PIE is a file editor which allows for multiple views of the same file. It does this by isolating parts of the file, allowing each part to be viewed differently. The purpose of this editor is to allow for a user to easily view and edit files containing multiple types of data (executables, archives, roms, etc.). The goal for this editor is to support the editing and viewing of the most common forms of data, allowing for less common types via plugins and the import and export of data. For the initial release, the program consists of a basic hex editor with some nice features and the ability to create "slices" from a file.
A slice is a contiguous, static-size, non-overlapping section of data derived from either another slice or the file itself. Each slice can be further subdivided into smaller slices. Unrelated slices—those slices that do not share data in common—are treated as separate entities and do not affect each other. When used correctly, each slice should represent a specific entity within the file, such as a code bank, text, or a table.
As an example, let's suppose we have a rom which is divided into 16 kilobyte banks, each bank containing various types of data. When loaded, each bank occupies the same address space. Slices allow us to partition the file into 16 kilobyte banks, each able to be viewed with the correct address space. Further, each slice can be subdivided to isolate the different types of data contained in the bank.
In order to save all the slice metadata associated with a project, PIE uses project files. These project files are XML documents that store the data associated with each slice as well as the entire project. Included in the project file is the directory of the file associated with the project. If the file is moved, PIE will search in the project directory for the file. But if it can't find it in either location the project will fail to open.
Before you can begin editing a file, you must create a new project. You can do this by either clicking on the "New Project" button in the toolbar or by clicking on "File" and selecting "New Project." PIE will then ask you to select the file to be associated with the project. After you have selected the file, it will open and be ready to edit.
To save a project, click on "File" and select "Save Project." If this project is newly created, PIE will ask you what you want it named and where you want to save it. After clicking save, the project file will be created.
The hex editor is the base way in which to view and edit data in PIE. While currently it is also the only way to edit data, all current and future slices can be viewed via the hex editor.
The hex editor can overwrite and insert data. You can do this by either entering hexadecimal values or ASCII characters. The hex editor also allows for standard copy/paste actions:
To find a certain string of hex or ASCII characters, click on "Find" under the "Edit" menu. PIE will open another window with the following fields:
To change the appearance of the hex editor, select "Options" from the "Tools" menu. This opens the options windows with customization for the following:
Once finished editing the options, click "OK" to apply the changes.
Slices are the main way that data is managed and viewed. All existing slices are shown in a tree structure to the right of the main program. To view a specific slice, double click on the slice or select it and either Select "Show" under "Slice" or by right-clicking.
There are multiple ways to create slices. The quick and easy way is to highlight a section of data, and either right click and select "Slice" or click the slice button. If successful, the new slice is created and displayed in the project view.
To manually create a slice, select the slice to slice and select "Slice..." by either right-clicking or by selecting the "Slice" menu. This will open another window with the following options:
NOTE: When creating a slice, make sure that it lies within the bounds of the slice you are creating it from and that it does not overlap another slice. Otherwise, you will receive an error. If you get an error, change the slice bounds until the slice you are attempting to create is valid.
When editing a slice, it's important to understand that the size of the slice can not be changed. Therefore, if you insert data, that same amount will roll off the end of the slice. Conversely, if you remove data from a slice, the end of the slice is padded with zeroes.
To save a slice, either select "Save" from the "File" menu or click the "Save" button in the toolbar. When saving, all changes are propagated up to the containing slices all the way to the file.
To reload a slice, select "Reload" from the "Slice" menu. By reloading the slice, PIE copies the data from the containing slice, including any unsaved changes. Keep this in mind if the slice doesn't reload what you think it should.
If you create a slice and would like to duplicate it, select the slice and click on "Clone..." by either right-clicking or by going into the "Slice" menu. This will open another window with the following options:
If the slice you create is the wrong size, it can be resized by selecting the slice and selecting "Resize..." from either the right-click menu or from the "Slice" menu. The options are similar for creating a slice:
After you have edited them to your satisfaction, click OK for the resize to take effect. Note that, just like when creating a slice, the bounds you specify must not overlap another slice and must be within the bounds of the parent slice.
To delete a slice, select the slice and either press the Delete key or select "Delete" from either the "Slice" menu or by right-clicking on the slice. Note that when deleting a slice, all sub-slices will be deleted along with it. Note also that none of the data contained in the slice is deleted, merely the slice structure itself.
To export the data of any slice, open the slice you want to export and select "Export..." from the file menu. Choose where you want it saved and name it. After you click save, the slice will be saved in the format of the slice.
To import a slice, click "Import..." under the "File" menu and select the file to import. Note that, just like editing data, the data will be modified to fit the slice.
HexBox is open source and used under the MIT license. It can be found here.
Slice and Program Icons taken from findicons.com


There is an efficiency advantage in using conditionals whose test part
consists only of arithmetic comparisons or type tests. Consider the
following alternative definitions of the predicate
type_of_character/2
. In the first definition, four clauses
are used to group characters on the basis of arithmetic comparisons.
type_of_character(Ch, Type) :- Ch >= "a", Ch =< "z", !, Type = lowercase. type_of_character(Ch, Type) :- Ch >= "A", Ch =< "Z", !, Type = uppercase. type_of_character(Ch, Type) :- Ch >= "0", Ch =< "9", !, Type = digit. type_of_character(_Ch, Type) :- Type = other.
In the second definition, a single clause with a conditional is used. The compiler generates equivalent, optimized code for both versions.
type_of_character(Ch, Type) :- ( Ch >= "a", Ch =< "z" -> Type = lowercase ; Ch >= "A", Ch =< "Z" -> Type = uppercase ; Ch >= "0", Ch =< "9" -> Type = digit ; otherwise -> Type = other ).
Following is a list of built-in predicates that are compiled efficiently in conditionals:
atom/1
atomic/1
callable/1
compound/1
float/1
ground/1
integer/1
nonvar/1
number/1
simple/1
var/1
</2
=</2
=:=/2
=\=/2
>=/2
>/2
@</2
@=</2
==/2
\==/2
@>=/2
@>/2
This optimization is actually somewhat more general than what is described above. A sequence of guarded clauses:
Head1 :- Guard1, !, Body1. ... Headm :- Guardm, !, Bodym. Headn :- Bodym.
is eligible for the same optimization, provided that the arguments of the clause heads are all unique variables and that the “guards” are simple tests as listed above.


{% include 'navbar.html' %} {% include 'flash.html' %}
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Score: 0
Speed: 100%
Time: 00:00:00
Tetris
Controls
Up: rotate
Right: move right
Left: move left
Down: move down
Space: drop figure
R: restart
P: pause
Esc - return/pause
- null
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Game Over!
Press [R] to
try again
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/**
* @module carddeck
* @author Ashok S. Nair
* @description The CardDeck module defines a deck of cards.
* The class implements methods to initialize, shuffle, deal and clear
* a deck of cards. The size of the deck can be queried as well.
*/
'use strict';
// Define imports.
let CardSuit = require('./cardsuit');
let CardRank = require('./cardrank');
let PlayingCard = require('./playingcard');
let randomize = require('./randomize');
/**
* @constant {number} DECKSIZE The size of the deck of cards.
*/
const DECKSIZE = 52;
/**
* @constructor
* @description It initializes an un-shuffled deck with 52 cards.
*/
function CardDeck() {
this._cardDeck = [];
for (let suit of CardSuit.keys()) {
for (let rank of CardRank.keys()) {
this._cardDeck.push(new PlayingCard(suit, rank));
}
}
}
/**
* @function shuffleDeck
* @description Shuffles the deck using the Fisher-Yates algorithm. Please see
* https://en.wikipedia.org/wiki/Fisher–Yates_shuffle#The_modern_algorithm
* for details. It does not accept parameters or return a value/ object.
*/
CardDeck.prototype.shuffleDeck = function() {
/*
* The Fisher-Yates shuffle algorithm is as follows:
* (reference: https://en.wikipedia.org/wiki/Fisher–Yates_shuffle#The_modern_algorithm)
* To shuffle an array a of n elements of indices 0 to n-1:
* for i from n−1 down to 1 do
* j ← random integer such that 0 ≤ j ≤ i
* exchange a[j] and a[i]
*/
let rIndex = 0;
let holdingRef = null;
for (let sRef = DECKSIZE - 1; sRef > 0; sRef--) {
rIndex = randomize.getRandomNumber(0, sRef);
holdingRef = this._cardDeck[sRef - 1];
this._cardDeck[sRef - 1] = this._cardDeck[rIndex];
this._cardDeck[rIndex] = holdingRef;
}
};
/**
* @function deal
* @description Deals (pops) a card from a non-empty deck; returns null otherwise.
* @returns {(PlayingCard|null)}
*/
CardDeck.prototype.deal = function() {
let retVal = null;
if (this._cardDeck.length > 0) {
retVal = this._cardDeck.pop();
}
return retVal;
};
/**
* @function clear
* @description Clears the deck and sets its size to zero.
*/
CardDeck.prototype.clear = function() {
this._cardDeck.length = 0;
};
/**
* @function size
* @description Returns the number of cards on the deck.
* @return {number}
*/
CardDeck.prototype.size = function() {
return this._cardDeck.length;
};
// Export the CardDeck class.
module.exports = {
CardDeck : CardDeck,
DECKSIZE : DECKSIZE
};


The Adventures of
Danny Boy
HTML, CSS & JS
Danny Boy is a web game using HTML, CSS & JavaScript (jQuery). Music was taken from Epidemic Sound, background images from Pexels, SFX from SoundBible, and artwork done by me in Illustrator CC.The story is about a boy who is trying to show his parents he's independent by working at the Potato Cafe. Have fun playing! :D


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I Woke: —
Night, lingering, poured upon the world
Of drowsy hill and wood and lake
Her moon-song,
And the breeze accompanied with hushed fingers
On the birches.
Gently the dawn held out to me
A golden handful of bird’s-notes.


