public class ArrayOperator
extends java.lang.Object
ArrayOperation
includes frequently used operation
functions on double
arrays. The argument vector is
required to have been allocated memory before being used in
the array operations.| Constructor and Description |
|---|
ArrayOperator() |
| Modifier and Type | Method and Description |
|---|---|
static double[] |
allocate1DArray(int n)
Allocate continuous memory block for a 1D
double
array. |
static double[] |
allocate1DArray(int n,
double v)
Allocate continuous memory block for a 1D
double
array and assign all elements with a given value. |
static double[][] |
allocate2DArray(int m,
int n)
Allocate a 2D
double array and assign all
elements with zero by default. |
static double[][] |
allocate2DArray(int m,
int n,
double v)
Allocate a 2D
double array and assign all
elements with a given value. |
static int[] |
allocateIntegerVector(int n)
Allocate continuous memory block for a 1D
int
array. |
static int[] |
allocateIntegerVector(int n,
int v)
Allocate continuous memory block for a 1D
int
array. |
static double[][] |
allocateMatrix(int nRows,
int nCols)
Allocate memory for a 2D
double array. |
static double[] |
allocateVector(int n)
Allocate continuous memory block for a 1D
double
array. |
static double[] |
allocateVector(int n,
double v)
Allocate continuous memory block for a 1D
double
array and assign all elements with a given value. |
static int |
argmax(double[] V)
Compute the maximum argument.
|
static int |
argmax(double[] V,
int begin,
int end)
Compute the maximum argument.
|
static int |
argmin(double[] V)
Compute the minimum argument.
|
static int |
argmin(double[] V,
int begin,
int end)
Compute the minimum argument.
|
static void |
assign(double[][] res,
double v)
Set all elements of a 2D
double array res by
a real scalar v. |
static void |
assign(double[][] res,
double[][] A)
Populate a 2D
double array res by another
2D double array A, i.e., res[i][j] = A[i][j]. |
static void |
assign(double[] V,
double v)
Assign a 1D
double array by a real scalar. |
static void |
assign(int[] V,
int v)
Assign a 1D
int array by a real scalar. |
static void |
assignIntegerVector(int[] V,
int v)
Assign a 1D
int array by a real scalar. |
static void |
assignVector(double[] V,
double v)
Assign a 1D
double array by a real scalar. |
static void |
assignVector(double[] V1,
double[] V2)
Element-wise assignment operation.
|
static void |
clear(double[] V)
Clear all elements of a 1D
double array to zero. |
static void |
clear(double[][] M)
Clear all elements of a 2D
double array to zero. |
static void |
clearMatrix(double[][] M)
Clear all elements of a 2D
double array to zero. |
static void |
clearVector(double[] V)
Clear all elements of a 1D
double array to zero. |
static double[] |
colon(double begin,
double end)
Same as colon(begin, 1, end).
|
static double[] |
colon(double begin,
double d,
double end)
Generates a linearly spaced integer array with distance of
D between two consecutive numbers.
|
static int[] |
colon(int begin,
int end)
Same as colon(begin, 1, end).
|
static int[] |
colon(int begin,
int d,
int end)
Generates a linearly spaced integer array with distance of
D between two consecutive numbers.
|
static void |
divideAssign(double[][] res,
double v)
res = res / v.
|
static void |
divideAssign(double[] V,
double v)
Element-wise division and assignment operation.
|
static void |
divideAssign(double[] V1,
double[] V2)
Element-wise division and assignment operation.
|
static int |
fix(double x)
Round towards zero.
|
static double |
innerProduct(double[] V1,
double[] V2)
Inner product of two vectors, i.e.,
|
static double |
innerProduct(double[] V1,
double[] V2,
int from,
int to)
Inner product of two vectors, i.e.,
|
static double[][] |
inv(double[][] A)
Compute A \ I.
|
static void |
main(java.lang.String[] args) |
static double |
max(double[] V)
Compute the maximal element.
|
static double |
max(double[] V,
int begin,
int end)
Compute the maximal element.
|
static double |
mean(double[] V)
Compute the mean of a 1D
double array. |
static double |
min(double[] V)
Compute the minimal element.
