loans
All repaid loans
Loan detsils
Loan approve
Debit Loan
All clients
Client details
Verify client


Emotional intelligence (EQ) - being aware of your emotions as well as the emotions of others
EQ has to do with how you relate, reflect and react to others and to yourself. IQ, on the other hand, is a more structured way of defining your intelligence. Cognitive intelligence is how you work through and achieve various outcomes within a standardized test.
It is important to understand both concepts and to see how they relate to each other but also how they are different. EQ & IQ are components of the whole person and to measure someone’s intelligence based on only one of these factors would be unfair. There will be areas where you are more competent than others and that is ok.
Where EQ plays a role in your personal or professional life is that it will allow you to understand not only your emotions but the emotions of others when you are interacting, listening or solving problems. By working on your EQ skills you will be in a better position to negotiate, resolve conflict and come to resolutions that are more beneficial to all parties.  This isn’t a ‘me’ game. As you explore and work through your own EQ you will become a better communicator and problem solver overall.


NES "Black Bass USA" Password Generator
Date:
6/ 15
6/ 25
7/ 5
7/ 15
7/ 25
8/ 5
8/ 15
8/ 25
9/ 5
9/ 15
9/ 25
Rating:
Black Bass:
Total Weight:
Low High
Heaviest:
Lure:
Pencil Bait
Frog
Shallow Runner
Spinner
Spoon
Plastic Worm
Colour:
Red
Blue
Silver
Random:
Password:
*San/Moore Lake requires "A" class and odd/even Date Number, respectively;
*Class and beginner's lakes depend on Rating (200/100/50);
*Average_Weight = Total_Weight / Black_Bass
By
Griever
. [2008]


MARKETING 728
Cable Tv 49$ & free tv
Yash pednekar
Rahul prabhakar
Karan Balaji
BUSINESS OBJECTIVES
Target Market
Meet Alex, he is a 24 year old student at University. He loves to
watch action movies and ice hockey. He earns around $1000 through his part time job.
Creative Idea
“ Bring Your Home Togetherness”
Meet Alex, he is a 24 year old student at University. He loves to watch action movies and ice hockey. He earns around $1000 through his part time job. >>>>>>> parent of 63fdefa... Preview
Integrated Marketing Campaign
| Sales Promotion Strategy (Trade and Customer) | Press Release | Digital Media Plan | Experiential Marketing OR Sponsorship |
|---|---|---|---|
| Tactic Details | Emotional connection and the nostalgia factor towards Television | Targeting towards millenials and the idea of togetherness | To target the older target market |
| Creative Translation | The taglines published would be talking about togetherness and the visual presentation of the whole idea | Millennials would be shown short video ads and images having an emotional tone towards family and friends. | Sponsoring events like Ice Hockey games, basketball games where banner ads and display ads would focus on the free TV aspect of the promotion. |
Email Ad
Meet Alex, he is a 24 year old student at University. He loves to watch action movies and ice hockey. He earns around $1000 through his part time job.
Budget and Kpi's
| Activity | Cost | How Measure | KPI Goal |
|---|---|---|---|
| Sales Promotion | Printing: $500 Distribution: $300 |
Redemption | 200(0.1%) |
| Short Video | Production: $4000 Distribution Rights: $2000 |
Impressions | 200,000 impressions |
| Sponsorship | Sponsorship Rights: $2000 | Chatter volume | increease by 13% |
| Creation and distribution: $1000 | Click through rates | 25% CTR |


Reset Zoom
Vph, Photoelectron Knee (First knee)
Vbar, Barrier Potential (Extra knee above Vph)
Vsc, Spacecraft Potential (Derivative roughly constant)
Disturbed sweep
Poorly calibrated
Submit!
<
Row:
{{row_id}}
>


Right now I am looking mostly at the U and neumont but I have alot that could change so we will see :)


-
15 Of The Most Interesting Maps In Google’s New Maps Gallery | TechCrunch
techcrunch.com
Google Maps released the new Maps Gallery to the public today. Previously, individuals and organizations could publicly share their custom maps, but ...
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Home - Google Science Fair 2014
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Google Science Fair is a global online competition open to students from 13 to 18 years old. What do you want to change?
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About - Google Maps
www.google.com
Discover the world with Google Maps. Experience Street View, 3D Mapping, turn-by-turn directions, indoor maps and more across your devices.
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Google Discussiegroepen
productforums.google.com
Met Google Discussiegroepen kun je online forums en e-mailgroepen maken en eraan deelnemen. Bovendien profiteer je van uitgebreide ...
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See who’s online more easily in Hangouts in Gmail Starting today it's ...
plus.google.com
See who’s online more easily in Hangouts in Gmail Starting today it's easier to see which of your chat buddies are currently online, thanks to a new ...


Enter a big ol' blob of JSON data (arrays only) into the
<textarea>
below and click Start.
Lazy.js will parse as much
of the JSON as possible, asynchronously. (Tip: try putting some
garbage in the middle to see how Lazy can parse the beginning even if
the overall string is invalid.)
Click Cancel at any point to stop parsing.
Optionally, also transform the data:
filter(function(data) { return typeof data === 'string'; })


Text
Audio
Video
Image
Code
Other
Directory
# of layers:
Select type:
1
2
3
Filter by:
Time
Type
Size
# of Files


Choices.js is a lightweight, configurable select box/text input plugin. Similar to Select2 and Selectize but without the jQuery dependency.
For all config options, visit the GitHub repo.
If the following example do not load, the Discogs rate limit has probably been reached. Try again later!
If the following two examples do not load, the Discogs rate limit has probably been reached. Try again later!
Below is an example of how you could have two select inputs depend on eachother. 'Boroughs' will only be enabled if the value of 'States' is 'New York'


Almaty, Kazakhstan
@Alisher Bazarkhanov | Creating New Inc.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


Yesterday's advent calendar picture was so terrifying that you have probably had a fit of the vapours and taken to your bed, whimpering and neurasthenic. In such circumstances, you will be subsisting on a diet of invalid food, so voila! today's picture shows a lovely bowl of pap ‘n’ slops. Yum!
from Toast


Dear {{fullname}},
We are writing to let you know that {{patient.fullname}}'s appointment with you has been canceled. Kindly click on the link below to manage your schedule
My Schedule
Regards,
The Neolafia Team


Hi there, here's your list of new words:
{% for definition in definitions %}
{{ definition }}
{% endfor %}


Garden Care
Seeking justice in the world is a sed significant emotional and investment when we follow this call.
{% extends "base.html" %} {% block title %}COR. ONE{% endblock %} {% block content %}
A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which roasted parts of sentences fly into your.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.


