<QtEndian> - Endian Conversion Functions
The <QtEndian> header provides functions to convert between little and big endian representations of numbers. More...
Functions
| T | qFromBigEndian(T src) |
| T | qFromBigEndian(const void *src) |
| T | qFromLittleEndian(T src) |
| T | qFromLittleEndian(const void *src) |
| T | qToBigEndian(T src) |
| void | qToBigEndian(T src, void *dest) |
| T | qToLittleEndian(T src) |
| void | qToLittleEndian(T src, void *dest) |
Function Documentation
T qFromBigEndian(T src)
This is an overloaded function.
Converts src from big-endian byte order and returns the number in host byte order representation of that number. On CPU architectures where the host byte order is little-endian (such as x86) this will return src with the byte order swapped; otherwise it will return src unmodified.
This function was introduced in Qt 4.3.
T qFromBigEndian(const void *src)
Reads a big-endian number from memory location src and returns the number in the host byte order representation. On CPU architectures where the host byte order is little-endian (such as x86) this will swap the byte order; otherwise it will just read from src.
Note: Template type T
can either be a qint16, qint32 or qint64. Other types of integers, e.g., qlong, are not applicable.
Note: Since Qt 5.7, the type of the src parameter is a void pointer.
There are no data alignment constraints for src.
This function was introduced in Qt 4.3.
See also qFromLittleEndian(), qToBigEndian(), and qToLittleEndian().
T qFromLittleEndian(T src)
This is an overloaded function.
Converts src from little-endian byte order and returns the number in host byte order representation of that number. On CPU architectures where the host byte order is big-endian (such as PowerPC) this will return src with the byte order swapped; otherwise it will return src unmodified.
This function was introduced in Qt 4.3.
T qFromLittleEndian(const void *src)
Reads a little-endian number from memory location src and returns the number in the host byte order representation. On CPU architectures where the host byte order is big-endian (such as PowerPC) this will swap the byte order; otherwise it will just read from src.
Note: Template type T
can either be a qint16, qint32 or qint64. Other types of integers, e.g., qlong, are not applicable.
Note: Since Qt 5.7, the type of the src parameter is a void pointer.
There are no data alignment constraints for src.
This function was introduced in Qt 4.3.
See also qFromBigEndian(), qToBigEndian(), and qToLittleEndian().
T qToBigEndian(T src)
This is an overloaded function.
Converts src from host byte order and returns the number in big-endian byte order representation of that number. On CPU architectures where the host byte order is little-endian (such as x86) this will return src with the byte order swapped; otherwise it will return src unmodified.
This function was introduced in Qt 4.3.
void qToBigEndian(T src, void *dest)
Writes the number src with template type T
to the memory location at dest in big-endian byte order.
Note that template type T
can only be an integer data type (signed or unsigned).
There are no data alignment constraints for dest.
Note: Since Qt 5.7, the type of the dest parameter is a void pointer.
This function was introduced in Qt 4.3.
See also qFromBigEndian(), qFromLittleEndian(), and qToLittleEndian().
T qToLittleEndian(T src)
This is an overloaded function.
Converts src from host byte order and returns the number in little-endian byte order representation of that number. On CPU architectures where the host byte order is big-endian (such as PowerPC) this will return src with the byte order swapped; otherwise it will return src unmodified.
This function was introduced in Qt 4.3.
void qToLittleEndian(T src, void *dest)
Writes the number src with template type T
to the memory location at dest in little-endian byte order.
Note that template type T
can only be an integer data type (signed or unsigned).
There are no data alignment constraints for dest.
Note: Since Qt 5.7, the type of the dest parameter is a void pointer.
This function was introduced in Qt 4.3.
See also qFromBigEndian(), qFromLittleEndian(), and qToBigEndian().


Interface for authentication of the user upon his credentials before generation of the tokens.
| Methods | |
|---|---|
|
public
|
Authenticates the user based on the specified credentials. |
Authenticates the user based on the specified credentials.
| Name | Pass type | Value type | Default value | Description |
|---|---|---|---|---|
| $credentials | by value | array |
The credentials may vary depending on implementation. |
The method should return the user id upon success, otherwise false.
It might throw an exception in the case of any system errors.
It should throw the InvalidCredentialsException if the authentication fails.
It should throw the MissingParametersException if any required paramters are empty.


Note
Click here to download the full example code
CTFs are computed for four labels in the MNE sample data set for linear inverse operators (MNE, dSPM, sLORETA). CTFs describe the sensitivity of a linear estimator (e.g. for one label) to sources across the cortical surface. Sensitivity to sources outside the label is undesirable, and referred to as “leakage” or “cross-talk”.
Out:
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
2 source spaces read
Desired named matrix (kind = 3523) not available
Read MEG forward solution (7498 sources, 306 channels, free orientations)
Desired named matrix (kind = 3523) not available
Read EEG forward solution (7498 sources, 60 channels, free orientations)
MEG and EEG forward solutions combined
Source spaces transformed to the forward solution coordinate frame
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...

Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Noise covariance matrix read.
22494 x 22494 diagonal covariance (kind = 2) found.
Source covariance matrix read.
22494 x 22494 diagonal covariance (kind = 6) found.
Orientation priors read.
22494 x 22494 diagonal covariance (kind = 5) found.
Depth priors read.
Did not find the desired covariance matrix (kind = 3)
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
Reading a source space...
Computing patch statistics...
Patch information added...
Distance information added...
[done]
2 source spaces read
Read a total of 4 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Average EEG reference (1 x 60) active
Source spaces transformed to the inverse solution coordinate frame
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
About to process 4 labels
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 1
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a full noise covariance matrix (3 small eigenvalues omitted)
Applying inverse operator to ""...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
[done]
Dimension of inverse matrix: (7498, 306)
Computing SVD within labels, using 1 component(s)
First 5 singular values: [11650.27808851 7034.33675366 3267.03724057 2762.39609838
2221.0665056 ]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 63.6% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [8512.68820924 7872.78518609 6741.64926914 3353.04131104 2498.07715718]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 35.6% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [15630.02296497 7418.15820631 5346.56712701 4258.90971316
1958.40295249]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 69.4% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [18096.44764144 8773.30811442 4831.2396142 3456.55903612
3129.60231085]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 71.5% variance in label.
Average patch normals will be employed in the rotation to the local surface coordinates....
Converting to surface-based source orientations...
[done]
About to process 4 labels
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 1
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a full noise covariance matrix (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Applying inverse operator to ""...
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
dSPM...
[done]
Dimension of inverse matrix: (7498, 306)
Computing SVD within labels, using 1 component(s)
First 5 singular values: [7.55262104e+13 5.83910239e+13 3.44075225e+13 2.36505439e+13
1.94336945e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 48.7% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [9.07577774e+13 7.61818100e+13 5.75087046e+13 3.39598529e+13
2.66587446e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 41.4% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [5.31303675e+13 4.01608741e+13 2.99674179e+13 2.09348683e+13
1.16746740e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 47.2% variance in label.
Computing SVD within labels, using 1 component(s)
First 5 singular values: [9.06302929e+13 5.13491534e+13 3.33843084e+13 3.13749745e+13
1.90838680e+13]
(This tells you something about variability of estimators in sub-inverse for label)
Your 1 component(s) explain(s) 59.3% variance in label.
# Author: Olaf Hauk<EMAIL_ADDRESS>#
# License: BSD (3-clause)
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import cross_talk_function, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects/'
fname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
fname_label = [data_path + '/MEG/sample/labels/Aud-rh.label',
data_path + '/MEG/sample/labels/Aud-lh.label',
data_path + '/MEG/sample/labels/Vis-rh.label',
data_path + '/MEG/sample/labels/Vis-lh.label']
# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# read label(s)
labels = [mne.read_label(ss) for ss in fname_label]
inverse_operator = read_inverse_operator(fname_inv)
# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr ** 2
mode = 'svd'
n_svd_comp = 1
method = 'MNE' # can be 'MNE', 'dSPM', or 'sLORETA'
stc_ctf_mne = cross_talk_function(
inverse_operator, forward, labels, method=method, lambda2=lambda2,
signed=False, mode=mode, n_svd_comp=n_svd_comp)
method = 'dSPM'
stc_ctf_dspm = cross_talk_function(
inverse_operator, forward, labels, method=method, lambda2=lambda2,
signed=False, mode=mode, n_svd_comp=n_svd_comp)
time_label = "MNE %d"
brain_mne = stc_ctf_mne.plot(hemi='rh', subjects_dir=subjects_dir,
time_label=time_label,
figure=mlab.figure(size=(500, 500)))
time_label = "dSPM %d"
brain_dspm = stc_ctf_dspm.plot(hemi='rh', subjects_dir=subjects_dir,
time_label=time_label,
figure=mlab.figure(size=(500, 500)))
# Cross-talk functions for MNE and dSPM (and sLORETA) have the same shapes
# (they may still differ in overall amplitude).
# Point-spread functions (PSfs) usually differ significantly.
Total running time of the script: ( 0 minutes 15.649 seconds)


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I am passionate Software Developer with 5+ years of developement and devops experience.
My strong forte is Java and Kubernetes in devops. My hobbies are running and blogging.
February 2020 - August 2020
• Managed the cloud and services orchestration project worth $80,000.
• Enhanced to orchestrate and migrate existing infrastructure environment of Kubernetes to a private data center with 3000 CPU cores. Created technical documentation to recreate the environment with different profile like QA and prod
• Designed and developed production-grade Kubernetes cluster for docker container orchestration of 24 Java microservices, Flyway, RabbitMQ, Postgres, pgAdmin, SQL Server, MongoDB, and Kafka from scratch and tested in swagger: project completed within 2 months.
• Demonstrated knowledge of company's mission, purpose, goals, and the ability to achieve them with a roadmap to internal teams and external partners (Google SADA).
• Helped team visualize and assured code quality through Jacoco plugin in maven project and Jenkins.
• Created UAT test suites and unit tested 2 microservices with 80% of code coverage using Junit 4.
• Gathered requirements and improved security through authorization controller in Java – multi threading.
• Revised the applications architecture which reduced the size of 11 dockers by 50% and performance by 25%. Introduced Kafka and RabbitMQ into microservices and configured postgres and mongodb databases.
July 2017 - August 2018
• Gathered requirements and built 3 new micro services of ATM application in a test-driven development environment by ensuring code quality through unit and integration testing using Java – Spring Boot and Java Script – Angular.
• Communicated with customers (Wells Fargo and Chase) to revisit priorities, understand the product deliverables, and expected timelines during ad hoc requests in SDLC. Supported System integration testing (SIT).
• Troubleshooted and analyzed customer transaction journals to identify cause of 22 bugs and resolved them.
• Built APIs to simulate the ATM application in VM’s and deployed using CI/CD pipeline through Jenkins.
• Reduced manual effort and saved companies time by 3 hours for every release by creating tool to automate gathering the test results data, manage reports on multiple key performance indicators as a part of release management.
• Collaborated with customers and UI/UX teams to design and develop 12 interactive screens and controller endpoints.
• Provided insights to the team members and suggested new technologies to automate operations of code deployment
• Successfully prepared and mentored 2 team members across multiple transactions and other technical aspects of the application.
• Created technical documentation and user guide on end to end transactions of cash and check deposits.
April 2014 - April 2017
• Developed an online modelling of proactive moderation system for auction fraud detection in an e-commerce website using Java, Selenium Framework for testing, and SQL on a cloud.
• Tested with 1000+ customers and 200+ vendors with performance of 100% through 3 months. Designed the application using GoF design patterns and microservices.
(408)627-1354
mhprasanthi@gmail.com