|
static double |
min(double[] V,
int begin,
int end)
Compute the minimal element.
|
static double[] |
minus(double[] V1,
double[] V2)
Element-wise subtraction.
|
static void |
minusAssign(double[][] res,
double v)
res = res - v.
|
static void |
minusAssign(double[] V,
double v)
Element-wise subtraction and assignment operation.
|
static void |
minusAssign(double[] V1,
double[] V2)
Element-wise subtraction and assignment operation.
|
static void |
minusAssign(double[] res,
double a,
double[] V)
res -= a * V.
|
static void |
minusAssign(int[] V,
int v)
Element-wise subtraction and assignment operation.
|
static double[] |
operate(double[][] A,
double[] V)
res = A * V;
|
static double[] |
operate(double[] V,
double[][] A)
res' = V' * A
|
static void |
operate(double[] V1,
double[][] A,
double[] V2)
V1 = A * V2.
|
static void |
operate(double[] V1,
double[] V2,
double[][] A)
V1' = V2' * A.
|
static double[] |
plus(double[] V1,
double[] V2)
Element-wise addition.
|
static void |
plusAssign(double[][] res,
double v)
res = res + v.
|
static void |
plusAssign(double[] V,
double v)
Element-wise addition and assignment operation.
|
static void |
plusAssign(double[] V1,
double[] V2)
Element-wise addition and assignment operation.
|
static void |
plusAssign(double[] res,
double a,
double[] V)
res += a * V.
|
static void |
quickSort(double[] values,
double[] indices,
int start,
int end,
java.lang.String order)
Sort a 1D
double array in a specified order. |
static void |
quickSort(double[] values,
int[] indices,
int start,
int end,
java.lang.String order)
Sort a 1D
double array in a specified order. |
static int[] |
sort(double[] V)
Sort a
double array in an ascending order. |
static int[] |
sort(double[] V,
java.lang.String order)
Sort a
double array in a specified order. |
static double |
std(double[] V,
int flag)
Compute standard deviation.
|
static double |
sum(double[] V)
Compute the sum of a 1D
double array. |
static void |
sum2one(double[] V)
Sum a 1D
double array to one, i.e., V[i] = V[i] / sum(V). |
static double[] |
times(double[] V1,
double[] V2)
Element-wise multiplication.
|
static void |
timesAssign(double[][] res,
double v)
res = res * v.
|
static void |
timesAssign(double[] V,
double v)
Element-wise multiplication and assignment operation.
|
static void |
timesAssign(double[] V1,
double[] V2)
Element-wise multiplication and assignment operation.
|
public static void main(java.lang.String[] args)
args
- public static double[][] inv(double[][] A)
A
- a 2D double
arraypublic static int[] sort(double[] V, java.lang.String order)
double
array in a specified order.V
- a double
arrayorder
- "ascend" or "descend"public static int[] sort(double[] V)
double
array in an ascending order.V
- a double
arraypublic static void quickSort(double[] values, int[] indices, int start, int end, java.lang.String order)
double
array in a specified order.values
- a 1D double
array containing the values to be sortindices
- index arraystart
- start index (inclusive)end
- end index (inclusive)order
- a String
variable either "descend" or "ascend"public static void quickSort(double[] values, double[] indices, int start, int end, java.lang.String order)
double
array in a specified order.values
- a 1D double
array containing the values to be sortindices
- index arraystart
- start index (inclusive)end
- end index (inclusive)order
- a String
variable either "descend" or "ascend"public static int fix(double x)
x
- a real numberpublic static int[] colon(int begin, int d, int end)
begin
- starting point (inclusive)d
- distance between two consecutive numbersend
- ending point (inclusive if possible)public static int[] colon(int begin, int end)
begin
- starting point (inclusive)end
- ending point (inclusive)public static double[] colon(double begin, double d, double end)
begin
- starting point (inclusive)d
- distance between two consecutive numbersend
- ending point (inclusive if possible)public static double[] colon(double begin, double end)
begin
- starting point (inclusive)end
- ending point (inclusive)public static int argmax(double[] V)
V
- a 1D double
arraypublic static int argmax(double[] V, int begin, int end)
V
- a 1D double
arraybegin
- beginning index (inclusive)end
- end index (exclusive)public static int argmin(double[] V)
V
- a 1D double