ResTORbio stops testing lead drug for respiratory illness; shares crash
(Reuters) - Shares of drug developer ResTORbio Inc plunged more than 80% to a record low on Friday after a late-stage trial failure prompted the company to abandon testing its lead drug as a treatment for respiratory illness.
The company, however, said it continues to test the drug, RTB101, in other conditions, including Parkinson’s disease.
On a conference call with analysts, ResTORbio executives expressed confidence in the ongoing Parkinson’s trial, data from which is expected in mid-2020, saying it was unlikely to face a similar fate.
“In the Parkinson’s disease trial, the mechanism of action will be completely different than what we were aiming for (in the respiratory illness trial),” Chief Medical Officer Joan Mannick said, adding the doses tested in the two trials were very different.
Shares of ResTORbio were trading down 82.3% at $1.40 and have fallen nearly 8% this year.
“Beyond today’s trading, investors are unlikely to give the company much credit for its further development efforts in this or any other indication,” SVB Leerink analyst Geoffrey Porges said.
“The timelines for value realization in such indications are likely to be long and the cash requirements substantial.”
The respiratory tract illness trial tested RTB101 in people over 65 during the winter flu season and sought to prevent the illness from developing.
The trial failure dampens the company’s hopes of tapping a large potential commercial opportunity in the prevention of respiratory tract infections in seniors.
When asked about the company’s restructuring plans after the failure of the trial, ResTORbio’s Chief Executive Officer Chen Schor said the company was “well funded” for its upcoming trials, but added it may need to make “some minor modifications”.
As of Sept. 30, the company had $117.3 million in cash, cash equivalents and marketable securities.
(The story corrects advisory to remove extraneous words)


(A) As used in this section:
(1) "Agency of a political subdivision" means each organized body, office, or agency established by a political subdivision for the exercise of any function of the political subdivision, except that "agency of a political subdivision" does not include an agency that is a covered entity as defined in 45 C.F.R. 160.103, as amended.
(a) "Breach of the security of the system" means unauthorized access to and acquisition of computerized data that compromises the security or confidentiality of personal information owned or licensed by a state agency or an agency of a political subdivision and that causes, reasonably is believed to have caused, or reasonably is believed will cause a material risk of identity theft or other fraud to the person or property of a resident of this state.
(b) For purposes of division (A)(2)(a) of this section:
(i) Good faith acquisition of personal information by an employee or agent of the state agency or agency of the political subdivision for the purposes of the agency is not a breach of the security of the system, provided that the personal information is not used for an unlawful purpose or subject to further unauthorized disclosure.
(ii) Acquisition of personal information pursuant to a search warrant, subpoena, or other court order, or pursuant to a subpoena, order, or duty of a regulatory state agency, is not a breach of the security of the system.
(3) "Consumer reporting agency that compiles and maintains files on consumers on a nationwide basis" means a consumer reporting agency that regularly engages in the practice of assembling or evaluating, and maintaining, for the purpose of furnishing consumer reports to third parties bearing on a consumer's creditworthiness, credit standing, or credit capacity, each of the following regarding consumers residing nationwide:
(a) Public record information;
(b) Credit account information from persons who furnish that information regularly and in the ordinary course of business.
(4) "Encryption" means the use of an algorithmic process to transform data into a form in which there is a low probability of assigning meaning without use of a confidential process or key.
(5) "Individual" means a natural person.
(a) "Personal information" means, notwithstanding section 1347.01 of the Revised Code, an individual's name, consisting of the individual's first name or first initial and last name, in combination with and linked to any one or more of the following data elements, when the data elements are not encrypted, redacted, or altered by any method or technology in such a manner that the data elements are unreadable:
(i) Social security number;
(ii) Driver's license number or state identification card number;
(iii) Account number or credit or debit card number, in combination with and linked to any required security code, access code, or password that would permit access to an individual's financial account.
(b) "Personal information" does not include publicly available information that is lawfully made available to the general public from federal, state, or local government records or any of the following media that are widely distributed:
(i) Any news, editorial, or advertising statement published in any bona fide newspaper, journal, or magazine, or broadcast over radio or television;
(ii) Any gathering or furnishing of information or news by any bona fide reporter, correspondent, or news bureau to news media described in division (A)(6)(b)(i) of this section;
(iii) Any publication designed for and distributed to members of any bona fide association or charitable or fraternal nonprofit corporation;
(iv) Any type of media similar in nature to any item, entity, or activity identified in division (A)(6)(b)(i), (ii), or (iii) of this section.
(7) "Political subdivision" has the same meaning as in section 2744.01 of the Revised Code.
(8) "Record" means any information that is stored in an electronic medium and is retrievable in perceivable form. "Record" does not include any publicly available directory containing information an individual voluntarily has consented to have publicly disseminated or listed, such as name, address, or telephone number.
(9) "Redacted" means altered or truncated so that no more than the last four digits of a social security number, driver's license number, state identification card number, account number, or credit or debit card number is accessible as part of the data.
(10) "State agency" has the same meaning as in section 1.60 of the Revised Code, except that "state agency" does not include an agency that is a covered entity as defined in 45 C.F.R. 160.103, as amended.
(11) "System" means, notwithstanding section 1347.01 of the Revised Code, any collection or group of related records that are kept in an organized manner, that are maintained by a state agency or an agency of a political subdivision, and from which personal information is retrieved by the name of the individual or by some identifying number, symbol, or other identifier assigned to the individual. "System" does not include any collected archival records in the custody of or administered under the authority of the Ohio history connection , any published directory, any reference material or newsletter, or any routine information that is maintained for the purpose of internal office administration of the agency, if the use of the directory, material, newsletter, or information would not adversely affect an individual and if there has been no unauthorized external breach of the directory, material, newsletter, or information.
(1) Any state agency or agency of a political subdivision that owns or licenses computerized data that includes personal information shall disclose any breach of the security of the system, following its discovery or notification of the breach of the security of the system, to any resident of this state whose personal information was, or reasonably is believed to have been, accessed and acquired by an unauthorized person if the access and acquisition by the unauthorized person causes or reasonably is believed will cause a material risk of identity theft or other fraud to the resident. The disclosure described in this division may be made pursuant to any provision of a contract entered into by the state agency or agency of a political subdivision with any person or another state agency or agency of a political subdivision prior to the date the breach of the security of the system occurred if that contract does not conflict with any provision of this section. For purposes of this section, a resident of this state is an individual whose principal mailing address as reflected in the records of the state agency or agency of a political subdivision is in this state.
(2) The state agency or agency of a political subdivision shall make the disclosure described in division (B)(1) of this section in the most expedient time possible but not later than forty-five days following its discovery or notification of the breach in the security of the system, subject to the legitimate needs of law enforcement activities described in division (D) of this section and consistent with any measures necessary to determine the scope of the breach, including which residents' personal information was accessed and acquired, and to restore the reasonable integrity of the data system.
(C) Any state agency or agency of a political subdivision that, on behalf of or at the direction of another state agency or agency of a political subdivision, is the custodian of or stores computerized data that includes personal information shall notify that other state agency or agency of a political subdivision of any breach of the security of the system in an expeditious manner, if the personal information was, or reasonably is believed to have been, accessed and acquired by an unauthorized person and if the access and acquisition by the unauthorized person causes or reasonably is believed will cause a material risk of identity theft or other fraud to a resident of this state.
(D) The state agency or agency of a political subdivision may delay the disclosure or notification required by division (B), (C), or (F) of this section if a law enforcement agency determines that the disclosure or notification will impede a criminal investigation or jeopardize homeland or national security, in which case, the state agency or agency of a political subdivision shall make the disclosure or notification after the law enforcement agency determines that disclosure or notification will not compromise the investigation or jeopardize homeland or national security.
(E) For purposes of this section, a state agency or agency of a political subdivision may disclose or make a notification by any of the following methods:
(1) Written notice;
(2) Electronic notice, if the state agency's or agency of a political subdivision's primary method of communication with the resident to whom the disclosure must be made is by electronic means;
(3) Telephone notice;
(4) Substitute notice in accordance with this division, if the state agency or agency of a political subdivision required to disclose demonstrates that the agency does not have sufficient contact information to provide notice in a manner described in division (E)(1), (2), or (3) of this section, or that the cost of providing disclosure or notice to residents to whom disclosure or notification is required would exceed two hundred fifty thousand dollars, or that the affected class of subject residents to whom disclosure or notification is required exceeds five hundred thousand persons. Substitute notice under this division shall consist of all of the following:
(a) Electronic mail notice if the state agency or agency of a political subdivision has an electronic mail address for the resident to whom the disclosure must be made;
(b) Conspicuous posting of the disclosure or notice on the state agency's or agency of a political subdivision's web site, if the agency maintains one;
(c) Notification to major media outlets, to the extent that the cumulative total of the readership, viewing audience, or listening audience of all of the outlets so notified equals or exceeds seventy-five per cent of the population of this state.
(5) Substitute notice in accordance with this division, if the state agency or agency of a political subdivision required to disclose demonstrates that the agency has ten employees or fewer and that the cost of providing the disclosures or notices to residents to whom disclosure or notification is required will exceed ten thousand dollars. Substitute notice under this division shall consist of all of the following:
(a) Notification by a paid advertisement in a local newspaper that is distributed in the geographic area in which the state agency or agency of a political subdivision is located, which advertisement shall be of sufficient size that it covers at least one-quarter of a page in the newspaper and shall be published in the newspaper at least once a week for three consecutive weeks;
(b) Conspicuous posting of the disclosure or notice on the state agency's or agency of a political subdivision's web site, if the agency maintains one;
(c) Notification to major media outlets in the geographic area in which the state agency or agency of a political subdivision is located.
(F) If a state agency or agency of a political subdivision discovers circumstances that require disclosure under this section to more than one thousand residents of this state involved in a single occurrence of a breach of the security of the system, the state agency or agency of a political subdivision shall notify, without unreasonable delay, all consumer reporting agencies that compile and maintain files on consumers on a nationwide basis of the timing, distribution, and content of the disclosure given by the state agency or agency of a political subdivision to the residents of this state. In no case shall a state agency or agency of a political subdivision that is required to make a notification required by this division delay any disclosure or notification required by division (B) or (C) of this section in order to make the notification required by this division.
(G) The attorney general, pursuant to sections 1349.191 and 1349.192 of the Revised Code, may conduct an investigation and bring a civil action upon an alleged failure by a state agency or agency of a political subdivision to comply with the requirements of this section.
Amended by 131st General Assembly File No. TBD, HB 141, §1, eff. 9/29/2015.
Effective date: 02-17-2006; 03-30-200.


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Booking Policy
Cancellations: Reservations cancelled 30 days prior to arrival will be charged 100% of the full stay.
Bookings: A deposit of total room stay is due at a time of booking.
Please specify that you have read and understood the rate policies for your itinerary. *


A Statistical Model to predict the optimal Airbnb Listing price in NYC given listing information (e.g. bedrooms, type of bed, location, ratings) and taking into account seasonality data. Deployed at https://airbnb-pricing-prediction.herokuapp.com.
Samuel Lam | https://www.samuelklam.com/.


You are the Blue Block!
Avoid the Flashing, Moving Barriers!
Evade the Green Specter Blocks!
Get as many Gold Coins as you can before Time runs out!
The Game is Lost by hitting the Barriers or getting caught by a Specter!