I am a Web Designer- Frontend and Backend, C# Developer I was born in Poland and I moved to Italy,
educated in Avellino, Italy and have been living in Naples alone since 2009.
I moved to London UK
on 22-09-2017, with no restrictions to work. I hold a full EU driving license.
I have the ability to work in a team and by myself, focused and hardworking. Always work towards achieving the best results. Swift to identifying issues and ways to improve or solve them. Attentive to details, accompanied by my great desire to do well, which allow me to deal with various situations without difficulty.
Able to play a key role throughout web project development to ensure maximum accessibility, customer experience and usability. Very good organize, deadline orientated and able to produce detailed technical specifications from clients’ requirements. A quick learner who can absorb new ideas and can communicate clearly and effectively.
I work with C# and all the other things that usually go with it, like ASP.NET, .Net Core, MVC, APIs,
WebForms, WinForms, MySQL- SQL Server, Entity Framework, ADO.NET, LINQ.
I really enjoy working as Front-End using HTML, CSS, JavaScript, Bootstrap, jQuery, Ajax and other
technologies along with Adobe Photoshop and Illustrator that overall provide better UX.
▶Development IDEs and tools:
VS Code, Visual Studio, and Brakets along extensions such as Bootstrap, Git, MySQL- SQL Server, etc.
From when in March 2019 I started to work at Tlc Marketing, I started to use new technologies solution for development and releases. First of all, I learned about the Angular 6+ framework and all the linked options to help to develop a web app( like Angular Material, Ngx, etc). At the same time, I had to learn how to use Azure (DevOps and Portal) for deployment, build and fix Pipelines (I'm just a beginner, I'm not a DevOps); or for database management (to store data and metadata) I had the opportunity to learn about Cosmos DB, and then PowerBi and DAX to perform the queries to create the reports . The, of course, I started to work in C# with .NET Core 2.2+, in a Microservices Architecture, or how to create, maintain and consume an API. Working in a team gave me the opportunity some time to apply Pair programming and extreme Programming practice and of course TDD to deliver clean readable code, not perfect but easy to understand and debug.
From several years I was exploring coding through free online services such as videos on youtube(my native language is the "old" Pascal) or websites such as CodeCamp and Udemy. I was greatly intrigued by the process of writing a code and to create something. I decided I wanted to further my understanding and knowledge and enrolled in the full-time developer's programme at Just IT. I was exposed to several languages such as JavaScript, HTML, CSS, C#, asp.Net.
My first long period job here in the UK. As Waiter, I needed strong communication skills(i really improved my English Skills in that period) and a high attention to detail. As required, I was adept at to organize the floor with my mates, welcoming and serving guests and accepting guest food and beverage orders in a professional manner.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!
My family manage a little farm in Italy, i work there from when i was a teenager. Our product are vegetable, chicken and nuts (it's our main income). I'm in charge to organize employers hiring, stuff to buy, and i work in the proprety too as well!
Completed Diploma exam, equivalent to GCSE’s from V. De Caprariis School, 70%
(Grade A) in 2008.
Mathematics(A), Physics, Chemical(A), Science(A), English(B)


…in JavaScript is really
JavaScript is not a strict OO language like Java, but we can still use it in an object-oriented way.
Easy to Understand
Without seeing my code, you can probably guess what an object called Bike
can do (or not do)
Encapsulation
a fancy way of saying "put all of my properties/behaviors in one place where other objects can't touch them"
Inheritance
Just as you inherited traits from you mom, your objects may inherit traits from other objects
Every object in JavaScript has a prototype.
All JavaScript objects inherit their properties and their functions from their prototype.
This is called prototypal inheritance
We know that when we create a new array:
length
propertymap()
functionfilter()
functionjoin()
functionAn array inherits these properties and functions from an Array
prototype that defines what an array is (properties) and what it can do (functions)
Let's use a book as an example of an object prototype.
Properties:
Function:
There can be many instances
of object prototypes:
by Octavia Butler
264 pages
by Margaret Atwood
311 pages
by Madeline L'Engle
211 pages
Each is an instance of the Book
object prototype.
Define a constructor function with the object prototype's properties and functions:
function Book(title, author, numPages) {
this.title = title;
this.author = author;
this.numPages = numPages;
this.currentPage = 0;
this.read = function() {
this.currentPage = this.numPages;
console.log("You read " + this.numPages + " pages!");
};
}
Conventionally, object prototype names are capitalized.
http://bit.ly/intermediate-js-bookNow that we have a prototype, we can instantiate a new object:
var kindred = new Book('Kindred', "Octavia Butler", 211);
kindred.read(); // Will log "You read 264 pages!" to the console
This creates a new object that inherits the properties and functions from the prototype that we defined.
new
do?Object
this
to that objectprototype
property to be the constructor function's prototype
objectLet's say we've created two book objects using the Book prototype we set up earlier:
var persepolis = new Book('Persepolis', 'Marjane Satrapi', 160);
var frankenstein = new Book('Frankenstein', 'Mary Shelley', 280);
Let's add a series
property to the first book:
persepolis.series = 'Persepolis';
This new property gets added to Persepolis, but not to Frankenstein, and not to any other new objects created from the Book prototype.
We can use the prototype property to add new properties and functions to the object prototype:
Adding a property:
Book.prototype.series = '';
Adding a function:
Book.prototype.buy = function(){
console.log('You bought ' + this.name);
}
Let's use our Book prototype, but let's add a couple of new properties and functions
rating
- a number, rating out of 10genre
- a string, the genre of the bookgetRating()
- a function that logs a string to the console, e.g. "You rated this book x out of 10!"
Then instantiate a new Book
object and call your new functions on it.
Object prototypes can inherit properties and functions from other object prototypes.
title
author
pages
read()
Inherits from book, but also has:
filesize
download()
Inherits from book, but also has:
cover
burn()
First, define a constructor function
function PaperBack(title, author, numPages, cover) {
Book.call(this, title, author, numPages);
this.cover = cover;
}
Extend the Book object prototype
PaperBack.prototype = Object.create(Book.prototype);
Define a new function on this object prototype
PaperBack.prototype.burn = function() {
console.log("Omg, you burnt all " + this.numPages + " pages");
this.numPages = 0;
}
Instantiate a new object
var paperback = new PaperBack("1984", "George Orwell", 250, "cover.jpg");
paperback.read();
paperback.burn();
All Ebooks are Books, but not all Books are Ebooks.
Building on the exercise from last time, create a new object prototype Hardcover
that extends Book
. The constructor should also take in an edition
property.
Instantiate a new Hardcover
object and call the read()
function on it.
Add a new function to the Hardcover
object prototype called getEdition()
that logs a message to the console, e.g. "Your book is a first edition!"
By passing in a config object, we can make our code easier to read and understand:
function Book(config) {
this.title = config.title;
this.author = config.author;
this.numPages = config.numPages;
this.currentPage = 0;
}
Then we pass in an object instead of a series of arguments to instantiate a new object:
var book = new Book({
title: "Middlemarch",
author: "George Eliot",
numPages: 736
});
Use the ||
operator to define a default value for properties.
function Book(config) {
this.title = config.title || "Untitled";
this.author = config.author || "Unknown";
this.numPages = config.numPages || 100;
this.currentPage = 0;
}
var book = new Book({
title: "In the Woods",
numPages: 464
});
Building on the exercise from last time, comment out the Hardcover
code.
Change the Book
constructor function to accept an object literal instead of multiple arguments.
Define a default value for every property.
Finally, instantiate a new Book
object and call the read()
function on it.
Any questions on object prototypes or inheritance?