test name 1
test name 2
fill test data
Enter player Id
Enter player price for selling
Info
All players
All Available players
All Transfer players
owner all players
owner all available players
owner all transfer players
Buy player
Sell player


A class to do ACS photometry.
Methods
| __init__(path, out) | |
| calculateZeropoint(photflam) | Calculates the zeropoint from a given header keyword photflam value. |
| calculateac05(input, column, operation, ...) | Can be used to calculate the ac05 (from 3 pix to 10pix) aperture correction. |
| changeColumn(table, column, name, fmt, unit) | Changes the column name and format that IRAF supports. Also unit can be |
| correctMagnitude(input, column, operation, ...) | Essentially the same as calculateac05 function. |
| displayDS9(image, buffer) | Displays the given image on DS9. The function requires that |
| doImcalc(input, output, operation) | Simple interface for IRAF task imcalc. Can be used for simple |
| doPhotometryACS(file, inputcoords, ...) | This function can be used to do photometry from a given image. |
| gStat(file) | Calculates statistics from given image. |
| getBandpar(configuration) | |
| getMeaningfulColumns(input, columns, output) | Uses IRAF task tdump for parsing columns from a table. |
| getSize(file) | This function can be used to get the size of the image. |
| hdiff(file1, file2) | Compares headers from two files with hdiff. |
| interactiveImexam(image, frame) | Can be used to call interactive IRAF task imexam for a given frame. DS9 must be |
| markStars(data, buffer) | Can be used to show an overlapping image on DS9 buffer. The data is taken |
| mergeTables(merged, output) | Can be used to merge multiple tables together. |
| omitPhotcorr(file) | This function will change PHOTCORR keyword value to OMIT in the header |
| queryKeywords(file, keywords) | Queries keywords from header. |
| runCalACS(file) | Runs calacs for the given file. |


| rcmdcheck {rcmdcheck} | R Documentation |
Run R CMD check form R programatically, and capture the results of the individual checks.
rcmdcheck(path = ".", quiet = FALSE, args = character(), build_args = character(), check_dir = NULL, libpath = .libPaths(), repos = getOption("repos"), timeout = Inf, error_on = c("never", "error", "warning", "note"))
path |
Path to a package tarball or a directory. |
quiet |
Whether to print check output during checking. |
args |
Character vector of arguments to pass to
|
build_args |
Character vector of arguments to pass to
|
check_dir |
Path to a directory where the check is performed.
If this is not |
libpath |
The library path to set for the check. The default uses the current library path. |
repos |
The |
timeout |
Timeout for the check, in seconds, or as a
base::difftime object. If it is not finished before this, it will be
killed. |
error_on |
Whether to throw an error on |
An S3 object (list) with fields errors
,
warnings
and notes
. These are all character
vectors containing the output for the failed check.