The way of generating the adaptation profiles is very similar to the generation of assessment profiles. Adaptation profiles are used to adapt (modifiy) the game based on a set of "external properties" regarding the users coming from the server (e.g. state of the LMS, Learning Managemente System).
When these properties are fulfilled at the beginning of the chapter a set of flags and initial variables are activated or deactivated automatically and the first scene changes (if desired). So, it is possible to change the number of available objects, adapt the difficulty according to the student profile, etc.
Also these external properties can be empty in order to make changes in game automatically at startup. This way it is possible to provide differents version of the game simply by changing the adaptation profile associated with it.
In the same way that in assessment profiles, the type of adapatation profile must be set as SCORM 2004 or SCORM v1.2 if we are going to make an exportation of these types. If you select any type, a list will appear in the attribute "Id" of the state of the unit of learning with the parts of the SCORM data model which are available.
For further details of the configuration of Adapatation Profiles:


Save Op Mode
Download Java
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Download Image of Blocks
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mike neuder
^ this is milady 1696. an NFT i own and use as my online avi. i just like the art :-)
hi. thanks for visiting my bare-bones personal website! i am based in brooklyn and recently left big tech to work in crypto full-time
as a researcher with the ethereum foundation! this page
is a personal experiment in presenting more of my work and interests
online.
in its current form it provides
(1) links to my various pieces of the internet (below) links
(2) a list of things i've written writing
(3) a list of books i've read recently books
links
public eth address
raw address: 0x3d84a438Af72F6396785EEa97B32F903520e36C8
ens aliases: mikeneuder.eth & constexpr.eth
aztec address
raw address: aztec:0x0ce9c77f4110aadc90315abd41e75c47a8ee65ca25d838d0c233dfd05efee42927d35194f25b5545f28feac8fe2da75a757fbab47b232bb4e9be8c938cdc6f64
alias: @mikeneuder
linkedin do people use this?!


|
Drake
|
A wrapper class around KinematicsCache and several useful matrices such as the inertia matrix, etc. More...
#include <drake/attic/systems/controllers/qp_inverse_dynamics/robot_kinematic_state.h>
Public Member Functions | |
| RobotKinematicState (const RigidBodyTree< T > *robot) | |
| Constructor. More... | |
| virtual | ~RobotKinematicState () |
| std::unique_ptr< RobotKinematicState< T > > | Clone () const |
| void | UpdateKinematics (T t, const Eigen::Ref< const VectorX< T >> &q, const Eigen::Ref< const VectorX< T >> &v) |
The generalized position q and velocity v are used to update the internal KinematicsCache. More... | |
| void | UpdateKinematics (const Eigen::Ref< const VectorX< T >> &q, const Eigen::Ref< const VectorX< T >> &v) |
The generalized position q and velocity v are used to update the internal KinematicsCache. More... | |
| T | get_time () const |
| const RigidBodyTree< T > & | get_robot () const |
| const KinematicsCache< T > & | get_cache () const |
| const Vector3< T > & | get_com () const |
| Vector3< T > | get_com_velocity () const |
| const Vector6< T > & | get_centroidal_momentum () const |
| const MatrixX< T > & | get_M () const |
| const VectorX< T > & | get_bias_term () const |
| const Matrix6X< T > & | get_centroidal_momentum_matrix () const |
| const Vector6< T > & | get_centroidal_momentum_matrix_dot_times_v () const |
Protected Member Functions | |
| virtual RobotKinematicState< T > * | DoClone () const |
Implements CopyConstructible, CopyAssignable, MoveConstructible, MoveAssignable | |
| RobotKinematicState (const RobotKinematicState &)=default | |
| RobotKinematicState & | operator= (const RobotKinematicState &)=default |
| RobotKinematicState (RobotKinematicState &&)=default | |
| RobotKinematicState & | operator= (RobotKinematicState &&)=default |
A wrapper class around KinematicsCache and several useful matrices such as the inertia matrix, etc.
This class serves mainly as a explicit cache to avoid repeated computation. This class can be replaced when System's cache is ready.
|
protecteddefault |
|
protecteddefault |
|
inlineexplicit |
Constructor.
The internal KinematicsCache is initialized to robot's
zero configuration and zero velocity. Internal time is set to zero.
| robot | Is aliased internally. robot's life span must be longer than this. |
|
inlinevirtual |
|
inline |
|
inlineprotectedvirtual |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
|
protecteddefault |
|
protecteddefault |
|
inline |
The generalized position q
and velocity v
are used to update the internal KinematicsCache.
time
is used to update the internal time.
| time | In seconds |
| q | Generalized positions. |
| v | Generalized velocities. |
|
inline |
The generalized position q
and velocity v
are used to update the internal KinematicsCache.
| q | Generalized positions. |
| v | Generalized velocities. |


gsw_CT_second_derivatives
second derivatives of Conservative Temperature
Contents
USAGE:
[CT_SA_SA, CT_SA_pt, CT_pt_pt] = gsw_CT_second_derivatives(SA,pt)
DESCRIPTION:
Calculates the following three, second-order derivatives of Conservative
Temperature
(1) CT_SA_SA, the second derivative with respect to Absolute Salinity
at constant potential temperature (with pr = 0 dbar),
(2) CT_SA_pt, the derivative with respect to potential temperature
(the regular potential temperature which is referenced to 0 dbar)
and Absolute Salinity, and
(3) CT_pt_pt, the second derivative with respect to potential
temperature (the regular potential temperature which is referenced
to 0 dbar) at constant Absolute Salinity.
INPUT:
SA = Absolute Salinity [ g/kg ]
pt = potential temperature (ITS-90) [ deg C ]
SA & pt need to have the same dimensions.
OUTPUT:
CT_SA_SA = The second derivative of Conservative Temperature with
respect to Absolute Salinity at constant potential
temperature (the regular potential temperature which
has reference sea pressure of 0 dbar).
The CT_SA_SA output has units of: [ K/((g/kg)^2) ]
CT_SA_pt = The derivative of Conservative Temperature with
respect to potential temperature (the regular one with
pr = 0 dbar) and Absolute Salinity.
CT_SA_pt has units of: [ 1/(g/kg) ]
CT_pt_pt = The second derivative of Conservative Temperature with
respect to potential temperature (the regular one with
pr = 0 dbar) at constant SA.
CT_pt_pt has units of: [ 1/K ]
EXAMPLE:
SA = [34.7118; 34.8915; 35.0256; 34.8472; 34.7366; 34.7324;]
pt = [28.7832; 28.4209; 22.7850; 10.2305; 6.8292; 4.3245;]
[CT_SA_SA, CT_SA_pt, CT_pt_pt] = gsw_CT_second_derivatives(SA,pt)
CT_SA_SA =
1.0e-003 *
-0.060718502077064
-0.062065324400873
-0.084017055354742
-0.148436050120131
-0.171270386500246
-0.189920754900116
CT_SA_pt =
-0.001197415000869
-0.001198309530139
-0.001226523296082
-0.001335896286481
-0.001380492698572
-0.001417751669135
CT_pt_pt =
1.0e-003 *
0.123012754427146
0.124662008871271
0.140829458783443
0.140646803448166
0.113684095615077
0.082286843477998
AUTHOR:
Trevor McDougall and Paul Barker [<EMAIL_ADDRESS>]
VERSION NUMBER:
3.05 (16th February, 2015)
REFERENCES:
IOC, SCOR and IAPSO, 2010: The international thermodynamic equation of
seawater - 2010: Calculation and use of thermodynamic properties.
Intergovernmental Oceanographic Commission, Manuals and Guides No. 56,
UNESCO (English), 196 pp. Available from the TEOS-10 web site.
See appendix A.12 of this TEOS-10 Manual.
This software is available from http://www.TEOS-10.org


esutils (esutils) is utility box for ECMAScript language tools.
Returns true if node
is an Expression as defined in ECMA262 edition 5.1 section
11.
Returns true if node
is a Statement as defined in ECMA262 edition 5.1 section
12.
Returns true if node
is an IterationStatement as defined in ECMA262 edition
5.1 section 12.6.
Returns true if node
is a SourceElement as defined in ECMA262 edition 5.1
section 14.
Returns Statement?
if node
has trailing Statement
.
if (cond)
consequent;
When taking this IfStatement
, returns consequent;
statement.
Returns true if node
is a problematic IfStatement. If node
is a problematic IfStatement
, node
cannot be represented as an one on one JavaScript code.
{
type: 'IfStatement',
consequent: {
type: 'WithStatement',
body: {
type: 'IfStatement',
consequent: {type: 'EmptyStatement'}
}
},
alternate: {type: 'EmptyStatement'}
}
The above node cannot be represented as a JavaScript code, since the top level else
alternate belongs to an inner IfStatement
.
Return true if provided code is decimal digit.
Return true if provided code is hexadecimal digit.
Return true if provided code is octal digit.
Return true if provided code is white space. White space characters are formally defined in ECMA262.
Return true if provided code is line terminator. Line terminator characters are formally defined in ECMA262.
Return true if provided code can be the first character of ECMA262 Identifier. They are formally defined in ECMA262.
Return true if provided code can be the trailing character of ECMA262 Identifier. They are formally defined in ECMA262.
Returns true
if provided identifier string is a Keyword or Future Reserved Word
in ECMA262 edition 5.1. They are formally defined in ECMA262 sections
<IP_ADDRESS> and <IP_ADDRESS>,
respectively. If the strict
flag is truthy, this function additionally checks whether
id
is a Keyword or Future Reserved Word under strict mode.
Returns true
if provided identifier string is a Keyword or Future Reserved Word
in ECMA262 edition 6. They are formally defined in ECMA262 sections
<IP_ADDRESS> and
<IP_ADDRESS>,
respectively. If the strict
flag is truthy, this function additionally checks whether
id
is a Keyword or Future Reserved Word under strict mode.
Returns true
if provided identifier string is a Reserved Word in ECMA262 edition 5.1.
They are formally defined in ECMA262 section 7.6.1.
If the strict
flag is truthy, this function additionally checks whether id
is a Reserved Word under strict mode.
Returns true
if provided identifier string is a Reserved Word in ECMA262 edition 6.
They are formally defined in ECMA262 section 11.6.2.
If the strict
flag is truthy, this function additionally checks whether id
is a Reserved Word under strict mode.
Returns true
if provided identifier string is one of eval
or arguments
.
They are restricted in strict mode code throughout ECMA262 edition 5.1 and
in ECMA262 edition 6 section 12.1.1.
Return true if provided identifier string is an IdentifierName as specified in ECMA262 edition 5.1 section 7.6.
Return true if provided identifier string is an Identifier as specified in
ECMA262 edition 5.1 section 7.6. If the strict
flag is truthy, this function additionally checks whether id
is an Identifier
under strict mode.
Return true if provided identifier string is an Identifier as specified in
ECMA262 edition 6 section 12.1.
If the strict
flag is truthy, this function additionally checks whether id
is an Identifier under strict mode.
Copyright (C) 2013 Yusuke Suzuki (twitter: @Constellation) and other contributors.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
 IN NO EVENT SHALL