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The Importance of Being Earnest is only one of many sources of witty sayings by Oscar Wilde. To take one example:
I never travel without my diary. One should always have something sensational to read in the train.


Portville loss ends Section VI streak in girls volleyball state final
By Will Springstead
Special to The News
GLENS FALLS – Portville had a lot going right in its first set against Valhalla on Sunday, but it just seemed to awaken a sleeping giant.
After losing the first set, the Vikings from Section I rallied to win the next three and earn the New York State Public High School Athletic Association title in Class C at Cool Insuring Arena. Game scores were 17-25, 25-22, 25-16 and 25-13.
Valhalla became the first non-Section VI school to win a Class C state title since Byron-Bergen of Section V in 2006. Portville was the two-time defending state Class C champion.
“Just good volleyball, I think,” Portville coach Kelly Unverdorben said of Valhalla’s win. “We’re young with a freshman, sophomore and an eighth grader on the court. (Valhalla) is pretty senior-dominant.
"I had a senior-dominant team last year, I know what that’s like. Sometimes with seniors, they just know they’re not going to let their season end. I don’t know that my team will ever let that happen again, either.”
With Valhalla leading 4-3 in the first set, Portville scored 13 of the next 15 points and held on for a comfortable win.
“I think the first set we just came out a little bit too pumped,” Valhalla coach Trish Ward said. “We just needed to relax and play our game, and once we did that, we proved what we could do.”
Portville rarely held a lead for the rest of the day, the last time being a 5-4 advantage in the second set. Valhalla got out to a 20-10 lead that set before Portville rallied and closed it to 24-22 before the Vikings closed it out.
Besides Class C tournament MVP Jasmine Grant’s all-out kills – she had 30 in total – the West Point-bound middle hitter scored a fair number of points for the Vikings on soft touches when Portville made sure to try to block her at the net.
“We all talked about that, and she and Steph (Sicilian), our setter, talked about it, too, really just focusing on finding the holes,” Ward said. “We knew Jasmine was probably going to get triple-blocked today, so there had to be some spots open.”
“We knew that,” Unverdorben said. “For some odd reason, we just could not get under that ball, and boy, we practice that an awful lot. Some days it’s just not there.”
Making the all-tournament team for Portville were setter Kylie Blessing (39 assists) and Olivia Cook (11 kills). Olivia Emley added 20 digs, while Laura Wilhelm and Tori Unverdorben had 13 and 10 kills, respectively.
“It’s OK,” Unverdorben said. “My team played so hard today, I couldn’t be prouder of them.”


Samurai Shodown trailer brings the steel-clashing fighter to Switch
But when will it see western shores?
by Chris MoyseSNK has released a new trailer for the Samurai Shodown Switch port, which brings all of the deliberately-paced sword-swinging action of the PS4 and Xbox One release to the Nintendo platform.
The Switch port will feature a selection of setup modes to suit your environment, and allows for two-player Vs. battle with each player using a single Joy-Con. Early adopters will also receive a copy of the cute Neo Geo Pocket Color release Samurai Shodown! 2, which is a neat bonus. Unfortunately, we're still waiting on an official worldwide launch date for both the Nintendo Switch and the PC port of this great modern fighter.
Samurai Shodown is available on Nintendo Switch in Japan December 12. It is available worldwide now on PS4 and Xbox One.
Samurai Shodown for Switch trailer [Gematsu]