8 Data transformation
This section focusses on transforming rectangular datasets.
8.1 Set up
The sa3_income
dataset will be used for all key examples in this chapter.1 It is a long dataset from the ABS that contains the average income and number of workers by Statistical Area 3, occupation and sex between 2010 and 2016.
## Parsed with column specification:
## cols(
## sa3 = col_double(),
## sa3_name = col_character(),
## sa3_sqkm = col_double(),
## sa3_income_percentile = col_double(),
## sa4_name = col_character(),
## gcc_name = col_character(),
## state = col_character(),
## occupation = col_character(),
## occ_short = col_character(),
## prof = col_character(),
## gender = col_character(),
## year = col_double(),
## median_income = col_double(),
## average_income = col_double(),
## total_income = col_double(),
## workers = col_double()
## )
## # A tibble: 6 x 16
## sa3 sa3_name sa3_sqkm sa3_income_perc… sa4_name gcc_name state
## <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 10102 Queanbe… 6511. 80 Capital… Rest of… NSW
## 2 10102 Queanbe… 6511. 76 Capital… Rest of… NSW
## 3 10102 Queanbe… 6511. 78 Capital… Rest of… NSW
## 4 10102 Queanbe… 6511. 76 Capital… Rest of… NSW
## 5 10102 Queanbe… 6511. 74 Capital… Rest of… NSW
## 6 10102 Queanbe… 6511. 79 Capital… Rest of… NSW
## # … with 9 more variables: occupation <chr>, occ_short <chr>, prof <chr>,
## # gender <chr>, year <dbl>, median_income <dbl>, average_income <dbl>,
## # total_income <dbl>, workers <dbl>
8.2 The pipe: %>%
You will almost always want to perform more than one of the operations described below on your dataset. One way to perform multiple operations, one after the other, is to ‘nest’ them inside. This nesting will be painfully familiar to Excel users.
Consider an example of baking and eating a cake.2 You take the ingredients, combine them, then mix, then bake, and then eat them. In a nested formula, this process looks like:
In a nested formula, you need to start in the middle and work your way out. This means anyone reading your code – including you in the future! – needs to start in the middle and work their way out. But because we’re used to left-right reading, we’re not particularly good at naturally interpreting nested functions like this one.
This is where the ‘pipe’ can help. The pipe operator %>%
(keyboard shortcut: cmd + shift + m
) takes an argument on the left and ‘pipes’ it into the function on the right. Each time you see %>%
, you can read it as ‘and then’.
So the you could express the baking example as:
ingredients %>% # and then
combine() %>% # and then
mix() %>% # and then
bake() %>% # and then
eat() # yum!
Which reads as:
take the
ingredients
, thencombine
, thenmix
, thenbake
, theneat
them.
This does the same thing as eat(bake(mix(combine(ingredients))))
. But it’s much nicer and more natural to read, and to write.
Another example: the function paste
takes arguments and combines them together into a single string. So you could use the pipe to:
## [1] "hello dear reader"
which is the same as
## [1] "hello dear reader"
Or you could define a vector of numbers and pass3 them to the sum()
function:
## [1] 32
Or you could skip the intermediate step altogether:
## [1] 32
This is the same as
## [1] 32
The benefits of piping become more clear when you want to perform a few sequential operations on a dataset. For example, you might want to filter
the observations in the sa3_income
data to only NSW
, before you group_by
gender
and summarise
the av_income
of these grops (these functions are explained in detail below). All of these functions take ‘data’ as the first argument, and are designed to be used with pipes.
Like the income differential it shows, writing this process as a nested function is outrageous and hard to read:
## # A tibble: 2 x 2
## gender av_mean_income
## <chr> <dbl>
## 1 Men 58202.
## 2 Women 41662.
The original common way to avoid this unseemly nesting in R
was to assign each ‘step’ its own object, which is definitely clearer:
data1 <- filter(sa3_income, state == "NSW")
data2 <- group_by(data1, gender)
data3 <- summarise(data2, av_mean_income = mean(av_income))
data3
## # A tibble: 2 x 2
## gender av_mean_income
## <chr> <dbl>
## 1 Men 58202.
## 2 Women 41662.
And using pipes make the steps clearer still:
- take the
sa3_income
data, and then %>% filter
it to only NSW, and then %>%group
it by gender, and then %>%summarise
it
sa3_income %>% # and then
filter(state == "NSW") %>% # and then
group_by(gender) %>% # and then
summarise(av_mean_income = mean(av_income))
## # A tibble: 2 x 2
## gender av_mean_income
## <chr> <dbl>
## 1 Men 58202.
## 2 Women 41662.
8.3 Key dplyr
functions:
All have the same syntax structure, which enable pipe-chains.
8.4 Select variables with select()
The select
function takes a dataset and keeps or drops variables (columns) that are specified.
For example, look at the variables that are in the sa3_income
dataset (using the names()
function):
## [1] "year" "sa3_name" "state" "gender" "av_income" "workers"
If you wanted to keep just the state
and av_income
variables, you could take the sa3_income
dataset and select just those variables:
## # A tibble: 4,019 x 2
## state av_income
## <chr> <dbl>
## 1 SA 48760.
## 2 SA 38359.
## 3 SA 54343.
## 4 SA 35889.
## 5 WA 48659.
## 6 WA 32629.
## 7 NSW 49330.
## 8 NSW 35300.
## 9 NT 52602.
## 10 NT 41401.
## # … with 4,009 more rows
Or you could use -
(minus) to remove the state
and sa3_name
variables:4
## # A tibble: 4,019 x 4
## year gender av_income workers
## <dbl> <chr> <dbl> <dbl>
## 1 2011 Men 48760. 13737
## 2 2011 Women 38359. 4311
## 3 2011 Men 54343. 44904
## 4 2011 Women 35889. 14890
## 5 2011 Men 48659. 32915
## 6 2011 Women 32629. 10896
## 7 2011 Men 49330. 38139
## 8 2011 Women 35300. 12351
## 9 2011 Men 52602. 23663
## 10 2011 Women 41401. 7900
## # … with 4,009 more rows
8.4.1 Selecting groups of variables
Sometimes it can be useful to keep or drop variables with names that have a certain characteristic; they begin with some text string, or end with one, or contain one, or have some other pattern altogether.
You can use patterns and ‘select helpers’5 from the Tidyverse to help deal with these sets of variables.
For example, if you want to keep just the SA3 and SA4 variables – ie the variables that start with "sa"
– you could:
## # A tibble: 4,019 x 1
## sa3_name
## <chr>
## 1 Adelaide City
## 2 Adelaide City
## 3 Adelaide Hills
## 4 Adelaide Hills
## 5 Albany
## 6 Albany
## 7 Albury
## 8 Albury
## 9 Alice Springs
## 10 Alice Springs
## # … with 4,009 more rows
Or, instead, if you wanted to keep just the variables that contain "er"
, you could:
## # A tibble: 4,019 x 2
## gender workers
## <chr> <dbl>
## 1 Men 13737
## 2 Women 4311
## 3 Men 44904
## 4 Women 14890
## 5 Men 32915
## 6 Women 10896
## 7 Men 38139
## 8 Women 12351
## 9 Men 23663
## 10 Women 7900
## # … with 4,009 more rows
And if you wanted to keep both the "sa"
variables and the "er"
variables, you could:
## # A tibble: 4,019 x 3
## sa3_name gender workers
## <chr> <chr> <dbl>
## 1 Adelaide City Men 13737
## 2 Adelaide City Women 4311
## 3 Adelaide Hills Men 44904
## 4 Adelaide Hills Women 14890
## 5 Albany Men 32915
## 6 Albany Women 10896
## 7 Albury Men 38139
## 8 Albury Women 12351
## 9 Alice Springs Men 23663
## 10 Alice Springs Women 7900
## # … with 4,009 more rows
The full list of these handy select functions are provided with the ?tidyselect::select_helpers
documentation, listed below:
starts_with()
: Starts with a prefix.ends_with()
: Ends with a suffix.contains()
: Contains a literal string.matches()
: Matches a regular expression.num_range()
: Matches a numerical range like x01, x02, x03.one_of()
: Matches variable names in a character vector.everything()
: Matches all variables.last_col()
: Select last variable, possibly with an offset.
8.5 Filter with filter()
The filter
function takes a dataset and keeps observations (rows) that meet the conditions.
filter
has one required first argument – the data – and then as many ‘conditions’ as you want to provide.
8.5.1 Conditions; logical operations; TRUE
or FALSE
The conditions are logical operations, meaning they are a statement that return either TRUE
or FALSE
in the computer’s mind.6
We know, for instance, that 1 + 2
does not equal 12
. Which means if we type 1 + 2 == 12
into the console it should give FALSE
:
## [1] FALSE
Or, we can see if 1 + 2
is equal 5
or 9
or 3
by providing a vector of those numbers:
## [1] FALSE FALSE TRUE
This works for character strings, too:
## [1] FALSE TRUE FALSE
A lot of what we do in ‘data science’ is based on these TRUE
and FALSE
conditions.
8.5.2 Filtering data with filter
Turning back to the sa3_income
data, if you just wanted to see observations people in NT
:
## # A tibble: 123 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2011 Alice Springs NT Men 52602. 23663
## 2 2011 Alice Springs NT Women 41401. 7900
## 3 2011 Barkly NT Men 50517. 2272
## 4 2011 Barkly NT Women 41478. 698
## 5 2011 Christmas Island NT Men 69033. 1036
## 6 2011 Christmas Island NT Women 50373. 256
## 7 2011 Daly - Tiwi - West Arnhem NT Men 51421. 3775
## 8 2011 Daly - Tiwi - West Arnhem NT Women 39272. 1151
## 9 2011 Darwin City NT Men 57199. 24274
## 10 2011 Darwin City NT Women 44391. 7157
## # … with 113 more rows
Or you might just want to look at high-income (av_income > 100,000
) areas from Victoria in 2015:
## # A tibble: 1 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2015 Bayside Vic Men 77175. 62460
Each of the commas in the filter
function represent an ‘and’ &
. So you can read the steps above as:
take the
sa3_income
data and filter to keep only the observations that are from Victoria,
and that have a average income above 100k,
and are from the year 2015.
Sometimes you might want to relax a little, keeping observations from one category or another. You can do this with the or symbol: |
7
sa3_income %>%
filter(state == "Vic" | state == "Tas",
av_income > 100000,
year == 2015 | year == 2016)
## # A tibble: 0 x 6
## # … with 6 variables: year <dbl>, sa3_name <chr>, state <chr>,
## # gender <chr>, av_income <dbl>, workers <dbl>
Which reads:
take the
sa3_income
data and filter to keep only the observations that are from Victoria OR NSW, and that have a average income above 100k, and are from the year 2015 OR 2016.
8.5.3 Grouped filtering with group_by()
The group_by
function groups a dataset by given variables. This effectively generates one dataset per group within your main dataset. Any function you then apply – like filter()
– will be applied to each of the grouped datasets.
For example, you could filter the sa3_income
dataset to keep just the observation with the highest average income:
## # A tibble: 1 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2015 West Pilbara WA Men 107844. 22928
To keep the observations that have the highest average incomes in each state, you can group_by
state, then filter
:8
## # A tibble: 8 x 6
## # Groups: state [8]
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2013 Molonglo ACT Men 92947. 227
## 2 2015 Gladstone Qld Men 97282. 48026
## 3 2015 Outback - North and East SA Men 71791. 15849
## 4 2015 West Pilbara WA Men 107844. 22928
## 5 2016 Bayside Vic Men 78624. 64541
## 6 2016 Christmas Island NT Men 84474. 621
## 7 2016 North Sydney - Mosman NSW Men 90668. 74702
## 8 2016 West Coast Tas Men 58116. 11117
From the description of the tibble above, you can learn that your data has 8 unique groups of state:
## # Groups: state [8]
Or you could keep the observations with the lowest average incomes in each state and year:9
## # A tibble: 48 x 6
## # Groups: state, year [48]
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2011 Belconnen ACT Women 43235 22708
## 2 2011 Daly - Tiwi - West Arnhem NT Women 39272. 1151
## 3 2011 Lower Murray NSW Women 30800. 2122
## 4 2011 Murray River - Swan Hill Vic Women 29889. 6930
## 5 2011 Noosa Qld Women 31786. 6922
## 6 2011 North East Tas Women 31569. 6579
## 7 2011 Wheat Belt - South WA Women 31958. 3727
## 8 2011 Yorke Peninsula SA Women 28975. 3732
## 9 2012 Albany WA Women 34853. 11183
## 10 2012 Daly - Tiwi - West Arnhem NT Women 42567. 1100
## # … with 38 more rows
The dataset remains grouped after your function(s). To explicitly ‘ungroup’ your data, add the ungroup
function to your chain (the ‘Groups’ note has disappeared in the below):
## # A tibble: 48 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2011 Belconnen ACT Women 43235 22708
## 2 2011 Daly - Tiwi - West Arnhem NT Women 39272. 1151
## 3 2011 Lower Murray NSW Women 30800. 2122
## 4 2011 Murray River - Swan Hill Vic Women 29889. 6930
## 5 2011 Noosa Qld Women 31786. 6922
## 6 2011 North East Tas Women 31569. 6579
## 7 2011 Wheat Belt - South WA Women 31958. 3727
## 8 2011 Yorke Peninsula SA Women 28975. 3732
## 9 2012 Albany WA Women 34853. 11183
## 10 2012 Daly - Tiwi - West Arnhem NT Women 42567. 1100
## # … with 38 more rows
8.6 Edit and add new variables with mutate()
To add new variables to your dataset, use the mutate
function. Like all dplyr
verbs, the first argument to mutate
is your data. Then define variables using a new_variable_name = x
format, where x
can be a single number or character string, or simple operation or function using current variables.
To add a new variable to the sa3_income
dataset that shows the log the number of workers:
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers log_workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2011 Adelaide City SA Men 48760. 13737 9.53