{% load static %}
To keep connected with us please login with your personal info
Enter your personal details and start journey with us


|
OPAE C API
-..
FPGA API
|
FPGA UMsg API. More...
#include <opae/types.h>
Go to the source code of this file.
Functions | |
| fpga_result | fpgaGetNumUmsg (fpga_handle handle, uint64_t *value) |
| fpga_result | fpgaSetUmsgAttributes (fpga_handle handle, uint64_t value) |
| fpga_result | fpgaTriggerUmsg (fpga_handle handle, uint64_t value) |
| fpga_result | fpgaGetUmsgPtr (fpga_handle handle, uint64_t **umsg_ptr) |
FPGA UMsg API.
Definition in file umsg.h.
| fpga_result fpgaGetNumUmsg | ( | fpga_handle | handle, |
| uint64_t * | value | ||
| ) |
Get number of Umsgs
Retuns number of umsg supported by AFU.
| [in] | handle | Handle to previously opened accelerator resource |
| [out] | value | Returns number of UMsgs |
| fpga_result fpgaSetUmsgAttributes | ( | fpga_handle | handle, |
| uint64_t | value | ||
| ) |
Sets Umsg hint
Writes usmg hint bit.
| [in] | handle | Handle to previously opened accelerator resource |
| [in] | value | Value to use for UMsg hint, Umsg hit is N wide bitvector where N = number of Umsgs. |
| fpga_result fpgaTriggerUmsg | ( | fpga_handle | handle, |
| uint64_t | value | ||
| ) |
Trigger Umsg
Writes a 64-bit value to trigger low-latency accelerator notification mechanism (UMsgs).
| [in] | handle | Handle to previously opened accelerator resource |
| [in] | value | Value to use for UMsg |
| fpga_result fpgaGetUmsgPtr | ( | fpga_handle | handle, |
| uint64_t ** | umsg_ptr | ||
| ) |
Access UMsg memory directly
This function will return a pointer to the memory allocated for low latency accelerator notifications (UMsgs).
| [in] | handle | Handle to previously opened accelerator resource |
| [out] | umsg_ptr | Pointer to memory where a pointer to the virtual address space will be returned |


Houston! We have a problem...
Dude, if you're here you're lost. The page that you're looking for doesn't exist!


Breaking_Bad
Breaking Bad follows protagonist Walter White, a chemistry teacher who lives in New Mexico with his wife and teenage son who has cerebral palsy. White is diagnosed with Stage III cancer and given a prognosis of two years left to live. With a new sense of fearlessness based on his medical prognosis, and a desire to secure his family's financial security, White chooses to enter a dangerous world of drugs and crime and ascends to power in this world. The series explores how a fatal diagnosis such as White's releases a typical man from the daily concerns and constraints of normal society and follows his transformation from mild family man to a kingpin of the drug trade.


Unity component to implement eating behaviours.
Quick start steps let you make a working game objects with Eater components with a minimum functionality.
This is a 2D demo to show how Eater components works in 3D scene.
This is a 2D demo to show how Eater components works in 2D scene.


Get rid of random bookmarks and tabs, and organize your favorite articles for any occasion in one spot.
Organize in 1, 2, 3
Create a board in just a few clicks. Create a board name, choose between a private board and a public board to share with friends, and add the link to your desired article. Boom, you're done!
Be Inspired.
Inspire Others.
Finding inspiration for research has never been easier. Stay update on your friends' favorite boards or share your favorite boards with an intuitive, minimal feed.


:irritate vex
:to warm by rubbing especially with the hands
:to rub so as to wear away :abrade the strap chafed his skin
:to make sore by or as if by rubbing
:to feel irritation, discontent, or impatience :fret chafes at the rules
:to rub and thereby cause wear or irritation


In the year 2000, our Founder Chairman Late Sri Raj Kumar Goelji laid the foundation of this premier institute with a vision to provide value based technical education and to create a congenial environment for the overall development of the students, faculty and staff members.
Prof(Dr.) B.K. Gupta
Dr. B.K. Gupta, is B.E. (Mech.) Roorkee, First Rank Holder 1955; M.S.(1960) and Ph.D. (1962) from Cornell University, USA. Formerly he was Principal, Moti Lal Nehru Regional Engineering College, Allahabad, Founder Principal, Ajay Kumar Garg Engineering College, Ghaziabad, Raj Kumar Goel Institute of Technology Ghaziabad, and Founder Vice President of J.K. Technosoft Ltd.
Dr. Laxman Prasad
Dr. Laxman Prasad, Director, R & D and Professor, an eminent Scientist obtained his B.Sc. in Electrical Engineering in 1970 and Ph.D. in Electronics Engineering in 1976 from Institute of Technology, Banaras Hindu University, Varanasi. He also obtained LL.B. with specialization in International Law and Intellectual Property Rights from Delhi University in 1988.
Dr.R.P. Maheswari
Prof. R.P.Maheswari obtained his B.E.(Elect.)1982 and MSc(Engg) instrumentation and control 1985 from AMU;PhD(Roorkee)1996.He is having thirty years of acadmic and administrative experience thirteen years at AMU,Aligarh and seventeen years at Roorkee University/IIt Roorkee.


--- layout: post title: Fresh paint date: '2016-10-24T02:56:35+02:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/152240456201 ---


It sounds like the beginning of a terrible joke, but it's actually my resume.
A creative at heart with a passion for design and technology, I'm drawn towards projects that use bold, beautiful design choices to improve human/computer interaction.
I'm a recent graduate of Dev Bootcamp, the original intensive web development bootcamp, and am doggedly pursuing opportunities that favor non-linear solutions to complex problems, visual feasts in design and execution, and creative ways to eschew convention.
JavaScript, jQuery, React, Ajax, Handlebars, HTML5, CSS3
Ruby, Rails, Sinatra, ActiveRecord, PostgreSQL, SQLite3
Web, mobile, graphic, illustration, iconography
Marketing, advertising, account management, copywriting, sales


|
ObjectAL
2.1
ObjectAL API Reference (iOS) for www.kobold2d.com developers
|
Calls a selector on a target. More...
#import <OALUtilityActions.h>
Public Member Functions | |
| (id) | - initWithCallTarget:selector: |
| Initialize an action. | |
| (id) | - initWithCallTarget:selector:withObject: |
| Initialize an action. | |
| (id) | - initWithCallTarget:selector:withObject:withObject: |
| Initialize an action. | |
Static Public Member Functions | |
| (id) | + actionWithCallTarget:selector: |
| Create an action. | |
| (id) | + actionWithCallTarget:selector:withObject: |
| Create an action. | |
| (id) | + actionWithCallTarget:selector:withObject:withObject: |
| Create an action. | |
Protected Attributes | |
| id | callTarget |
| The target to call the selector on. | |
| SEL | selector |
| The selector to invoke. | |
| int | numObjects |
| The number of parameters which will be passed to the selector. | |
| id | object1 |
| The first object to pass to the selector, if any. | |
| id | object2 |
| The second object to pass to the selector, if any. |
Calls a selector on a target.
This action will ignore whatever target it is run against, and will invoke the selector on the target specified at creation time.
| + (id) actionWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
Create an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| + (id) actionWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
| withObject: | (id) | object | |
Create an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| object | The object to pass to the selector. |
| + (id) actionWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
| withObject: | (id) | firstObject | |
| withObject: | (id) | secondObject | |
Create an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| firstObject | The first object to pass to the selector. |
| secondObject | The second object to pass to the selector. |
| - (id) initWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
Initialize an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| - (id) initWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
| withObject: | (id) | object | |
Initialize an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| object | The object to pass to the selector. |
| - (id) initWithCallTarget: | (id) | callTarget | |
| selector: | (SEL) | selector | |
| withObject: | (id) | firstObject | |
| withObject: | (id) | secondObject | |
Initialize an action.
| callTarget | The target to call. |
| selector | The selector to invoke. |
| firstObject | The first object to pass to the selector. |
| secondObject | The second object to pass to the selector. |
- (id) callTarget [protected] |
The target to call the selector on.
- (int) numObjects [protected] |
The number of parameters which will be passed to the selector.
- (id) object1 [protected] |
The first object to pass to the selector, if any.
- (id) object2 [protected] |
The second object to pass to the selector, if any.
- (SEL) selector [protected] |
The selector to invoke.


BLOCK VS. INLINE ELEMENTS
There are essentially two kinds of HTML elements: Block elements and inline elements. The best way to think about them is by how much page width they take up.
Block elements, e.g.
,
,
,

- Take up the full available width, thus creating their own block on a page.
- Each element fits into the box model and has its own spacing.
- Have a line-break before and after.
HTML Code:
Result:
Inline elements, e.g. , , ,
HTML Code
Result:
Changing element levels
You can change the visual presentation of an element using the CSS display property. For example, by changing the value of display from "inline" to "block", you can tell the browser to render the inline element in a block box rather than an inline box, and vice versa. Let's see a example In the figure below.
HTML Code
Result:
On the figure example we have an outer div, called div1, which contains some divs, and then have another outer div, called "inline", which also has some divs. Now, in div1, notice there's no CSS styling up here for div1. So these divs display as they would by default. And by default, divs display on their inline.
We have a separate line for each of these divs being displayed. Okay, so those are the first three divs, they're displayed as block elements by default.
Okay, now let's have a look at the second div. For this div, I've also got nested divs. For "inline div", I've said this publicly, this rule here, it says, In "inline div", any divs inside it will be inline as opposed to block. So when we have "inline div", any divs inside it will be displayed as inline elements. So that's the effect that you see.
Moving on, I have a navbar which contains some hyperlinks, and I have another navbar which also displays hyperlinks, but let's see how styling kicks in. So, this first navbar "nav1", I haven't defined any CSS for nav1. So these hyperlinks get displayed in their normal format, which is as an inline element.
The second "block navbar", the same hyperlinks, but this time, I've said for "block navbar", for that section, any anchors, any hyperlinks inside "block navbar" will be displayed as block. So when you set displayed block, then each hyperlink goes on its own line like so.


{% extends "index.html" %} {% block head %}
The message {{query}} has been classified. The categories that the message has been classified into have been highlighted below.


{% extends "member/base.html" %} {% block sub-title %}Contact Request | {% endblock %} {% block style %} {% endblock %} {% block content %}
There are two ways you can use Nadine to connect with other members. We don't give out any personal information so we can't just give you their email address, but we can alert you when you are both in the space at the same time, or we can send an email to them with your information ({{user.email}}) and they can contact you.


Topcount data can be uploaded in two forms:
- Individual file which contains concatenated topcount data blocks in one file
- Zip file which contains packed files from individual topcount reads
Check the Dosc section for details about suported data layouts inside tomcats file, we can currently read
ASCI content with "plate map" or "column" layouts.