5 How much R do I need to know to pass?
This is a communication exam because 30-40% of the points are based on your writing and data storytelling quality.
You do not need to become an R expert for this exam. While you are expected to develop a basic familiarity with R, the .Rmd template will provide you with the vast majority of the R commands needed. You will need to practice taking code templates and adjusting them to the specific variables and formulas you need. The most difficult coding questions will be during data exploration. These will ask you to
- Use basic mathematical operators and functions such as
exp()
andlog()
- Select, modify, and summarize data in a dataframe
- Display data from a dataframe in common types of plots, using ggplot2
When fitting predictive models, you will also need to
- Modify or add a formula or other parameters to model-fitting functions like
glm()
andrpart()
- Extract and displaying results from a fitted predictive model
You are not expected to construct loops, write functions, or use other programmatic techniques with R. The scope is limited to single-line commands.
You will have two cheat sheets for data visualization and base R. You can use this in your study to become familiar with how to find the R code quickly. These cheat sheets that the SOA gives you were designed by the RStudio team for everyone who uses R, and so we have gone through and removed the parts that you will not need to learn. For example, no one will be making new types of ggplot graphs under the “graphical primitives” section, which has been blocked out. Enroll in either of our online courses to get our simplified Base R cheat sheet and tutorial.
5.1 How to use the PA R cheat sheets?
Be observant and expect to spend twice as long explaining what your code is doing as you write the code itself. A few points are based on the organization your .Rmd file, although you do not need this to read like an essay. The vast majority of your time will be spent on your Word document.
The June 16 2020 Project Statement has this under “General information for candidates.”
Each task will be graded on the quality of your thought process, added or modified code, and conclusions
At a minimum, you must submit your completed report template and an .Rmd file that supports your work. Graders expect that your .Rmd code can be run from beginning to end. The code snippets provided should either be commented out or adapted for execution. Ensure that it is clear where in the code each of the tasks is addressed.
In other words, the results of your report must be consistent with what the grading team finds when they run your .Rmd file.
5.2 Example: SOA PA 6/16/20, Task 8
This question is from the June 16, 2020 exam. You can see that significantly only minor code changes need to be made. The remainder of this question consists of a short-answer response. This is very typical of Exam PA.
Already enrolled? Watch the full video: Practice Exams | Practice Exams + Lessons
- (4 obserations) Perform feature selection with lasso regression.
Run a lasso regression using the code chunk provided. The code will need to be modified to reflect your decision in Task 7 regarding the PCA variable.
You probably read this and asked “what is a lasso regression?” and with good reason - we haven’t yet covered this topic. All that you need to know is highlighted in black: you will need to change the code that they give you, which is below.
You need to choose between using one of two data sets:
- DATA SET A
- DATA SET B
Then ignore everything else!
# Format data as matrices (necessary for glmnet).
# Uncomment two items that reflect your decision from Task 7.
#DATA SET A
<- model.matrix(days ~ . - PC1, data.train)
lasso.mat.train <- model.matrix(days ~ . - PC1, data.test)
lasso.mat.test
#DATA SET B
# lasso.mat.train <- model.matrix(days ~ . - num_procs - num_meds - num_ip - num_diags, data.train)
# lasso.mat.test <- model.matrix(days ~ . - num_procs - num_meds - num_ip - num_diags, data.test)
set.seed(789)
<- cv.glmnet(
lasso.cv x = lasso.mat.train,
y = data.train$days,
family = "poisson", # Do not change.
alpha = 1 # alpha = 1 for lasso
)
If you wanted to use data set B, you would just add comments to data set A and uncomment B.
#DATA SET A
# lasso.mat.train <- model.matrix(days ~ . - PC1, data.train)
# lasso.mat.test <- model.matrix(days ~ . - PC1, data.test)
#DATA SET B
<- model.matrix(days ~ . - num_procs - num_meds - num_ip - num_diags, data.train)
lasso.mat.train <- model.matrix(days ~ . - num_procs - num_meds - num_ip - num_diags, data.test) lasso.mat.test
5.3 Example 2 - Data exploration
That last example was easy. They might ask you to do something like the following:
Template code:
# This code takes a continuous variable and creates a binned factor variable.
# The code applies it directly to the capital gain variable as an
# example. right = FALSE means that the left number is included and
# the right number excluded. So, in this case, the first bin runs from 0 to
# 1000 and includes 0 and excludes 1000. Note that the code creates a new
# variable, so the original variable is retained.
$cap_gain_cut <- cut(df$cap_gain, breaks = c(0, 1000, 5000, Inf), right = FALSE, labels = c("lowcg", "mediumcg", "highcg")) df
To answer this question correctly, you would need to
- Understand that the code is taking the capital gains recorded on investments,
cap_gain
, and then creating bins so that the new variable is “lowcg” for values between 0 and 1000, “mediumcp” from 1000 to 5000, and “highcg” for all values above 5000.
- Then you would need to interpret a statistical model
- Finally, use this result to change these cutoff values so that “low cg” is all values less than 5095.5, “medium cg” is all values from 5095.5 to 7055.5, and so forth. You would need to do this for two data sets,
data.train
, anddata.test
.
Solution code:
# This code cuts a continuous variable into buckets.
# The process is applied to both the training and test sets.
$cap_gain_cut <- cut(data.train$cap_gain, breaks = c(0, 5095.5, 7055.5, Inf), right = FALSE, labels = c("lowcg", "mediumcg", "highcg"))
data.train
$cap_gain_cut <- cut(data.test$cap_gain, breaks = c(0, 5095.5, 7055.5, Inf), right = FALSE, labels = c("lowcg", "mediumcg", "highcg")) data.test
Do not panic if all of this code is confusing. Just focus on reading the comments. As you can see, this is less of a programming question than it is a “logic and reasoning” question.


--- layout: jsdoc title: "Class: CachedRangeResponsePlugin" jsdocNav: - Modules: - workbox-background-sync - workbox-broadcast-cache-update - workbox-build - workbox-cache-expiration - workbox-cacheable-response - workbox-google-analytics - workbox-precaching - workbox-range-requests - workbox-routing - workbox-runtime-caching - workbox-sw - workbox-webpack-plugin ---
This class is meant to be automatically invoked as a plugin to a
RequestWrapper, which
is used by the workbox-sw
and workbox-runtime-caching
modules. If you
are not using workbox-sw
or workbox-runtime-caching
, then using the
handleRangeRequest function directly is recommended.
Under the hood, this will call handleRangeRequest()
, passing
in the appropriate request and response objects.
const workboxSW = new WorkboxSW();
const cacheFirstRangeHandler = workboxSW.strategies.cacheFirst({
cacheName: 'range-requests',
// There's no need to instantiate the class; all methods are static.
plugins: [workbox.rangedRequests.CachedRangeResponsePlugin]
});
// Create a route which will match all requests that have a Range: header,
// and handle them using a cache-first strategy, taking the Range: header
// into account.
workboxSW.registerRoute(
({event}) => event.request.headers.has('range'),
cacheFirstRangeHandler
);


--- title: scatter sidebar: function_reference_sidebar permalink: orientation.scatter.html folder: function_reference toc: false ---
plots orientations in 3d
| ori | orientation |
| axisAngle | axis angle projection |
| Rodrigues | rodrigues parameterization |
| Euler | 3d Bunge Euler plot |
| points | number of orientations to be plotted |
| center | orientation center |
| ignoreFundamentalRegion | plot orientation as they are |
| project2FundamentalRegion | project orientations to fundamentalRegion (default) |
| restrict2FundamentalRegion | ignore all orientations outside the fundamentalRegion |