## 2 2011 Adelaide City SA Women 38359. 4311 8.37
## 3 2011 Adelaide Hills SA Men 54343. 44904 10.7
## 4 2011 Adelaide Hills SA Women 35889. 14890 9.61
## 5 2011 Albany WA Men 48659. 32915 10.4
## 6 2011 Albany WA Women 32629. 10896 9.30
## 7 2011 Albury NSW Men 49330. 38139 10.5
## 8 2011 Albury NSW Women 35300. 12351 9.42
## 9 2011 Alice Springs NT Men 52602. 23663 10.1
## 10 2011 Alice Springs NT Women 41401. 7900 8.97
## # … with 4,009 more rows
To edit a variable, redefine it in mutate
. For example, if you wanted to take the last two digits of year:
## # A tibble: 4,019 x 6
## year sa3_name state gender av_income workers
## <int> <chr> <chr> <chr> <dbl> <dbl>
## 1 11 Adelaide City SA Men 48760. 13737
## 2 11 Adelaide City SA Women 38359. 4311
## 3 11 Adelaide Hills SA Men 54343. 44904
## 4 11 Adelaide Hills SA Women 35889. 14890
## 5 11 Albany WA Men 48659. 32915
## 6 11 Albany WA Women 32629. 10896
## 7 11 Albury NSW Men 49330. 38139
## 8 11 Albury NSW Women 35300. 12351
## 9 11 Alice Springs NT Men 52602. 23663
## 10 11 Alice Springs NT Women 41401. 7900
## # … with 4,009 more rows
8.6.1 Using if_else()
or case_when()
Sometimes you want to create a new variable based on some sort of condition. Like, if the number of workers in an sa3
is more than 2,000
, set the new many_workers
variable to TRUE
, and set it to FALSE
otherwise.
This kind of operation can be thought of as if_else
: if
(some condition), do this, otherwise do that.
That’s what the if_else()
function does. It takes three arguments: a condition, a value if that condition is true, and a value if that condition is false.
You can use the if_else()
function when you are creating new variables in a mutate
command:
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers many_workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 Many workers
## 2 2011 Adelaide City SA Women 38359. 4311 Many workers
## 3 2011 Adelaide Hills SA Men 54343. 44904 Many workers
## 4 2011 Adelaide Hills SA Women 35889. 14890 Many workers
## 5 2011 Albany WA Men 48659. 32915 Many workers
## 6 2011 Albany WA Women 32629. 10896 Many workers
## 7 2011 Albury NSW Men 49330. 38139 Many workers
## 8 2011 Albury NSW Women 35300. 12351 Many workers
## 9 2011 Alice Springs NT Men 52602. 23663 Many workers
## 10 2011 Alice Springs NT Women 41401. 7900 Many workers
## # … with 4,009 more rows
Which reads:
Take the
sa3_income
dataset, and then add a variable that says ‘Many workers’ if there are more than 2,000 workers, and ‘Not many workers’ if there are fewer-or-equal than 2,000 workers.
With the if_else
function, you take one conditional statement and return something based on that. But often you don’t want to be so binary; you want to do this if this is true, that if that is true, and the other if the other is true, etc.
This could be done by nesting if_else
statements:
sa3_income %>%
mutate(worker_group = if_else(workers > 2000, "More than 2000 workers",
if_else(workers > 1000, "1000-2000 workers",
if_else(workers > 500, "500-1000 workers",
"500 workers or less"))))
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers worker_group
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 More than 2000 work…
## 2 2011 Adelaide City SA Women 38359. 4311 More than 2000 work…
## 3 2011 Adelaide Hills SA Men 54343. 44904 More than 2000 work…
## 4 2011 Adelaide Hills SA Women 35889. 14890 More than 2000 work…
## 5 2011 Albany WA Men 48659. 32915 More than 2000 work…
## 6 2011 Albany WA Women 32629. 10896 More than 2000 work…
## 7 2011 Albury NSW Men 49330. 38139 More than 2000 work…
## 8 2011 Albury NSW Women 35300. 12351 More than 2000 work…
## 9 2011 Alice Springs NT Men 52602. 23663 More than 2000 work…
## 10 2011 Alice Springs NT Women 41401. 7900 More than 2000 work…
## # … with 4,009 more rows
But that syntax can be a bit difficult to read. You can do this in a clearer way using case_when
:
sa3_income %>%
mutate(worker_group = case_when(
workers > 2000 ~ "More than 2000 workers",
workers > 1000 ~ "More than 1000 workers",
workers > 500 ~ "More than 500 workers",
workers <= 500 ~ "500 or fewer workers"
))
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers worker_group
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 More than 2000 work…
## 2 2011 Adelaide City SA Women 38359. 4311 More than 2000 work…
## 3 2011 Adelaide Hills SA Men 54343. 44904 More than 2000 work…
## 4 2011 Adelaide Hills SA Women 35889. 14890 More than 2000 work…
## 5 2011 Albany WA Men 48659. 32915 More than 2000 work…
## 6 2011 Albany WA Women 32629. 10896 More than 2000 work…
## 7 2011 Albury NSW Men 49330. 38139 More than 2000 work…
## 8 2011 Albury NSW Women 35300. 12351 More than 2000 work…
## 9 2011 Alice Springs NT Men 52602. 23663 More than 2000 work…
## 10 2011 Alice Springs NT Women 41401. 7900 More than 2000 work…
## # … with 4,009 more rows
The case_when
function takes the first condition (LHS) and applies some value (RHS) if it is true. It then moves to the next condition, and so on. Once an observation has been classified – eg an observation has more than 2000 workers – it is ignored in proceeding conditions.
Ending a case_when
statement with TRUE ~ [some value]
is a catch all, and will apply the RHS [some value]
to any observations that did not meet an explicit condition. For example, you could end the worker classification with:
sa3_income %>%
mutate(worker_group = case_when(
workers > 2000 ~ "More than 2000 workers",
workers > 1000 ~ "More than 1000 workers",
workers > 500 ~ "More than 500 workers",
TRUE ~ "500 or fewer workers"
))
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers worker_group
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 More than 2000 work…
## 2 2011 Adelaide City SA Women 38359. 4311 More than 2000 work…
## 3 2011 Adelaide Hills SA Men 54343. 44904 More than 2000 work…
## 4 2011 Adelaide Hills SA Women 35889. 14890 More than 2000 work…
## 5 2011 Albany WA Men 48659. 32915 More than 2000 work…
## 6 2011 Albany WA Women 32629. 10896 More than 2000 work…
## 7 2011 Albury NSW Men 49330. 38139 More than 2000 work…
## 8 2011 Albury NSW Women 35300. 12351 More than 2000 work…
## 9 2011 Alice Springs NT Men 52602. 23663 More than 2000 work…
## 10 2011 Alice Springs NT Women 41401. 7900 More than 2000 work…
## # … with 4,009 more rows
Meaning, for any observation that did not have workers more than 2000 or more than 1000 or more than 500, assign the value "500 or fewer workers"
.
Observations that do not meet a condition will be set to NA
:
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers worker_group
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 <NA>