Blog
This is where jot down notes on things like javascript, various tools, and occasionally my personal life. You can also check out my projects and read more about me.
{% extends 'main.html' %} {% block page_title %}Steven Schobert - Web Developer & Designer{% endblock %} {% block page_content %}
This is where jot down notes on things like javascript, various tools, and occasionally my personal life. You can also check out my projects and read more about me.
{{ post.desc|raw }}


bokeh.charts
Interface¶Bar
(data, label=None, values=None, color=None, stack=None, group=None, agg='sum', xscale='categorical', yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)¶Create a Bar chart using BarBuilder
render the geometry from values, cat and stacked.
| Parameters: |
|
|---|
In addition the the parameters specific to this chart, userguide_charts_generic_arguments are also accepted as keyword parameters.
| Returns: | class:Chart <bokeh.charts.Chart> |
|---|---|
| Return type: | a new |
Examples
from collections import OrderedDict
from bokeh.charts import Bar, output_file, show
# (dict, OrderedDict, lists, arrays and DataFrames are valid inputs)
xyvalues = OrderedDict()
xyvalues['python']=[-2, 5]
xyvalues['pypy']=[12, 40]
xyvalues['jython']=[22, 30]
cat = ['1st', '2nd']
bar = Bar(xyvalues, cat, title="Stacked bars",
xlabel="category", ylabel="language")
output_file("stacked_bar.html")
show(bar)
Error
Unable to generate Bokeh plot at /home/travis/build/bokeh/bokeh/sphinx/source/docs/reference/charts.rst:31:
BoxPlot
(data, label=None, values=None, color=None, group=None, xscale='categorical', yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)¶Generate a box plot from table-like and column-like inputs.
Histogram
(data, values=None, label=None, color=None, agg='count', bins=None, yscale='linear', xgrid=False, ygrid=True, continuous_range=None, **kw)¶Line
(data=None, x=None, y=None, **kws)¶Create a line chart using LineBuilder
to
render the geometry from values and index.
| Parameters: |
|
|---|
In addition the the parameters specific to this chart, userguide_charts_generic_arguments are also accepted as keyword parameters.
| Returns: | class:Chart <bokeh.charts.Chart> |
|---|---|
| Return type: | a new |
Examples:
import numpy as np
from bokeh.charts import Line, output_file, show
# (dict, OrderedDict, lists, arrays and DataFrames are valid inputs)
xyvalues = np.array([[2, 3, 7, 5, 26], [12, 33, 47, 15, 126], [22, 43, 10, 25, 26]])
line = Line(xyvalues, title="line", legend="top_left", ylabel='Languages')
output_file('line.html')
show(line)
Error
Unable to generate Bokeh plot at /home/travis/build/bokeh/bokeh/sphinx/source/docs/reference/charts.rst:29:
If using all scalar values, you must pass an index
Scatter
(data=None, x=None, y=None, **kws)¶Create a scatter chart using ScatterBuilder
to render the geometry from values.
| Parameters: | data (arrays or dict(array) or list(dict) or pd.DataFrame) – table-like data |
|---|
In addition the the parameters specific to this chart, userguide_charts_generic_arguments are also accepted as keyword parameters.
| Returns: | class:Chart <bokeh.charts.Chart> |
|---|---|
| Return type: | a new |
Examples:
from bokeh.sampledata.autompg import autompg as df
from bokeh.charts import Scatter, output_file, show
scatter = Scatter(df, x='mpg', y='hp', color='cyl', marker='origin',
title="mpg", xlabel="Miles Per Gallon", ylabel="Horsepower")
output_file('scatter.html')
show(scatter)
Chart
(*args, **kwargs)¶The main Chart class, the core of the Bokeh.charts
interface.
add_builder
(builder)¶add_labels
(dim, label)¶add_legend
(legends)¶Add the legend to your plot, and the plot to a new Document.
It also add the Document to a new Session in the case of server output.
| Parameters: | legends (List(Tuple(String, List(GlyphRenderer)) – A list of tuples that maps text labels to the legend to corresponding renderers that should draw sample representations for those labels. |
|---|
add_ranges
(dim, range)¶add_renderers
(builder, renderers)¶add_scales
(dim, scale)¶create_axes
()¶create_grids
(xgrid=True, ygrid=True)¶create_tools
(tools)¶Create tools if given tools=True input.
Only adds tools if given boolean and does not already have tools added to self.
filename
(value)¶Chained method for filename option.
get_class
(view_model_name)¶Given a __view_model__ name, returns the corresponding class object
height
(value)¶Chained method for height option.
id
(value)¶Chained method for id option.
legend
(value)¶Chained method for legend option.
make_axis
(dim, location, scale, label)¶Create linear, date or categorical axis depending on the location, scale and with the proper labels.
| Parameters: |
|
|---|---|
| Returns: | Axis instance |
| Return type: |
make_grid
(dimension, ticker)¶Create the grid just passing the axis and dimension.
| Parameters: |
|
|---|---|
| Returns: | Grid instance |
| Return type: |
notebook
(value)¶Chained method for notebook option.
server
(value)¶Chained method for server option.
show
()¶Main show function.
It shows the plot in file, server and notebook outputs.
start_plot
()¶Add the axis, grids and tools
width
(value)¶Chained method for width option.
xgrid
(value)¶Chained method for xgrid option.
xlabel
(value)¶Chained method for xlabel option.
xscale
(value)¶Chained method for xscale option.
ygrid
(value)¶Chained method for ygrid option.
ylabel
(value)¶Chained method for ylabel option.
yscale
(value)¶Chained method for yscale option.
BarBuilder
(*args, **kws)¶This is the Bar class and it is in charge of plotting Bar chart (grouped and stacked) in an easy and intuitive way.
Essentially, it provides a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (rects) taking the references from the source.
The x_range is categorical, and is made either from the cat argument or from the indexes of the passed values if no cat is supplied. The y_range can be supplied as the parameter continuous_range, or will be calculated as a linear range (Range1d) based on the supplied values using the following rules:
- with all positive data: start = 0, end = 1.1 * max
- with all negative data: start = 1.1 * min, end = 0
- with mixed sign data: start = 1.1 * min, end = 1.1 * max
glyph
¶alias of BarGlyph
collect_glyph_kwargs
(group)¶get_extra_args
()¶default_attributes
= {'color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1acdfdd0>, 'group': <bokeh.charts._attributes.GroupAttr object at 0x2adf1acdfe50>, 'stack': <bokeh.charts._attributes.GroupAttr object at 0x2adf1acdfe10>, 'label': <bokeh.charts._attributes.GroupAttr object at 0x2adf1acdfd90>}¶dimensions
= ['values']¶label_attributes
= ['stack', 'group']¶values
= <bokeh.charts._properties.Dimension object>¶BoxPlotBuilder
(*args, **kws)¶Produces Box Glyphs for groups of data.
Handles box plot options to produce one to many boxes, which are used to describe the distribution of a variable.
glyph
¶alias of BoxGlyph
default_attributes
= {'outlier_fill_color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1ace91d0>, 'group': <bokeh.charts._attributes.GroupAttr object at 0x2adf1ace92d0>, 'color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1ace9190>, 'label': <bokeh.charts._attributes.GroupAttr object at 0x2adf1ace9150>, 'whisker_color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1ace9250>, 'stack': <bokeh.charts._attributes.GroupAttr object at 0x2adf1ace9290>}¶HistogramBuilder
(*args, **kws)¶Generates one to many histograms with unique attributes.
The HistogramBuilder is responsible for producing a chart containing one to many histograms from table-like inputs.
glyph
¶alias of HistogramGlyph
get_extra_args
()¶bins
¶property type: Int
Number of bins to use for the histogram. (default: None (use Freedman-Diaconis rule)
density
¶property type: Bool
Whether to normalize the histogram. (default: True)
If True, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. If False, the result will contain the number of samples in each bin.
For more info check numpy.histogram
function documentation.
LineBuilder
(*args, **kws)¶This is the Line class and it is in charge of plotting Line charts in an easy and intuitive way. Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed lines taking the references from the source.
attr_measurement
(attr_name)¶Detect if the attribute has been given measurement columns.
default_attributes
= {'color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1acb7ed0>, 'dash': <bokeh.charts._attributes.DashAttr object at 0x2adf1acc1190>}¶dimensions
= ['y', 'x']¶measure_input
¶measures
¶ScatterBuilder
(*args, **kws)¶This is the Scatter class and it is in charge of plotting Scatter charts in an easy and intuitive way.
Essentially, we provide a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the ranges. And finally add the needed glyphs (markers) taking the references from the source.
default_attributes
= {'color': <bokeh.charts._attributes.ColorAttr object at 0x2adf1acad2d0>, 'marker': <bokeh.charts._attributes.MarkerAttr object at 0x2adf1ace9050>}¶


InterviewJS is an open-source tool for journalists to make stories engaging. Storytellers are taken through the process of building a chat story from creating interviewee profiles to composing interactive messaging exchanges combining text, videos, maps, audio, charts or any other type of embeddable content.
The result: mobile-friendly stories in the form of an embeddable web app that enable users to engage with interviewees seemingly directly. They can be shared on social media or embedded on your website.
This is the beta version of InterivewJS. It was funded by the Digital News Innovation Fund and developed by Al Jazeera.
Here are just some examples of what you can make with InterviewJS:
What is InterviewJS?
Who is InterviewJS for?
What do I need to get started?
Is InterviewJS free?
Who owns my story?
What types of media can I include in my interview?
Where is my data stored?
How long can my interview be?
Can I add custom styling to my interviews?
What is the maximum character length of a text bubble?
Which phones does InterviewJS work on?
Do I need to download any software?
What languages does it support?
Does InterviewJS access my Facebook messenger, Whatsapp or any other programme on my phone?
What do you do with the the information from the poll at the end?
What if I want to build a fictional story?
How long does it take to make an interview?
Who developed InterviewJS?
How many interviewees can I include?
Will my interviews be picked up by search engines?
What web browsers does InterviewJS support?
Can I embed an interview into my website?
How can I contribute to InterviewJS?


You can serve the site by running:
gerbil serve
in the root directory (where the site/ and web/ directories reside).


This repo branching model is inspired by Git Flow. Currently we have these types of branches:
- master - Latest release, stable
- develop - Main development, unstable
- release/ - Release archive
- feature/ - Specific features development
- example/ - Helpful examples
- hotfix/ - Emergency fixes for latest release
Read the Git Flow reference.
This repo uses Semantic Versioning.