|
Construct the deepest possible point in the DOM that's equivalent
to the given point, expressed as a node and an offset.
Arguments:
Returns:
A new point.
|
code » | ||||
|
Given a range, expand the range to include outer tags if the full contents of
those tags are entirely selected. This essentially changes the dom position,
but not the visible position of the range.
Ex.
Arguments:
Returns:
The expanded range.
|
code » | ||||
|
goog.editor.range.expandEndPointToContainer_(range, endpoint, opt_stopNode)
⇒
Given a range, expands the start or end points as far out towards the
range's common container (or stopNode, if provided) as possible, while
perserving the same visible position.
Arguments:
Returns:
The expanded range.
|
code » | ||||
|
Get the deepest point in the DOM that's equivalent to the endpoint of the
given range.
Arguments:
Returns:
The end point, expressed as a node and an offset.
|
code » | ||||
|
Returns whether the given range intersects with any instance of the given
tag.
Arguments:
Returns:
Whether the given range intersects with any instance of the given tag.
|
code » | ||||
|
Checks if a range is completely inside an editable region.
Arguments:
Returns:
Whether the range is completely inside an editable region.
|
code » | ||||
|
Given a range and an element, create a narrower range that is limited to the
boundaries of the element. If the range starts (or ends) outside the
element, the narrowed range's start point (or end point) will be the
leftmost (or rightmost) leaf of the element.
Arguments:
Returns:
A new narrowed range, or null if the element does not contain any part of the given range.
|
code » | ||||
|
goog.editor.range.normalize(range)
⇒
function(): goog.dom.AbstractRange
Given a range in the current DOM, create a factory for a range that
represents the same selection in a normalized DOM. The factory function
should be invoked after the DOM is normalized.
All browsers do a bad job preserving ranges across DOM normalization.
The issue is best described in this 5-year-old bug report:
https://bugzilla.mozilla.org/show_bug.cgi?id=191864
For most applications, this isn't a problem. The browsers do a good job
handling un-normalized text, so there's usually no reason to normalize.
The exception to this rule is the rich text editing commands
execCommand and queryCommandValue, which will fail often if there are
un-normalized text nodes.
The factory function creates new ranges so that we can normalize the DOM
without problems. It must be created before any normalization happens,
and invoked after normalization happens.
Arguments:
Returns:
function(): goog.dom.AbstractRange
A factory for a normalized range. Should be called after body.normalize() is called.
|
code » | ||||
|
Given a point in the current DOM, adjust it to reprecent the same point in
a normalized DOM.
See the comments on goog.editor.range.normalize for more context.
Arguments:
Returns:
The same point, for easy chaining.
|
code » | ||||
|
Position the cursor immediately to the left or right of "node".
Arguments:
Returns:
The newly selected range.
|
code » | ||||
|
Get the range to position the cursor immediately to the left or right
of "node".
Arguments:
Returns:
The range to select.
|
code » | ||||
|
Get the range to position the cursor immediately to the right or
left of "node". In Firefox, the selection parent is outside of "node",
so the cursor can effectively be moved to the end of a link node,
without being considered inside of it.
Note: This does not always work in WebKit. In particular, if you try to
place a cursor to the right of a link, typing still puts you in the link.
Bug: http://bugs.webkit.org/show_bug.cgi?id=17697
Arguments:
Returns:
The range to select.
|
code » | ||||
|
Normalizes the node, preserving a range of the document.
May also normalize things outside the node, if it is more efficient to do so.
Arguments:
Returns:
The range, adjusted for normalization.
|
code » | ||||
|
Saves the range by inserting carets into the HTML.
Unlike the regular saveUsingCarets, this SavedRange normalizes text nodes.
Browsers have other bugs where they don't handle split text nodes in
contentEditable regions right.
Arguments:
Returns:
A saved caret range that normalizes text nodes.
|
code » | ||||
|
goog.editor.range.selectNodeStart(node)
Cause the window's selection to be the start of this node.
Arguments:
|
code » | ||||
|
goog.editor.range.selectionPreservingNormalize(node)
Normalizes the node, preserving the selection of the document.
May also normalize things outside the node, if it is more efficient to do so.
Arguments:
|
code » |


<scribe> ACTION: do something [recorded in http://www.w3.org/2005/02/14-arch-minutes#action01]
HELLO!
MIDDLE!
BYE!
<scribe> ACTION: do something else [recorded in http://www.w3.org/2005/02/14-arch-minutes#action02]
This is scribe.perl Revision: 1.122 of Date: 2005/03/31 04:43:41 Check for newer version at http://dev.w3.org/cvsweb/~checkout~/2002/scribe/ Guessing input format: Normalized_Format (score 1.00) No ScribeNick specified. Guessing ScribeNick: dbooth Inferring Scribes: dbooth WARNING: No "Topic:" lines found. WARNING: No "Present: ... " found! Possibly Present: NamedAnchorHere You can indicate people for the Present list like this: <dbooth> Present: dbooth jonathan mary <dbooth> Present+ amy WARNING: No meeting title found! You should specify the meeting title like this: <dbooth> Meeting: Weekly Baking Club Meeting WARNING: No meeting chair found! You should specify the meeting chair like this: <dbooth> Chair: dbooth WARNING: No date found! Assuming today. (Hint: Specify the W3C IRC log URL, and the date will be determined from that.) Or specify the date like this: <dbooth> Date: 12 Sep 2002 Guessing minutes URL: http://www.w3.org/2005/02/14-arch-minutes People with action items: do WARNING: Input appears to use implicit continuation lines. You may need the "-implicitContinuations" option. WARNING: Duplicate named anchor: ignorable-duplicate WARNING: Duplicate named anchor: duplicate WARNING: No "Topic: ..." lines found! Resulting HTML may have an empty (invalid) <ol>...</ol>. Explanation: "Topic: ..." lines are used to indicate the start of new discussion topics or agenda items, such as: <dbooth> Topic: Review of Amy's report WARNING: IRC log location not specified! (You can ignore this warning if you do not want the generated minutes to contain a link to the original IRC log.)[End of scribe.perl diagnostic output]


The rational questions based on the article read will be displayed which will help you recall the prime components of the article and retain them adequately.


Death sentence for Spanish murderer upheld by Bangkok court
by Coconuts BangkokA court this morning doubled down on the death sentence for a 40-year-old Spanish man convicted of murdering a fellow Spaniard and dismembering him.
Judges upheld two prior rulings sentencing Artur Segarra to die for the 2016 murder of David Bernat in a grisly case brought to attention when the victim’s dismembered remains were found floating in different parts of the Chao Phraya River.
Segarra was in court today to hear his appeal denied for the second time since he was convicted in 2017. He’s been behind bars for over three years, most recently at the Bang Kwang Central Prison.
Spaniard sentenced to death in Thailand for grisly murder
Segarra reportedly turned red after the judges announced their ruling. Segarra’s lawyer, meanwhile, said that his client had made peace with death since he was sentenced in 2017. He added that fighting the case would be difficult because of the evidence against Segarra.
Six body parts were retrieved from the Chao Phraya River in January 2016.
One month later, police said they found his DNA and blood in Segarra’s apartment at the PG Rama 9 Condominium.
That’s where they concluded that Segarra tortured Bernat into wiring him upward of THB37 million before killing him and chopping up the 40-year-old man’s body. They filed charges of murder and concealing a body against him.
Segarra has exhausted his appeals and can now only be pardoned by the king. Once the legal process is over, he can also petition to have his sentence carried out in Spain, where there is no death penalty.
Related:
Spaniard sentenced to death in Thailand for grisly murder
Spanish man arrested over murder, dismemberment of fellow Spanish national
Cops say ‘foreign gang’ behind murder of dismembered man found in Chao Phraya
Body parts in Chao Phraya river are from Spanish businessman, say cops
Cops confirm body parts found in Chao Phraya belong to same person
Two more body parts found in Nonthaburi, Pathumthani
Human head, torso, limbs found in different locations of Chao Phraya River