arraypublic static int argmin(double[] V, int begin, int end)
V
- a 1D double
arraybegin
- beginning index (inclusive)end
- end index (exclusive)public static double min(double[] V)
V
- a 1D double
arraypublic static double max(double[] V)
V
- a 1D double
arraypublic static double min(double[] V, int begin, int end)
V
- a 1D double
arraybegin
- beginning index (inclusive)end
- end index (exclusive)public static double max(double[] V, int begin, int end)
V
- a 1D double
arraybegin
- beginning index (inclusive)end
- end index (exclusive)public static void assignVector(double[] V, double v)
double
array by a real scalar.V
- a 1D double
arrayv
- a real scalarpublic static void assign(double[] V, double v)
double
array by a real scalar.V
- a 1D double
arrayv
- a real scalarpublic static void assignIntegerVector(int[] V, int v)
int
array by a real scalar.V
- a 1D int
arrayv
- a real scalarpublic static void assign(int[] V, int v)
int
array by a real scalar.V
- a 1D int
arrayv
- a real scalarpublic static void clearVector(double[] V)
double
array to zero.V
- a 1D double
arraypublic static void clear(double[] V)
double
array to zero.V
- a 1D double
arraypublic static void clearMatrix(double[][] M)
double
array to zero.M
- a 2D double
arraypublic static void clear(double[][] M)
double
array to zero.M
- a 2D double
arraypublic static double[] allocate1DArray(int n)
double
array.n
- number of elements to be allocateddouble
array of length npublic static double[] allocate1DArray(int n, double v)
double
array and assign all elements with a given value.n
- number of elements to be allocatedv
- a real scalar to assign the 1D double
arraydouble
array of length npublic static double[] allocateVector(int n)
double
array.n
- number of elements to be allocateddouble
array of length npublic static double[] allocateVector(int n, double v)
double
array and assign all elements with a given value.n
- number of elements to be allocatedv
- a real scalar to assign the 1D double
arraydouble
array of length npublic static double[][] allocate2DArray(int m, int n, double v)
double
array and assign all
elements with a given value.m
- number of rowsn
- number of columnsv
- a real scalardouble
arraypublic static double[][] allocate2DArray(int m, int n)
double
array and assign all
elements with zero by default.m
- number of rowsn
- number of columnsdouble
arraypublic static int[] allocateIntegerVector(int n)
int
array.n
- number of elements to be allocatedint
array of length npublic static int[] allocateIntegerVector(int n, int v)
int
array.n
- number of elements to be allocatedv
- an integer to initialize the vectorint
array of length npublic static double[][] allocateMatrix(int nRows, int nCols)
double
array.nRows
- number of rowsnCols
- number of columnsdouble
arraypublic static void divideAssign(double[] V, double v)
V
- a 1D double
arrayv
- a real scalarpublic static void divideAssign(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static void divideAssign(double[][] res, double v)
res
- a 2D double
arrayv
- a real scalarpublic static void timesAssign(double[] V, double v)
V
- a 1D double
arrayv
- a real scalarpublic static void timesAssign(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static void timesAssign(double[][] res, double v)
res
- a 2D double
arrayv
- a real scalarpublic static double sum(double[] V)
double
array.V
- a 1D double
arraypublic static double mean(double[] V)
double
array.V
- a 1D double
arraypublic static double std(double[] V, int flag)
V
- a 1D double
array
flag 0: n - 1 in the divisor, 1: n in the divisorpublic static void sum2one(double[] V)
double
array to one, i.e., V[i] = V[i] / sum(V).V
- a 1D double
arraypublic static void plusAssign(double[] V, double v)
V
- a 1D double
arrayv
- a real scalarpublic static void plusAssign(double[] res, double a, double[] V)
res
- a 1D double
arraya
- a real scalarV
- a 1D double
arraypublic static void plusAssign(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static void plusAssign(double[][] res, double v)
res
- a 2D double
arrayv
- a real scalarpublic static void minusAssign(int[] V, int v)
V
- a 1D int
arrayv
- an integerpublic static void minusAssign(double[] V, double v)
V
- a 1D double
arrayv
- a real scalarpublic static void minusAssign(double[] res, double a, double[] V)
res
- a 1D double
arraya
- a real scalarV