There are some differences between the way that R, SAS, SPSS, and Stata represented labelled data and missing values. While SAS, SPSS, and Stata share some obvious similarities, R is little different. This vignette explores the differences, and shows you how haven bridges the gap.
Base R has one data type that effectively maintains a mapping between integers and character labels: the factor. This however, is not the primary use of factors: they are instead designed to automatically generate useful contrasts for linear models. Factors differ from the labelled values provided by the other tools in important ways:
SPSS and SAS can label numeric and character values, not just integer values.
The value do not need to be exhaustive. It is common to label the special missing values (e.g. .D
= did not respond, .N
= not applicable), while leaving other values as is.
Value labels in SAS are a little different again. In SAS, labels are just special case of general formats. Formats include currencies and dates, but user-defined just assigns labels to individual values (including special missings value). Formats have names and existing independently of the variables they are associated with. You create a named format with PROC FORMAT
and then associated with variables in a DATA
step (the names of character formats thealways start with $
).
labelled()
To allow you to import labelled vectors into R, haven provides the S3 labelled class, created with labelled()
. This class allows you to associated arbitrary labels with numeric or character vectors:
x1 <- labelled(
sample(1:5),
c(Good = 1, Bad = 5)
)
x1
#> <Labelled integer>
#> [1] 5 3 4 2 1
#>
#> Labels:
#> value label
#> 1 Good
#> 5 Bad
x2 <- labelled(
c("M", "F", "F", "F", "M"),
c(Male = "M", Female = "F")
)
x2
#> <Labelled character>
#> [1] M F F F M
#>
#> Labels:
#> value label
#> M Male
#> F Female
The goal of haven is not to provide a labelled vector that you can use everywhere in your analysis. The goal is to provide an intermediate datastructure that you can convert into a regular R data frame. You can do this by either converting to a factor or stripping the labels:
as_factor(x1)
#> [1] Bad 3 4 2 Good
#> Levels: Good 2 3 4 Bad
zap_labels(x1)
#> [1] 5 3 4 2 1
as_factor(x2)
#> [1] Male Female Female Female Male
#> Levels: Female Male
zap_labels(x2)
#> [1] "M" "F" "F" "F" "M"
See the documentation for as_factor()
for more options to control exactly what the factor uses for levels.
Both as_factor()
and zap_labels()
have data frame methods if you want to apply the same strategy to every column in a data frame:
df <- tibble::data_frame(x1, x2, z = 1:5)
df
#> # A tibble: 5 x 3
#> x1 x2 z
#> <int+lbl> <chr+lbl> <int>
#> 1 5 M 1
#> 2 3 F 2
#> 3 4 F 3
#> 4 2 F 4
#> 5 1 M 5
zap_labels(df)
#> # A tibble: 5 x 3
#> x1 x2 z
#> <int> <chr> <int>
#> 1 5 M 1
#> 2 3 F 2
#> 3 4 F 3
#> 4 2 F 4
#> 5 1 M 5
as_factor(df)
#> # A tibble: 5 x 3
#> x1 x2 z
#> <fct> <fct> <int>
#> 1 Bad Male 1
#> 2 3 Female 2
#> 3 4 Female 3
#> 4 2 Female 4
#> 5 Good Male 5
All three tools provide a global “system missing value” which is displayed as .
. This is roughly equivalent to R’s NA
, although neither Stata nor SAS propagate missingness in numeric comparisons: SAS treats the missing value as the smallest possible number (i.e. -inf
), and Stata treats it as the largest possible number (i.e. inf
).
Each tool also provides a mechanism for recording multiple types of missingness:
Stata has “extended” missing values, .A
through .Z
.
SAS has “special” missing values, .A
through .Z
plus ._
.
SPSS has per-column “user” missing values. Each column can declare up to three distinct values or a range of values (plus one distinct value) that should be treated as missing.
Stata and SAS only support tagged missing values for numeric columns. SPSS supports up to three distinct values for character columns. Generally, operations involving a user-missing type return a system missing value.
Haven models these missing values in two different ways:
For SAS and Stata, haven provides “tagged” missing values which extend R’s regular NA
to add a single character label.
For SPSS, haven provides a subclass of labelled
that also provides user defined values and ranges.
To support Stata’s extended and SAS’s special missing value, haven implements a tagged NA. It does this by taking advantage of the internal structure of a floating point NA. That allows these values to behave identical to NA in regular R operations, while still preserving the value of the tag.
The R interface for creating with tagged NAs is a little clunky because generally they’ll be created by haven for you. But you can create your own with tagged_na()
:
Note these tagged NAs behave identically to regular NAs, even when printing. To see their tags, use print_tagged_na()
:
To test if a value is a tagged NA, use is_tagged_na()
, and to extract the value of the tag, use na_tag()
:
is_tagged_na(x)
#> [1] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
is_tagged_na(x, "a")
#> [1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
na_tag(x)
#> [1] NA NA NA "a" "z" NA NA NA
My expectation is that tagged missings are most often used in conjuction with labels (described below), so labelled vectors print the tags for you, and as_factor()
knows how to relabel:
SPSS’s user-defined values work differently to SAS and Stata. Each column can have either up to three distinct values that are considered as missing, or a range. Haven provides labelled_spss()
as a subclass of labelled()
to model these additional user-defined missings.
x1 <- labelled_spss(c(1:10, 99), c(Missing = 99), na_value = 99)
x2 <- labelled_spss(c(1:10, 99), c(Missing = 99), na_range = c(90, Inf))
x1
#> <Labelled SPSS double>
#> [1] 1 2 3 4 5 6 7 8 9 10 99
#> Missing values: 99
#>
#> Labels:
#> value label
#> 99 Missing
x2
#> <Labelled SPSS double>
#> [1] 1 2 3 4 5 6 7 8 9 10 99
#> Missing range: [90, Inf]
#>
#> Labels:
#> value label
#> 99 Missing
These objects are somewhat dangerous to work with in R because most R functions don’t know those values are missing:
Because of that danger, the default behaviour of read_spss()
is to return regular labelled objects where user-defined missing values have been converted to NA
s. To get read_spss()
to return labelled_spss()
objects, you’ll need to set user_na = TRUE
.
I’ve defined an is.na()
method so you can find them yourself:
And the presence of that method does mean many functions with an na.rm
argument will work correctly:
But generally you should either convert to a factor, convert to regular missing vaues, or strip the all the labels:


Trains two recurrent neural networks based upon a story and a question.
The resulting merged vector is then queried to answer a range of bAbI tasks.
The results are comparable to those for an LSTM model provided in Weston et al.: "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks" http://arxiv.org/abs/1502.05698
| Task Number | FB LSTM Baseline | Keras QA |
|---|---|---|
| QA1 - Single Supporting Fact | 50 | 52.1 |
| QA2 - Two Supporting Facts | 20 | 37.0 |
| QA3 - Three Supporting Facts | 20 | 20.5 |
| QA4 - Two Arg. Relations | 61 | 62.9 |
| QA5 - Three Arg. Relations | 70 | 61.9 |
| QA6 - yes/No Questions | 48 | 50.7 |
| QA7 - Counting | 49 | 78.9 |
| QA8 - Lists/Sets | 45 | 77.2 |
| QA9 - Simple Negation | 64 | 64.0 |
| QA10 - Indefinite Knowledge | 44 | 47.7 |
| QA11 - Basic Coreference | 72 | 74.9 |
| QA12 - Conjunction | 74 | 76.4 |
| QA13 - Compound Coreference | 94 | 94.4 |
| QA14 - Time Reasoning | 27 | 34.8 |
| QA15 - Basic Deduction | 21 | 32.4 |
| QA16 - Basic Induction | 23 | 50.6 |
| QA17 - Positional Reasoning | 51 | 49.1 |
| QA18 - Size Reasoning | 52 | 90.8 |
| QA19 - Path Finding | 8 | 9.0 |
| QA20 - Agent's Motivations | 91 | 90.7 |
For the resources related to the bAbI project, refer to: https://research.facebook.com/researchers/1543934539189348
Notes
- With default word, sentence, and query vector sizes, the GRU model achieves:
- 52.1% test accuracy on QA1 in 20 epochs (2 seconds per epoch on CPU)
-
37.0% test accuracy on QA2 in 20 epochs (16 seconds per epoch on CPU) In comparison, the Facebook paper achieves 50% and 20% for the LSTM baseline.
-
The task does not traditionally parse the question separately. This likely improves accuracy and is a good example of merging two RNNs.
-
The word vector embeddings are not shared between the story and question RNNs.
-
See how the accuracy changes given 10,000 training samples (en-10k) instead of only 1000. 1000 was used in order to be comparable to the original paper.
-
Experiment with GRU, LSTM, and JZS1-3 as they give subtly different results.
-
The length and noise (i.e. 'useless' story components) impact the ability of LSTMs / GRUs to provide the correct answer. Given only the supporting facts, these RNNs can achieve 100% accuracy on many tasks. Memory networks and neural networks that use attentional processes can efficiently search through this noise to find the relevant statements, improving performance substantially. This becomes especially obvious on QA2 and QA3, both far longer than QA1.
from __future__ import print_function
from functools import reduce
import re
import tarfile
import numpy as np
from keras.utils.data_utils import get_file
from keras.layers.embeddings import Embedding
from keras import layers
from keras.layers import recurrent
from keras.models import Model
from keras.preprocessing.sequence import pad_sequences
def tokenize(sent):
'''Return the tokens of a sentence including punctuation.
>>> tokenize('Bob dropped the apple. Where is the apple?')
['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?']
'''
return [x.strip() for x in re.split(r'(\W+)', sent) if x.strip()]
def parse_stories(lines, only_supporting=False):
'''Parse stories provided in the bAbi tasks format
If only_supporting is true,
only the sentences that support the answer are kept.
'''
data = []
story = []
for line in lines:
line = line.decode('utf-8').strip()
nid, line = line.split(' ', 1)
nid = int(nid)
if nid == 1:
story = []
if '\t' in line:
q, a, supporting = line.split('\t')
q = tokenize(q)
if only_supporting:
# Only select the related substory
supporting = map(int, supporting.split())
substory = [story[i - 1] for i in supporting]
else:
# Provide all the substories
substory = [x for x in story if x]
data.append((substory, q, a))
story.append('')
else:
sent = tokenize(line)
story.append(sent)
return data
def get_stories(f, only_supporting=False, max_length=None):
'''Given a file name, read the file, retrieve the stories,
and then convert the sentences into a single story.
If max_length is supplied,
any stories longer than max_length tokens will be discarded.
'''
data = parse_stories(f.readlines(), only_supporting=only_supporting)
flatten = lambda data: reduce(lambda x, y: x + y, data)
data = [(flatten(story), q, answer) for story, q, answer in data
if not max_length or len(flatten(story)) < max_length]
return data
def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):
xs = []
xqs = []
ys = []
for story, query, answer in data:
x = [word_idx[w] for w in story]
xq = [word_idx[w] for w in query]
# let's not forget that index 0 is reserved
y = np.zeros(len(word_idx) + 1)
y[word_idx[answer]] = 1
xs.append(x)
xqs.append(xq)
ys.append(y)
return (pad_sequences(xs, maxlen=story_maxlen),
pad_sequences(xqs, maxlen=query_maxlen), np.array(ys))
RNN = recurrent.LSTM
EMBED_HIDDEN_SIZE = 50
SENT_HIDDEN_SIZE = 100
QUERY_HIDDEN_SIZE = 100
BATCH_SIZE = 32
EPOCHS = 20
print('RNN / Embed / Sent / Query = {}, {}, {}, {}'.format(RNN,
EMBED_HIDDEN_SIZE,
SENT_HIDDEN_SIZE,
QUERY_HIDDEN_SIZE))
try:
path = get_file('babi-tasks-v1-2.tar.gz',
origin='https://s3.amazonaws.com/text-datasets/'
'babi_tasks_1-20_v1-2.tar.gz')
except:
print('Error downloading dataset, please download it manually:\n'
'$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2'
'.tar.gz\n'
'$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz')
raise
# Default QA1 with 1000 samples
# challenge = 'tasks_1-20_v1-2/en/qa1_single-supporting-fact_{}.txt'
# QA1 with 10,000 samples
# challenge = 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt'
# QA2 with 1000 samples
challenge = 'tasks_1-20_v1-2/en/qa2_two-supporting-facts_{}.txt'
# QA2 with 10,000 samples
# challenge = 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt'
with tarfile.open(path) as tar:
train = get_stories(tar.extractfile(challenge.format('train')))
test = get_stories(tar.extractfile(challenge.format('test')))
vocab = set()
for story, q, answer in train + test:
vocab |= set(story + q + [answer])
vocab = sorted(vocab)
# Reserve 0 for masking via pad_sequences
vocab_size = len(vocab) + 1
word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
story_maxlen = max(map(len, (x for x, _, _ in train + test)))
query_maxlen = max(map(len, (x for _, x, _ in train + test)))
x, xq, y = vectorize_stories(train, word_idx, story_maxlen, query_maxlen)
tx, txq, ty = vectorize_stories(test, word_idx, story_maxlen, query_maxlen)
print('vocab = {}'.format(vocab))
print('x.shape = {}'.format(x.shape))
print('xq.shape = {}'.format(xq.shape))
print('y.shape = {}'.format(y.shape))
print('story_maxlen, query_maxlen = {}, {}'.format(story_maxlen, query_maxlen))
print('Build model...')
sentence = layers.Input(shape=(story_maxlen,), dtype='int32')
encoded_sentence = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(sentence)
encoded_sentence = RNN(SENT_HIDDEN_SIZE)(encoded_sentence)
question = layers.Input(shape=(query_maxlen,), dtype='int32')
encoded_question = layers.Embedding(vocab_size, EMBED_HIDDEN_SIZE)(question)
encoded_question = RNN(QUERY_HIDDEN_SIZE)(encoded_question)
merged = layers.concatenate([encoded_sentence, encoded_question])
preds = layers.Dense(vocab_size, activation='softmax')(merged)
model = Model([sentence, question], preds)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
print('Training')
model.fit([x, xq], y,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_split=0.05)
print('Evaluation')
loss, acc = model.evaluate([tx, txq], ty,
batch_size=BATCH_SIZE)
print('Test loss / test accuracy = {:.4f} / {:.4f}'.format(loss, acc))