Lab #4 - JavaScript and the Document Object Model
Part 1b
Create a new folder for lab4, and confirm that it is being served by Apache. You may choose to serve this lab from a new virtual host, though doing so is not required to receive credit.
Ensure that you have a browser plugin capable of debugging JavaScript (you should be familiar with setting breakpoints and watch variables from your previous CS/IT courses). If you are stuck with this, review the help for Debugging JavaScript using Developer Tools for Chrome as they are an excellent tool to learn with.
For this lab, you will be using an existing HTML document. Save the document as lab4.html in your lab4 folder.
Create the file lab4.js and use a <script> tag to reference it from your document.
Write a JavaScript function that iterates through the DOM of your document. For each Element node starting with the <html> element, recursively print the name of each node in the tree in a text-based format, prefixing each item with a dash for each additional layer of depth. Newlines are escaped via \n.
The first few lines should look something like this:
html
-head
--title
-body
Finally, replace the innerHTML of the element with the id "info" with the return value of this function.
To receive credit for Part 1: Show that your code iterates through the DOM and replaces the contents of #info with the resulting tree.
Modify your function in Part 1 to add an onclick event listener to each element in the body. The function bound to this event listener should display an alert box with the element's tag name.
Note that there is a much more performant way of doing this; we will discuss how in another lecture.
To receive credit for Part 2: Show how the onclick event bubbles through the DOM.
First, once the page has finished loading, copy your favorite quote (or some other text) to the end of the body using the cloneNode() and appendChild() methods on the appropriate Element objects.
Bind new onmouseover event listeners to each div (including the one you just copied), and in the function change the element's background color and move the element 10px to the right. For the onmouseout event for each element, change the background color back to white and reposition the element to its original location.
To receive credit for Part 3: Demonstrate that your onmouseout and onmouseover listeners fire correctly.


VISIT SINAI EGYPT
About
Location
Tourism
This is Egypt
Where CASTLES meet the SEA.
Read More
`
This is Egypt
Where MAGIC lives.

Read More
Camps
Hiking
Excursions
Diving
Yoga
Dining
Pyramids
Beaches


12
1
2
3
4
5
6
7
8
9
10
11
Vernon Malone
College and Career Academy
Timezones
Eastern Standard Time
Central Standard Time
Mountain Standard Time
Pacific Standard Time


So, you want to know about our robot?
Contrary to popular belief, much planning usually goes into the robot, whether it be drawing up the plans for the arm, or how much battery is being consumed at any one time; Planning has to be there every step of the way before anything can get done. Some plans for our robot start as primitive as this:
However no matter the planning big or small, all is taken into account and put forth in a way that benefits the team.
Once our builders get the plans for the robot, building can commence. For our builders we will often use equipment at our school's workshop however so for the most part, tools are never short. We will carefully plan each part that goes on the robot and quickly determine each and every piece that is necessary, and those pieces will be cut then applied accordingly to our robot.
For the Augusta High School Robotics team, the Notebook was the biggest and most important part of the competition, in fact most of our time was spent making sure the notebook was up to par with the high standards set by the team. The Notebook team consists of 4 people, who will rigorously work to collect data necessary for the Notebook to capture and write it down.
The programming team for Augusta High School consisted of 3 people. All three of which worked vigorously on the robot program to keep it free of bugs while at the same time keeping maximum readability in the code. The program for the robot was designed with RobotC and debugged with not only a test bed but also Simulink graciously provided by B.E.S.T. robotics.


Load ballot prior to voting Winning Vote (Vote for the candidate/choice that is winning)
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Choose when to vote: Vote Now (Send my vote now)
Vote Later (Send my vote a few minutes before the ballot ends)
Select ballot type: Poll (results are displayed live)
Election (results are displayed after end date)
First to X Votes (results are displayed live)
Seperate each candidate/choice with a comma:
Select voting restriction: Non-Whitelist (all e-mails with whitelisted domain are allowed to vote)
Whitelist (only certain e-mails with whitelisted domain are allowed to vote)


This transition indicates that a draft subscription was deleted. Therefore, a created subscription request will be also cancelled and deleted. No further actions with deleted drafts are required or available.
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--- layout: post title: honey pumpkin cornbread & shortcut chili date: 2011-10-20 02:07:14.000000000 -04:00 type: post published: true status: publish categories: - breads - budget bites - dinner - vegan - vegetarian tags: - budget bites - chili - cornbread - Happy Herbivore - hello kitty - honey - pumpkin - vegan meta: _edit_last: '1' author: login: sophie email<EMAIL_ADDRESS>display_name: Pika first_name: '' last_name: '' ---
Halloween is coming up right around the corner, which means…it's officially pumpkin season!
Unfortunately, it's also the start of (never-ending) midterm season, which means that things can get quite haphazard, to say the least. To celebrate a rare calm between deadlines and exams, I stirred up this hearty (and healthy!) bean chili, a stick-to-your-ribs rainy-day meal (perfect for hungry college students!). To go with it, I adapted Lindsay's cornbread recipe from the Happy Herbivore Cookbook to make it pumpkin (of course!) and honey flavored. I was a little skeptical, since it was not only vegan but fat free, but it was absolutely heavenly! So moist and sweet. Definitely the best cornbread recipe I've ever made.
If you have Lindsay's book, the recipe for cornbread is on page 49. Just substitute pumpkin puree for the applesauce, and honey for the maple syrup, if you're not vegan. No need for extra sugar!
Look at that Smart Balance shmear, mmm...
Shortcut Bean Chili
Serves 4
• 1 16-oz can kidney beans (or any sort of beans!)
• 1 16-oz can crushed tomatoes
• 1 green pepper, diced
• ½ sweet onion, diced
• 1 t cumin
• EVOO
1. In a medium saucepan, cook onions in olive oil until nice and caramelized. Stir in green peppers, and cook until tender.
2. Stir in remaining ingredients, and simmer over low heat until heated through.
3. Serve warm, with cheese and Honey-Pumpkin Cornbread.
P.S. Guess what came in the mail for me yesterday? A Hello Kitty Thermos! Yes, it's ridiculously pink, and yes, it cheers me up immensely. Can't wait to bring my chili in it to work :)