- a 1D double
arraypublic static void minusAssign(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static void minusAssign(double[][] res, double v)
res
- a 2D double
arrayv
- a real scalarpublic static void assignVector(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static void assign(double[][] res, double[][] A)
double
array res by another
2D double
array A, i.e., res[i][j] = A[i][j].res
- a 2D double
arrayA
- a 2D double
arraypublic static void assign(double[][] res, double v)
double
array res by
a real scalar v.res
- a 2D double
arrayv
- a real scalarpublic static double[] operate(double[][] A, double[] V)
A
- a 2D double
arrayV
- a 1D double
arraypublic static void operate(double[] V1, double[][] A, double[] V2)
V1
- a 1D double
arrayA
- a 2D double
arrayV2
- a 1D double
arraypublic static double[] operate(double[] V, double[][] A)
V
- a 1D double
arrayA
- a 2D double
arraypublic static void operate(double[] V1, double[] V2, double[][] A)
V1
- a 1D double
arrayV2
- a 1D double
arrayA
- a 2D double
arraypublic static double innerProduct(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static double innerProduct(double[] V1, double[] V2, int from, int to)
V1
- a 1D double
arrayV2
- a 1D double
arrayfrom
- beginning index (inclusive)to
- ending index (exclusive)public static double[] times(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static double[] plus(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
arraypublic static double[] minus(double[] V1, double[] V2)
V1
- a 1D double
arrayV2
- a 1D double
array


Qantas challenges Airbus, Boeing to offer better deal on ultra-long haul jets
SYDNEY: Qantas has asked Airbus and Boeing for a better deal on planes capable of non-stop Sydney-London flights before deciding whether to place an order.
"We asked them to go back and re-look at that to sharpen their pencils because there still was a gap there," Qantas International Chief Executive Tino La Spina told an investor briefing on Tuesday (Nov 19) after Qantas outlined plans for capital spending to average A$2 billion (US$1.36 billion) a year.
The Australian airline is considering launching the world's longest non-stop flights because it sees demand for time-poor travellers willing to pay a premium to cut out stop-overs.
Qantas is looking to replace its ageing fleet over the medium term and said it expected capacity growth in the domestic market to be near-flat in the current half.
On its non-stop Perth-London route, it is achieving a 30 per cent fare premium in business and premium economy over one-stop flights from rivals, La Spina said.
"That surprised us, just how much people valued direct flying," he said.
In addition to Sydney-London flights, the fleet of Airbus A350-1000 or Boeing 777-8 jets under consideration could also fly Melbourne-London and from Sydney and Melbourne to New York and Chicago, La Spina said.
Qantas is examining an initial order for 12 planes, a source familiar with the matter, who was not authorised to speak with media, told Reuters.
La Spina and Chief Executive Alan Joyce declined to be drawn on exact numbers during the briefing.
"You have to have a big enough sized fleet to make the economics work," Joyce said.
Last week, following a London-Sydney test flight, he said Qantas would decide by the end of the year whether to proceed with plans for ultra-long haul flights and could place an order for planes early next year.
Citi analysts in July forecast non-stop flights from Sydney to London and New York could add A$180 million annually to the carrier's profit before tax, which was A$1.3 billion in the financial year ended Jun 30.


'FIFA 20' to Add CONMEBOL Libertadores Feature
The prestigious club tournament arrives early next year.
by Mallory ChinIn only a few months time, FIFA 20 fans will be able to experience a new feature for the game. EA Sports will be adding South America’s biggest club tournament CONMEBOL Libertadores as part of a free content update.
Slated to arrive in March, the update will also allow players to play CONMEBOL’s other South American club tournaments, the CONMEBOL Sudamericana and CONMEBOL Recopa. The update will include clubs from Uruguay, Perú, Paraguay, Ecuador, Brazil, Argentina and beyond such as River Plate, Boca Juniors, Flamengo, Corinthians, Racing, and Indepentiente U.