Dongliang Chang1
1Beijing University of Posts and Telecommunications, CN
2University of Edinburgh, UK
3SketchX, CVSSP, University of Surrey, UK
CVPR 2023
Figure 1. How to identify the fine-grained labels of an object? Current paradigms require two stages: coarse-grained visual classification and fine-grained visual classification. This paper transforms the two stages of recognition into an erudite fine-grained visual classification model, which can directly recognise the fine-grained labels of objects across different coarse-grained label spaces..
Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of FGVC algorithms in real-life scenarios. In this paper, we propose an erudite FGVC model jointly trained by several different datasets, which can efficiently and accurately predict an object’s fine-grained label across the combined label space. We found through a pilot study that positive and negative transfers co-occur when different datasets are mixed for training, i.e., the knowledge from other datasets is not always useful. Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets. In detail, we reduce negative transfer by decoupling the deep features through many dataset-specific feature extractors. Subsequently, these are channel-wise re-fused to facilitate positive transfer. Finally, we propose a meta-learning based dataset-agnostic spatial attention layer to take full advantage of the multi-dataset training data, given that localisation is dataset-agnostic between different datasets. Experimental results across 11 different mixed-datasets built on four different FGVC datasets demonstrate the effectiveness of the proposed method. Furthermore, the proposed method can be easily combined with existing FGVC methods to obtain state-of-the-art results.
Figure 2. Performance differences (∆ (%)) between multi-dataset training vs. training each dataset alone. Subplots indicate target datasets and bars correspond to extra data used for training.
Table 1. Evaluation of the feature distribution of test samples after joint training. ♭: denotes the distribution of samples within each individual dataset, and †: represents the distribution of samples between any two datasets. Underline indicates the best results.
Figure 3. A schematic illustration of the proposed methods. The input x contains data belonging to multiple datasets. Here is a mix of 3 datasets as an example (i.e., N = 3). The dataset-specific classifiers (G1, G2, and G3) are only used in the training stage.
Table 2. Comparisons with different baselines. Underlining indicates the best results..
Figure 4. We highlight the supporting visual regions for attention layers of two compared models. The red circles denote the exclusive visual regions that Ours focus on.
@inproceedings{Chang2023Erudite,
title={An Erudite Fine-Grained Visual Classification Model},
author={Chang, Dongliang and Tong, Yujun and Du, Ruoyi and Timothy, Hospedales and Song, Yi-Zhe and Ma, Zhanyu},
booktitle={CVPR},
year={2023}
}
Proudly created by Dongliang Chang @ BUPT
2023.6


Click and drag on the canvas (on the left) to move the teapot.
This demo requires Firefox 47 or Chrome 52 (or later).
The teapot is drawn on the canvas element using WebGL. A stream is captured from the canvas element using its captureStream()
method and set as the srcObject
of the video element.
The canvas
, video
, and stream
variables are in global scope, so you can inspect them from the browser console.
For more demos and information about captureStream()
, see Media Capture from Canvas Implementation.


By definition on Wikipedia, impressionism is a 19th-century art movement characterized by relatively small, thin, yet visible brush strokes, open composition, emphasis on accurate depiction of light in its changing qualities (often accentuating the effects of the passage of time), ordinary subject matter, inclusion of movement as a crucial element of human perception and experience, and unusual visual angles.
You may not be fimiliar with the definition of impressionism but you must be pretty familiar with names of famous impressionists and their masterpieces.
Starry Night by Vincent Van Gogh
Impression, Sunrise by Claude Monet
San Giorgio Maggiore at Dusk by Claude Monet
This project is mainly inspired by the paper "Processing Images and Video for An Impressionist Effect" from Peter Litwinowicz.
One notable techique used by impressionist is using small, thin, yet visible brush strokes. So in order to achieve that effect, I tried to draw short strokes with preset lengths, widths and orientations using colors at the center of strokes.
However, there is another problem with drawing strokes. I tried to draw two triangles to form a stroke, but the poly2mask
function in Matlab is not antialiased so that it will create jagged edges at its margin.
Using Xiaolin Wu's line algorithm can significantly smooth the edge of strokes.
Here is the result:
A Photo of Altgeld Hall
Generated Image Using Fixed Strokes
Note that in this approach all stokes are generated in random order to help create a hand-touched look. However, the whole image is still way too unnatural compared to the aboving impressionist masterpieces. Impressionist artists are not robots so they probably cannot always paint stokes with the same length, width and orientation.
You will notice that stroke at the sky region in Impression, Sunrise by Monet has different colors and orientations. As I mentioned before, impressionist artists are trying to paint visible strokes. Therefore, since we are using the center pixel color at each strokes, strokes at a area with unnoticeable color difference will not be distinct enough from each other. A solution proposed by Peter Litwinowicz is to add some random perturbation to each individual stroke's parameters(i.e. length, width, orientation and color).
Here is the comparision of results:
Generated Image Using Fixed Stokes
Generated Image Using Random Perturbation
The generated image looks way better than before. But we are still some distance away from impressionist effects.
Now the strokes in our generated image looks somewhat similar to impresionist paintings. However, the image still looks blurred. Well, this is because the previous approach does not pay any attention to edges. Hence, strokes are clipped when it reaches an edge.
Here is result of edge detection:
Original Photo
Detected Edges Using Canny Edge Detector
Here is the comparision of results:
Without Edge Clipping
With Edge Clipping
Note that the edge detector uses a low-pass(Gaussian filtered) image so that it can be less sensitive to some minor details.
The generated image still does not seem to be enough impressionist-ish. In previous projects, professor Hoiem mentioned that gradient domain processing can also be used to create non-photorealistic rendering effects. So it must have something to do with gradients.
Lets take a look at the Starry Night Over the Rhône by Van Gogh:
You may notice that the strokes are not fixed to around 45 degress to the x axis. So how are we going to determine the orientation of each stroke? The answer is "gradient".
In NPR(non-photorealistic rendering), we are trying to preserve edges and simultaneously minimize the gradient within areas without edges. Hence, we can use the gradient normal for the orientation of strokes since gradient direction represents the the direction which intensity changes most frequently.
Here is the comparision of results:
Without Using Gradient Normal
Using Gradient Normal
Here is another comparision of results:
Without Using Gradient Normal
Using Gradient Normal
Using gradient normals as orientation of strokes also helps preserving edges.
In the preious results, the stroke orientation in the background looks kind of unnatural again. This problem is also related to image gradients.
Lets take the apple image as example:
Without Using Gradient Normal
Using Gradient Normal
You can see that the gradient directions in the background are almost all zero or near zero(Well that's because it has a white background). In that case, we cannot simply rely on them. Through observation, impressionist artists tend to use the gradient directions around edges for strokes far away from edges. Moreover, little difference in Gx
and Gy
can cause huge difference in angles.
Peter Litwinowicz also proposed a solution to this problem in his paper:
To accomplish this, gradient values are “thrown out” at pixel locations where the Magnitude((Gx,Gy)) is near zero. In this implementation, this is approximated by the test: |Gx| < 3.0 and |Gy| < 3.0, which was empirically found to be useful. The gradient at pixels with near zero gradient magnitude are then replaced by interpolating surrounding "good" gradients
At first I used a brute force seraching algorithm for finding the nearest non-zero neighbor for near-zero pixels but the running time is terrible when the resolution of input images went up. So I am using a kd-tree instead for better performence.
Here is the result of replacing "bad" gradients:
Original Gradient Directions
After Replacing "Bad" Gradient
Here is the comparision of results:
Without Using Gradient Normal
Using Gradient Normal
There still remains a lot to be improved for this project:
It almost took my computer two hours to generate the Shanghai Night View image(1500 * 993). The Matlab profiler says the antialiasing code account for the majority of running time. I think optimizing that part of code should result in a great improvement of overall running time.
If you take a closer look at Van Gogh or Monet's painting, you will find that the brush stroke textures of each stroke are varying from each other and the color within one stroke is also varying. This is probably a combinational effect of gravity, the strength when painting the stroke or other complicated factors. I believe it will be difficult to completely replicate impressionist stroke textures but it certainly should be better than using uniform color line-shaped stokes.
I used the values from center pixels of each stroke and achieved reasonably good quality. However, Peter Litwinowicz suggested to use bilinear interpolation of colors and gradient directions. I think this can slightly improve the color and orientation accuracy for strokes. For example, if there is a pixel with very distinct color in your input image and, with extremely bad luck, the god of randomness decides to use its color and gradient normal at that position in your output image, it may result in a very werid-looking and unnatural stroke.