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Smalltalk-Archive
Projects Submitted to the
Innovation Technology Awards 16th Edition
1st: PharoGs
Submitted by
:
James Foster
Download
:
https://github.com/jgfoster/PharoGs
Demo
:
https://youtu.be/8x2zS6YXuIE
2nd: GraalSqueak: A Squeak/Smalltalk implementation for the GraalVM
Submitted by
:
Fabio Niephaus
Download
:
https://github.com/hpi-swa/graalsqueak/
Demo
:
https://www.youtube.com/watch?v=1HjzDvNityU
3rd: El Carrillon
Submitted by
:
Mariano Martinez Peck
Download
:
https://github.com/gerasdf/carrillon/tree/master/src.st
Demo
:
https://www.youtube.com/watch?v=mP-7XB4fnao
LiveTyping
Submitted by
:
Hernan Wilkinson
Download
:
https://github.com/hernanwilkinson/LiveTyping/
Demo
:
https://www.youtube.com/watch?v=N3RpsGeYeYk
NewWave
Submitted by
:
Sebastijan Kaplar
Download
:
https://github.com/skaplar/NewWave
Demo
:
https://www.youtube.com/watch?v=6O-9iNT7VDA&feature=youtu.be
PowerAssert
Submitted by
:
Thomas Dupriez, Steven Costiou & Stéphane Ducasse
Download
:
https://github.com/dupriezt/PowerAssert
Demo
:
**
Event Music Manager
Submitted by
:
Benoit Verhaeghe
Download
:
https://github.com/badetitou/EMM
Demo
:
https://youtu.be/41cP6vWnGo8
Autonomous Robot 100% on PharoThings IoT
Submitted by
:
Allex Oliveira, Steven Costiou, Louise Plantec, Mickaël Kerboeuf, Alain Plantec
Download
:
https://github.com/StevenCostiou/Pharo-PiBorg
Demo
:
https://www.youtube.com/watch?v=1j5WSCkIiKk&feature=youtu.be
Objective-Smalltalk
Submitted by
:
Marcel Weiher
Download
:
https://github.com/mpw/objective-smalltalk
Demo
:
https://www.youtube.com/watch?v=B0OUl-AW7bo
ALLSTOCKER
Submitted by
:
Masashi Umezawa & Kazunori Ueda
Download
:
https://allstocker.com/
Demo
:
https://alfresco.sorabito.com/share/s/gg44OnneRn-Fk330HIeHRg
Scarlet SmallTalk
Submitted by
:
John McIntosh & Michael Rueger
Download
:
https://github.com/LabWare/ScarletSmallTalk
Demo
:
https://vimeo.com/354081838
Pharo Chip Designer
Submitted by
:
Pavel Krivanek
Download
:
https://github.com/pavel-krivanek/PharoChipDesigner
Demo
:
https://www.youtube.com/watch?v=kPxq_319wBY
Experimental Completion
Submitted by
:
Myroslava Romaniuk
Download
:
https://github.com/myroslavarm/Experimental-Completion
Demo
:
https://youtu.be/3E81xNUGieA
PythonBridge
Submitted by
:
Alejandro Infante
Download
:
https://objectprofile.github.io/PythonBridge/
Demo
:
https://www.youtube.com/watch?v=csk0w7xwf1Y&feature=youtu.be
PharoCodeGenerator
Submitted by
:
Julien Delplanque
Download
:
https://github.com/juliendelplanque/PharoCodeGenerator
Demo
:
https://youtu.be/L3CiHWkW9Gs
DrTDD
Submitted by
:
Nicolas Papagna
Download
:
https://github.com/npapagna/drtdd-pharo
Demo
:
https://vimeo.com/354461588
PolyMath
Submitted by
:
Serge Stinckwich
Download
:
https://github.com/PolyMathOrg/PolyMath
Demo
:
**
Unsere Gelder
Submitted by
:
Christian Haider
Download
:
https://unsere-gelder.de
Demo
:
https://drive.google.com/file/d/1v1LtMxUYO75BxPJ0R7pVAIl1ed6s6sdi/view
Eurofaktura.com
Submitted by
:
David Gorisek
Download
:
http://www.eurofaktura.com/
Demo
:
https://youtu.be/yiWeECkzy-g?t=160
Voyage in the WODANIC ALPHA
Submitted by
:
Ronie Salgado
Download
:
http://wodenengine.com/wodanic-alpha-voyage
Demo
:
https://youtu.be/p7blxmlhHQQ
PintGlas
Submitted by
:
Tomohiro Oda
Download
:
https://github.com/tomooda/PintGlas
Demo
:
https://www.youtube.com/watch?v=tnI0FrXPbXQ&feature=youtu.be
A Smalltalk GraphQL Implementation
Submitted by
:
Juan Pablo Sandoval Alcocer
Download
:
https://github.com/OBJECTSEMANTICS/GraphQL
Demo
:
https://drive.google.com/file/d/1rQYrmz_O3fimOKxAlfJE1_H8z4FY0Mhq/view?usp=sharing
Polyglot
Submitted by
:
Nikhil Pinnaparaju
Download
:
https://github.com/PolyMathOrg/Polyglot
Demo
:
https://drive.google.com/file/d/1PgNS1xpwos1txeclhbzFfVlbrbs54pGs/view?usp=sharing
DrTests
Submitted by
:
Dayne Guerra & Julien Deplanque
Download
:
https://github.com/juliendelplanque/DrTests
Demo
:
https://www.youtube.com/watch?v=XKRL6neJAmA