## 2 2011 Adelaide City SA Women 38359. 4311 <NA>
## 3 2011 Adelaide Hills SA Men 54343. 44904 <NA>
## 4 2011 Adelaide Hills SA Women 35889. 14890 <NA>
## 5 2011 Albany WA Men 48659. 32915 <NA>
## 6 2011 Albany WA Women 32629. 10896 <NA>
## 7 2011 Albury NSW Men 49330. 38139 <NA>
## 8 2011 Albury NSW Women 35300. 12351 <NA>
## 9 2011 Alice Springs NT Men 52602. 23663 <NA>
## 10 2011 Alice Springs NT Women 41401. 7900 <NA>
## # … with 4,009 more rows
Like any if
or if_else
, you can provide more than one condition to your conditional statement:
sa3_income %>%
mutate(women_group = case_when(
gender == "Women" & workers > 2000 ~ "More than 2000 women",
gender == "Women" & workers > 1000 ~ "More than 1000 women",
gender == "Women" & workers > 500 ~ "More than 500 women",
gender == "Women" ~ "500 or fewer women",
TRUE ~ "Men"
))
## # A tibble: 4,019 x 7
## year sa3_name state gender av_income workers women_group
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 2011 Adelaide City SA Men 48760. 13737 Men
## 2 2011 Adelaide City SA Women 38359. 4311 More than 2000 women
## 3 2011 Adelaide Hills SA Men 54343. 44904 Men
## 4 2011 Adelaide Hills SA Women 35889. 14890 More than 2000 women
## 5 2011 Albany WA Men 48659. 32915 Men
## 6 2011 Albany WA Women 32629. 10896 More than 2000 women
## 7 2011 Albury NSW Men 49330. 38139 Men
## 8 2011 Albury NSW Women 35300. 12351 More than 2000 women
## 9 2011 Alice Springs NT Men 52602. 23663 Men
## 10 2011 Alice Springs NT Women 41401. 7900 More than 2000 women
## # … with 4,009 more rows
8.6.2 Grouped mutates with group_by()
Like filtering, you can add or edit variables on grouped data. For example, you could get the average number of workers in each SA3 over the 6 years:
## # A tibble: 4,019 x 7
## # Groups: sa3_name, gender [672]
## year sa3_name state gender av_income workers av_workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2011 Adelaide City SA Men 48760. 13737 14072.
## 2 2011 Adelaide City SA Women 38359. 4311 4315.
## 3 2011 Adelaide Hills SA Men 54343. 44904 46244
## 4 2011 Adelaide Hills SA Women 35889. 14890 15295.
## 5 2011 Albany WA Men 48659. 32915 34964.
## 6 2011 Albany WA Women 32629. 10896 11416
## 7 2011 Albury NSW Men 49330. 38139 38815.
## 8 2011 Albury NSW Women 35300. 12351 12549.
## 9 2011 Alice Springs NT Men 52602. 23663 23854.
## 10 2011 Alice Springs NT Women 41401. 7900 7993.
## # … with 4,009 more rows
Above, the mean()
function is applied separately to each unique group of sa3_name
and gender
, taking one average for women in Queanbeyan, one average for men in Queanbeyan, and so on.
Grouping a dataset does not prohibit operations that don’t utilise the grouping. For example, you could get each year’s workers relative to the SA3/gender average in the same call to mutate
:
sa3_income %>%
group_by(sa3_name, gender) %>%
mutate(av_workers = mean(workers),
worker_diff = workers / av_workers)
## # A tibble: 4,019 x 8
## # Groups: sa3_name, gender [672]
## year sa3_name state gender av_income workers av_workers worker_diff
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2011 Adelaide Ci… SA Men 48760. 13737 14072. 0.976
## 2 2011 Adelaide Ci… SA Women 38359. 4311 4315. 0.999
## 3 2011 Adelaide Hi… SA Men 54343. 44904 46244 0.971
## 4 2011 Adelaide Hi… SA Women 35889. 14890 15295. 0.974
## 5 2011 Albany WA Men 48659. 32915 34964. 0.941
## 6 2011 Albany WA Women 32629. 10896 11416 0.954
## 7 2011 Albury NSW Men 49330. 38139 38815. 0.983
## 8 2011 Albury NSW Women 35300. 12351 12549. 0.984
## 9 2011 Alice Sprin… NT Men 52602. 23663 23854. 0.992
## 10 2011 Alice Sprin… NT Women 41401. 7900 7993. 0.988
## # … with 4,009 more rows
See that the data remains grouped after the mutate
. You can explicitly ungroup()
afterwards:
sa3_income %>%
group_by(sa3_name, gender) %>%
mutate(av_workers = mean(workers),
worker_diff = workers / av_workers) %>%
ungroup()
## # A tibble: 4,019 x 8
## year sa3_name state gender av_income workers av_workers worker_diff
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2011 Adelaide Ci… SA Men 48760. 13737 14072. 0.976
## 2 2011 Adelaide Ci… SA Women 38359. 4311 4315. 0.999
## 3 2011 Adelaide Hi… SA Men 54343. 44904 46244 0.971
## 4 2011 Adelaide Hi… SA Women 35889. 14890 15295. 0.974
## 5 2011 Albany WA Men 48659. 32915 34964. 0.941
## 6 2011 Albany WA Women 32629. 10896 11416 0.954
## 7 2011 Albury NSW Men 49330. 38139 38815. 0.983
## 8 2011 Albury NSW Women 35300. 12351 12549. 0.984
## 9 2011 Alice Sprin… NT Men 52602. 23663 23854. 0.992
## 10 2011 Alice Sprin… NT Women 41401. 7900 7993. 0.988
## # … with 4,009 more rows
8.7 Summarise data with summarise()
Summarising is a useful way to assess and present data. The summarise
function collapses your data down into a single row, performing the operation(s) you provide:
sa3_income %>%
summarise(mean_income = mean(av_income),
total_workers = sum(workers)) # this is a silly statistic
## # A tibble: 1 x 2
## mean_income total_workers
## <dbl> <dbl>
## 1 50272. 117002608
8.7.1 Grouped summaries with group_by()
8.8 Arrange with arrange()
Sorting data in one way or another is useful because XXXX.
## # A tibble: 670 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2016 Lord Howe Island NSW Women 37944 74
## 2 2016 Urriarra - Namadgi ACT Women 86672. 90
## 3 2016 Christmas Island NT Women 57640 141
## 4 2016 Canberra East ACT Women 52091. 182
## 5 2016 Lord Howe Island NSW Men 40292 255
## 6 2016 Urriarra - Namadgi ACT Men 86747. 296
## 7 2016 Christmas Island NT Men 84474. 621
## 8 2016 Barkly NT Women 52552. 704
## 9 2016 Canberra East ACT Men 58035. 711
## 10 2016 Daly - Tiwi - West Arnhem NT Women 50096. 1075
## # … with 660 more rows
Or:
## # A tibble: 670 x 6
## year sa3_name state gender av_income workers
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 2016 Sydney Inner City NSW Men 62770. 190906
## 2 2016 Wyndham Vic Men 60741. 150321
## 3 2016 Stirling WA Men 71653. 141758
## 4 2016 Whittlesea - Wallan Vic Men 58728. 140106
## 5 2016 Wanneroo WA Men 71374. 130464
## 6 2016 Townsville Qld Men 65773. 129587
## 7 2016 Geelong Vic Men 61040. 121606
## 8 2016 Joondalup WA Men 76514. 120019
## 9 2016 Brimbank Vic Men 54252 119954
## 10 2016 Dandenong Vic Men 51872. 116742
## # … with 660 more rows
8.9 Joining datasets with *_join()
From ABS Employee income by occupation and sex, 2010-11 to 2016-16↩
XXX cannot remember the source for this example; probably Hadley? Jenny Bryan? Maybe somenone else?↩
‘pass’ can also be used to mean ‘pipe’.↩
This is the same as keeping everything except the
state
andsa3_name
variables.↩Explained in useful detail by the Tidyverse people at https://tidyselect.r-lib.org/reference/select_helpers.html↩
Computers’ mind, more likely.↩
On the keyboard:
shift
+backslash
↩Wow they are all men!↩
Wow they are all women!↩