The red box and headline should have faded out, shifting the page up.
The blue box should slide up and the orange box should expand to the right, become semi-transparent and then shift to the left when the blue box disappears.
Clicking the pink box should show an alert.
click me
The green box should lose some opacity.


{% extends 'base.html' %} {% block nav_knowledge_base %}active{% endblock %} {% block header %}
You can add rules with the form above or you can upload a file with rule definitions below. See the instructions for more information on the format.
The retrieval run through the Wikidata dump takes a while. First define all the rules above and then start the retrieval for extracting the rules in parallel.


java.lang
classes.See: Description
| Class | Description |
|---|---|
| AnnotationUtils |
Helper methods for working with
Annotation instances. |
| ArrayUtils |
Operations on arrays, primitive arrays (like
int[] ) and
primitive wrapper arrays (like Integer[] ). |
| BitField |
Operations on bit-mapped fields.
|
| BooleanUtils |
Operations on boolean primitives and Boolean objects.
|
| CharEncoding |
Character encoding names required of every implementation of the Java platform.
|
| CharSequenceUtils |
Operations on
CharSequence that are
null safe. |
| CharSet |
A set of characters.
|
| CharSetUtils |
Operations on
CharSet instances. |
| CharUtils |
Operations on char primitives and Character objects.
|
| ClassUtils |
Operates on classes without using reflection.
|
| Conversion |
Static methods to convert a type into another, with endianness and bit ordering awareness.
|
| EnumUtils |
Utility library to provide helper methods for Java enums.
|
| LocaleUtils |
Operations to assist when working with a
Locale . |
| ObjectUtils |
Operations on
Object . |
| ObjectUtils.Null |
Class used as a null placeholder where
null
has another meaning. |
| RandomStringUtils |
Operations for random
String s. |
| Range<T> |
An immutable range of objects from a minimum to maximum point inclusive.
|
| SerializationUtils |
Assists with the serialization process and performs additional functionality based
on serialization.
|
| StringEscapeUtils |
Escapes and unescapes
String s for
Java, Java Script, HTML and XML. |
| StringUtils |
Operations on
String that are
null safe. |
| SystemUtils |
Helpers for
java.lang.System . |
| Validate |
This class assists in validating arguments.
|
| Enum | Description |
|---|---|
| ClassUtils.Interfaces |
Inclusivity literals for
ClassUtils.hierarchy(Class, Interfaces) . |
| JavaVersion |
An enum representing all the versions of the Java specification.
|
| Exception | Description |
|---|---|
| NotImplementedException |
Thrown to indicate that a block of code has not been implemented.
|
| SerializationException |
Exception thrown when the Serialization process fails.
|
java.lang
classes.Most of these classes are immutable and thus thread-safe. However Charset is not currently guaranteed thread-safe under all circumstances.
Copyright © 2001–2014 The Apache Software Foundation. All rights reserved.


0
–
0
HACK ARENA
Room name
User name
Team
Blue
Red
Class
Warrior
Mage
Healer
Move:
A,S,D,W
Attack:
↑ ↓ ← →
Change Attack:
Space
Chat:
Enter
team won!


hangman.html // Pick A Random Word // Take the Player's Guess // Quit the game //Check that the player guess // Keep Track of Letter the player guessed // Show player Progress. // End when player guess word.


Please fill in user's information.
Username
:
[username feedback]
Password
:
[password feedback]
First Name
:
[first name feedback]
Last Name
:
[last name feedback]
E-Mail
:
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Save
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--- layout: page title: Contact subtitle: "" css: "/css/contact.css" ---
You can email us or contact us using the form below:


From conducting experimental research, I’ve become motivated to deliver data-driven insights to curate compelling data stories and a data-first culture. In particular, I see an overarching goal between software engineering, computational science and psychology. The goal being to empathises with user capabilities and limitations, and create their own personalised data journey.
Now equipped with the essential data engineering and data analytics knowledge, I am enthused about ideating, conceptualizing and producing consumer-centric as well as technology-enabled products from level-zero. I look to use Big Data obtained from social networks and smart wearables to ensure success across the commercial and consumer marketing functions, deeply ingrained in data analytics and delivery of revenue marketing strategies.
Fully aware of the software development life cycle, I look to thrive in data roles to which will stimulate me to curate data driven insights to influence commercial and consumer marketing functions.
Bachelor of Science in Psychology with a Professional Placement
Data Engineering Programme
Birmingham, United Kingdom
Birmingham, United Kingdom
Bath, United Kingdom
Cardiff, United Kingdom
Birmingham, United Kingdom


Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron agents.
Bases: openstack_dashboard.api.base.APIDictWrapper
Bases: openstack_dashboard.api.network_base.FloatingIpManager
Bases: openstack_dashboard.api.base.APIDictWrapper
Bases: openstack_dashboard.api.base.APIDictWrapper
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron Networks.
Bases: openstack_dashboard.api.base.APIDictWrapper
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron ports.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron profiles.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron routers.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron routes extra route.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Bases: openstack_dashboard.api.network_base.SecurityGroupManager
Gets security groups of an instance.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron subnets.
Bases: openstack_dashboard.api.neutron.NeutronAPIDictWrapper
Wrapper for neutron subnetpools.
Check if a feature-specific field can be displayed.
This method check a permission for a feature-specific field. Such field is usually provided through Neutron extension.
| Parameters: |
|
|---|
Convert an ip version number to a human-friendly string.
List neutron resources with handling RequestURITooLong exception.
If filter parameters are long, list resources API request leads to 414 error (URL is too long). For such case, this method split list parameters specified by a list_field argument into chunks and call the specified list_method repeatedly.
| Parameters: |
|
|---|
Create a network object.
| Parameters: |
|
|---|---|
| Returns: | Network object |
Return a network list available for the tenant.
The list contains networks owned by the tenant and public networks. If requested_networks specified, it searches requested_networks only.
Create a port on a specified network.
| Parameters: |
|
|---|---|
| Returns: | Port object |
Retrieve servers networking information from Neutron if enabled.
Should be used when up to date networking information is required, and Nova’s networking info caching mechanism is not fast enough.
Create a subnet on a specified network.
| Parameters: |
|
|---|---|
| Returns: | Subnet object |
Although both cidr+ip_version and subnetpool_id+preifxlen is listed as optional you MUST pass along one of the combinations to get a successful result.
Create a subnetpool.
ip_version is auto-detected in back-end.
Parameters: request – Request context name – Name for subnetpool prefixes – List of prefixes for pool
Keyword Arguments (optional): min_prefixlen – Minimum prefix length for allocations from pool max_prefixlen – Maximum prefix length for allocations from pool default_prefixlen – Default prefix length for allocations from pool default_quota – Default quota for allocations from pool shared – Subnetpool should be shared (Admin-only) tenant_id – Owner of subnetpool
Returns: SubnetPool object


|
| string | Url [get, set] |
| | (required) The URL to the icon image (absolute or relative to your script path). More...
|
| |
| string | RetinalUrl [get, set] |
| | The URL to a retina sized version of the icon image (absolute or relative to your script path). Used for Retina screen devices. More...
|
| |
| Size? | Size [get, set] |
| | Size of the icon image in pixels. More...
|
| |
| Point? | Anchor [get, set] |
| | The coordinates of the "tip" of the icon (relative to its top left corner). The icon will be aligned so that this point is at the marker's geographical location. Centered by default if size is specified, also can be set in CSS with negative margins. More...
|
| |
| Point | PopupAnchor = Point.Empty [get, set] |
| | The coordinates of the point from which popups will "open", relative to the icon anchor. More...
|
| |
| Point | TooltipAnchor = Point.Empty [get, set] |
| | The coordinates of the point from which tooltips will "open", relative to the icon anchor. More...
|
| |
| string | ShadowUrl [get, set] |
| | The URL to the icon shadow image. If not specified, no shadow image will be created. More...
|
| |
|
string | ShadowRetinalUrl [get, set] |
| |
| Size? | ShadowSize [get, set] |
| | Size of the shadow image in pixels. More...
|
| |
| Size? | ShadowAnchor [get, set] |
| | The coordinates of the "tip" of the shadow (relative to its top left corner) (the same as iconAnchor if not specified). More...
|
| |
| string | ClassName = string.Empty [get, set] |
| | A custom class name to assign to both icon and shadow images. Empty by default. More...
|
| |
◆ Anchor
| Point? BlazorLeaflet.Models.Icon.Anchor |
|
getset |
The coordinates of the "tip" of the icon (relative to its top left corner). The icon will be aligned so that this point is at the marker's geographical location. Centered by default if size is specified, also can be set in CSS with negative margins.
◆ ClassName
| string BlazorLeaflet.Models.Icon.ClassName = string.Empty |
|
getset |
A custom class name to assign to both icon and shadow images. Empty by default.
◆ PopupAnchor
| Point BlazorLeaflet.Models.Icon.PopupAnchor = Point.Empty |
|
getset |
The coordinates of the point from which popups will "open", relative to the icon anchor.
◆ RetinalUrl
| string BlazorLeaflet.Models.Icon.RetinalUrl |
|
getset |
The URL to a retina sized version of the icon image (absolute or relative to your script path). Used for Retina screen devices.
◆ ShadowAnchor
| Size? BlazorLeaflet.Models.Icon.ShadowAnchor |
|
getset |
The coordinates of the "tip" of the shadow (relative to its top left corner) (the same as iconAnchor if not specified).
◆ ShadowSize
| Size? BlazorLeaflet.Models.Icon.ShadowSize |
|
getset |
Size of the shadow image in pixels.
◆ ShadowUrl
| string BlazorLeaflet.Models.Icon.ShadowUrl |
|
getset |
The URL to the icon shadow image. If not specified, no shadow image will be created.
◆ Size
| Size? BlazorLeaflet.Models.Icon.Size |
|
getset |
Size of the icon image in pixels.
◆ TooltipAnchor
| Point BlazorLeaflet.Models.Icon.TooltipAnchor = Point.Empty |
|
getset |
The coordinates of the point from which tooltips will "open", relative to the icon anchor.
◆ Url
| string BlazorLeaflet.Models.Icon.Url |
|
getset |
(required) The URL to the icon image (absolute or relative to your script path).
The documentation for this class was generated from the following file:
- BlazorLeaflet/BlazorLeaflet/Models/Icon.cs