How many coins do you get every of the 20 rounds ?
20 10 5 Other / not clear from instructions
If you put 5 in the group account, what is the lowest payment you are guaranteed from this round ?
5 7.5 10 Other / not clear from instructions
If you put 5 in the group account, and all others do the same, how much are you guaranteed from this round ?
7.5 15 20 Other / not clear from instructions
Are you more likely to be matched in a group with other high-contributers if you also put in a large amount than if you put in only a small amount ?
Yes No true if<5, false if>5 Other / not clear from instructions


My name is raed eid i am from Tafila and i am 24 years old i gradutaed from ttu as mining enginnering with
bachlor degree i like movie and music also i watch many sports like basketball i am fan los angloslakers
also
i love vdieo game my target in live to being software engineer casue programming is my patient


Following are some hints on how to use Electronic Battle Mat
Electronic Battle mat is a Web Application to help DMs and players have a better experience for running a role playing game. It encompasses four modes of operation.
Template dungeons contain all the maps and resources needed for managing a single adventure. It is called a template because it is not actually used directly by end users. This is because multiple groups could be playing the same dungeon at the same time and you would not want them to interfere with each other.
A dungeon session is a copy of a dungeon template that is owned by a certain group. This session is private so it can be changed without interfering with other groups playing the same one.
In this mode of operation a DM takes control of a session. They can do may things like clear areas of FOG of WAR so players can see stuff. They can make monsters and room objects visible or invisible to players. They can move pogs around the map to show correct positions. They can add new stuff like monsters that were not in the dungeon template SURPRISE this dragon happened by and wants to eat your face.
In this mode of operation a player can view the Battle Mat. They have very little functionality other than panning and zooming the map. They can also create a PC pog and move it around the visible areas of the map.
This is how the editor should look when it is first opened up on a new dungeon created from the "Template" dungeon.
This will show the picture of the currently selected Pog. You can click and drag in this area to place a copy of the pog onto the map.
This is where you select which dungeon level you wish to view or edit.
By checking this box you will cause a floating dialog window to appear. This window will allow you to view and edit the notes placed on a pog. You can edit both regular notes which are viewable by the players or DM notes only viewable by the DM.
By checking this box you will cause a floating dialog window to appear. This window will allow you to view the picture of the selected pog. You can resize the window by dragging in the lower right corner allowing you to get a better view.
By clicking here you will get this help page.
This area is where the map and all the objects placed on it appear. When you are DMing a session this will also display the areas that have Fog of War (FOW) on them. It will be transparent to the DM but will be solid black to the players preventing them from seeing stuff. Following are a few things you can do with and to this area.
By clicking on the map and holding the mouse button down you can change the position of the map.
By spinning the center mouse button you can zoom in and out of the map.
You can click on a pog and drag it to a new spot.
By dragging and dropping a picture of a pog from the ribbon bar you will create a new pog on the map. This is a copy of the pog and not the original. So if you want two more of a monster already on the map. You just need to select it so it shows in the ribbon bar then drag and drop twice from the ribbon bar.
By right clicking on a pog you can change some properties. Like make it visible or invisible to players or give it a number to help players more easily identify it. You can even delete it from the map.
Dms have the ability to adjust what the players see I.E. change the fog of war. This can be done several ways only when DMing a session
The DM can enable this mode by clicking in the checkbox. Once in this mode you can click on the map in a grid square. The FOW in that square will toggle. If you hold down the mouse button and move around the map other squares will also change to what the starting one was.
The DM can change FOW by just holding SHIFT key down and clicking in a square. The FOW in that square will toggle. If you hold down the mouse button and move around the map other squares will also change to what the starting one was.
The DM can change FOW by just holding Control key down and clicking in a square. Now you drag to form a red rectangle. All the squares in to rectangle will change to the opposite of the first one you clicked in.
This area allows the DM to edit and create all kinds of information related to the dungeon. It is broken into different tabs to help separate functionality.

These are the steps to create a new dungeon. You have access to this dialog just after you log in.
This opens up all the operations and privileges of a Dungeon Master.
In the text box labeled "Enter Dungeon Name" enter the name of your new dungeon. You can use letters, numbers and spaces in the name. The name must be greater than 4 letters long and cannot match an existing name. Once a legal name is entered the "Create New Dungeon" button will be enabled.
This will create a new dungeon with the entered name by copying the "Template" dungeon. It should now be visible in the "Select a Dungeon for Operations" list.
These are the steps to edit a dungeon.
This opens up all the operations and privileges of a Dungeon Master.
In the drop down called "Select a Dungeon for Operations" select a dungeon to edit. This will enable the "Edit Dungeon" button.
This will load in the dungeon and display the first level map. The ribbon bar will also map to the available operations that the DM can do on the dungeon.
One of the first operations you will probably need to do is manage some of the art assets. Specifically you will probably want to have access to a picture that will be used for this level of the dungeon
Above you can see that there are already some assets available and they belong in three categories.
All items in this list belong specifically to the Dungeon you are currently editing. For example level1.png is the picture for level 1. So if you need to add or subtract any artwork for your dungeon it would be in this list. dungeonData.json is a JSON formatted text file containing all the information about the dungeon. You should not need to manipulate this file manually. You can edit it using the Dungeon Editor Tab
This list contains any pictures for monsters that you wish to have available across all dungeons. pogs.json is a JSON formatted text file containing the pog information that uses the pictures. You should not need to manipulate this file manually. You can edit it using the Pog Editor Tab.
This list contains any pictures for room objects that you wish to have available across all dungeons. pogs.json is a JSON formatted text file containing the pog information that uses the pictures. You should not need to manipulate this file manually. You can edit it using the Pog Editor Tab.
Following is how you would add an art asset.
For example you may wish this to be a dungeon asset so select the Dungeon Assets area. You can select either the top level or any items in the list. This will enable the Choose File button.
Click the Choose File button then navigate to and select the picture file you wish to upload then press the open button. The filename should show up next to the Choose File button. The Upload Asset button should become enabled.
Click the Upload Asset button to actually upload it to the server. The list should update and show you the new file.
Select the item you wish to delete and the Delete Asset button will enable. Just click it to delete the asset. Some assets like dungeonData or pogs .json files cannot be deleted.
When you need to use an art asset in either the Dungeon Editor or the Pog Editor just select a picture file in one of the lists. The file path for the file you selected will show up under the buttons at the top of the panel. This path is also save internally and is available to be used by the mentioned editor. See those editors on the specifics on how to get at it.
The tab labeled Dungeon Editor allows you to edit levels of the dungeon. What information is shown here is dependent on what level is selected in the drop down list on the ribbon bar.
Following is a brief description of what the items in this view do.
This will call up the Manage Dungeons Dialog box allowing you to switch dungeons or create new ones as described in Creating a New Dungeon.
This will clear out the information for the current level and make the entries ready for a new one. You just need to fill in the appropriate information and click the save button to create the new level.
This deletes the currently selected level. There is no undo so be very careful with this.
This is where you enter or change the name of the selected level
This is where you enable or disable the grid from showing on the map. This is mainly used for sizing the grid. Most maps have the grid already drawn on them but this tool needs to match that grid in size and position so you would enable the grid to help with that
To assist in figuring out the size of a grid square you can do the following. Go to the map and zoom in until one square is pretty large. Then control click in the upper left corner of the square and draw a rectangle to the lower right corner. Then release the mouse button. The size of this box will get captured internally and get put into the Dungeon editor dialog when you press the Grid Size button. This will get the number close but might need some manual tweaking. The number can have a decimal point in it so you can fine tune it.
Once you have the grid size correct you probable will need to have the grid move up, down, left or right. You enter decimal numbers in Offset X and Y to do that.
The text area next to this button is where you enter the URL for the picture you wish to use for this level. The picture can be gotten from the internet by using its web path. It can also be gotten from one of your assets you uploaded in the asset management tab. Just go there and select the picture then come back here and press the Selected picture resource button.
If all information in the dialog area is correct then the save button will enable. Just press it to save the data. If you had pushed the New Level button then a new level will be created with the information else the existing level will be edited.
Pressing cancel will throw away changes and revert back to original information.
The tab labeled Pog Editor allows you to edit, create and delete pogs. Pogs are the little pictures you can create and drop onto the map. They represent the monsters, Player characters, and room object that might exist in the dungeon.
This will clear out the dialog information in preparation for creating a new Pog.
This delete the currently selected pog. There is no undo so be very careful with this.
This is where you give your pog a name. I suggest you use the following guideline in doing that. Make the name by using RACE CLASS SEX separated by spaces. By doing this in the future filtering can easily be added to narrow down choices to help find things.
There are three choices. MONSTER, ROOM OBJECT, and PLAYER. The type are mostly to help separate them in lists. Players are the only unique ones because there can only ever be one instance. So if you drag and drop them on a different map they disappear from the original map. Also PLAYER can only be created and used when DMing a session because they exist in the Session data and not in the dungeon data as explained later.
This is where the data for this pog resides. Following are the possibilities.
This means the data exists in the dungeonData.json file in the section for a specific level. Most of the time data is gotten here by dragging and dropping a pog from the ribbon bar onto the map. A copy of the select pogs information in placed in the Dungeon Level. This is where you would also create any dungeon specific pogs.
This means the data for the pog is stored in the sessionData.json file in a specific session. This file starts out as a copy of dungeonData.json file. This way any pogs moved around, created or deleted only affect the one session. Data is only changed here when the DM is managing a session.
This means the data for the pog is stored in the sessionData.json file in a specific session. The data for these player characters are in a special portion of the sessionData.json file. Data is only changed here when the DM is managing a session.
This means the data for the pog is stored in the pogs.json file in common resource area. This area is outside of any specific dungeon and therefore applies to all of them. This is where you would add pogs that all dungeons might shares like Kobolds.
The text area next to this button is where you enter the URL for the picture you wish to use for this pog. The picture can be gotten from the internet by using its web path. It can also be gotten from one of your assets you uploaded in the asset management tab. Just go there and select the picture then come back here and press the Selected picture resource button.
Pressing this button will bring up a dialog box the will allow you edit either the player visible notes or the DM notes that the players don't see. Any pog can have notes on them. These are useful for adding box text to the map.
Next to the notes button is a size drop down. Normal means the pog takes up one grid square. Large is 2x2. Huge is 3x3. Gargantuan is 4x4
This button will bring up a dialog to select player flags. These are flags players can adjust during a session.
This means the monster or player is dead. An indicator will be placed on the Pog to denote that.
This means the monster or player is invisible. The pog will be transparent to denote that. This transparent pog can be seen by players.
This button will bring up a dialog to select DM flags. These are flags only a DM can manipulate.
This means the monster or PC is invisible to players. The pog will be transparent to the DM denote that. This transparent pog cannot be seen by players.
If the picture for a pog has a transparent background then it is allowed to be used. Otherwise a white background is used.
Normally pogs are draw with the upper left corner matching the upper left corner of a grid square. Something you might wish it to be over a bit for example a secret door would be on the wall and not in a square. By selecting this option the pog will move to the right half a square.
Normally pogs are draw with the upper left corner matching the upper left corner of a grid square. Something you might wish it to be over a bit for example a secret door would be on the wall and not in a square. By selecting this option the pog will move up half a square.
Sometimes a light pog picture is hard to see against a light background. By making the background dark in edit mode it helps.
If all information in the dialog area is correct then the save button will enable. Just press it to save the data. If you had pushed the New Pog button then a new pog will be created with the information else the existing pog will be edited.
Pressing cancel will throw away changes and revert back to original information.
Below the editable area is where to trees of existing pogs lives. you can use this to find and select a particular pog. The information for it will show up in the edit area and its picture will show up in both the ribbon bar and at the lower part of this panel. You can drag and drop either picture to create a copy on the map.
To create a new session do the following steps.
You can do this by revisiting the Electronic Battle Mat screen and log back in.
This name must be unique from other sessions in this dungeon and it must be at least 4 characters long and can't start with "Enter "
Now the Session name should be in the Select session list. This makes it available for functions like "DM the Session" or "Delete the Session"
NOTICE: Once you create a session any edits you make on the template dungeon will not show up in the session. This is because a session is actually a copy of the dungeons database so any new changes will not show up in the copy.
Following are things you need to know and do while DMing a session.
One of the first thing you will want to do is create some Player Characters. To do this you first upload all the pictures you need to use if you don't have a URL for them. You do this in the Assets Tab under the Dungeon Assests tree. Adding Art Assets
You will then need to create a pog for each PC in the Pog Editor. Managing Pogs Make sure to select pog type "PLAYER" and pog location as "Player Location".
Clear FOG of WAR in an area on the starting map where you wish the PCs to start. Clearing FOW I usually do this by Control clicking and drawing a selection rectangle. All grid squares in the rectangle will toggle.
To place PCs on the map go to the Pog Editor tab and expand the Players tree. Then one at a time select a name in the list. This will selected the pog and the picture should show up in the ribbon bar and also at the bottom of the pog editor. You can now click on either picture and drag it onto the map. You will need to do this step every time the players go to a new level.
DMing is fairly straight forward. You just clear FOW from areas you want players to see. You can move around pogs during combat. You can change properties of a pog by right clicking on it for doing things like marking it as dead. You can add more monsters on the fly be selecting one either on the map or in the Pog editor and dragging and dropping it to the map from the ribbon bar.