“Bringing two of the most prestigious continental football tournaments, Conmebol Libertadores and Conmebol Sudamericana, into FIFA 20 is an exciting step forward,” said Nick Wlodyka, GM, EA SPORTS FIFA, in a press release. “This new partnership with Conmebol bolsters our position as the deepest football experience available as we continue to build the most authentic sports video game for our fans.” Brent Koning, FIFA Competitive Gaming Commissioner added, “We aim to offer the most inclusive esports competition in the world and are eager to welcome South American-based players and aspiring superstars to join the tens of millions already competing in FIFA.”
According to EA Sports, the CONMEBOL Libertadores update will only be for PS4, Xbox One, and PC. Unfortunately Nintendo Switch users won’t be getting this update. Head over to ea.com for more details.
In related news, check out our review: “FIFA 20: Does the Juggernaut Retain Its Title?”


Associate Software Engineer, CGI Inc.
Bangalore, India
Kaustav is a highly effective and results-oriented Software Engineer offering wealth of more than 1.5 years of expertise and competencies in Java, Web Development and RPA. Kaustav is Certified as an Advanced RPA Developer. He is considered talented leader when guiding teams and performing tasks for Projects. Kaustav is a valuable team player with personable and dedicated nature and also skilled at technical knowledge & leadership, communication and presentations. Kaustav is experienced in full project life cycle from design to implementation to integration. He is a motivated and hardworking professional with an eye to learn new technologies.
*In Search of similar roles(Development) which will provide him a platform to learn and grow.
B.Tech - Computer Engineering CGPA: 8.08
Projects
Developed a Node.js website for finding weather for any location using DarkSky API and MapBox API.
Developed a gaming zone website so that users can search for particular games and also get the correct details.
In a team of 5 developed an online website for the carpooling system keeping in mind women security.Developed the front-end using HTML, CSS, Bootstrap & JavaScript & back-end using C#, SQL Server. Developed the google maps page using MapsAPI to give the correct distance & time.
Certifications
Skills
Robotics Process Automation (RPA)
Java
Front-end Development(HTML,CSS, JavaScript)
Back-End/Databases(PHP, MySQL)
Relevant Coursework
Achievements
Position Of Responsibilty
Languages
English
Hindi
Bengali
Address


SYMPTOMS OF DYSLEXIA
Difficulty in reading and memorizing
Mispronouncing names or words, or problems retrieving words
Problems processing and understanding what he or she hears


Do-It-Yourself: Make a Brandub Set
Making board games can be a fun, creative and sometimes frustrating adventure. There is something wonderful about playing a game with family and friends on something created by your own hands. This is the first in a series of Do-It-Yourself blog articles on how to do just that and some of the lessons learned in the process. In this first article, we will describe the beginnings steps in making a Basic Brandub set.
The Board
The foundation of every board game is…well…the board. Our Basic Brandub set uses an 8 inch square board. Your best bet is to go to a local lumber supplier to purchase some plywood and get it cut down to that size. There are also some online options. Woodworks Ltd. sells individual 8x8 squares that are 1/8 inch thick for relatively cheap. If you don’t mind spending a bit more, KenCraft Company has a great selection of plywood and if you email them they will cut it down to the size you want for a fee. Be warned you will be spending more on cutting fees and shipping than on the plywood itself.
Once you have the piece of plywood, sand it down to the desired smoothness. Don’t skimp too much on this step. The more time put into the sanding, the better the board will be. Mark out the grid using 1 inch squares and a 1/2 inch border. This will produce a 7x7 Brandub board where the gameplay happens on the squares (in oppose to the intersections).
Using a woodburning pen and a metal ruler, burn the lines in. Unfortunately, the metal will draw heat from the woodburning pen and cause the lines to be a bit inconsistent in darkness. Don’t dismay and just keep going. When all the lines are done, carefully go over the lines again without the ruler to fill in any gaps. Burn an ‘x’ into the corner and center squares. Coat the board with a layer of sanding sealant, let it dry (about 1/2 hour) and smooth with #0000 steel wool (super fine grade). Finally, finish with beeswax. It is also possible to skip the sanding sealant and just use beeswax. The surface won’t be as hard or smooth but the beeswax will produce a lovely finish.
The Pieces
Brandub requires 13 pieces: 8 attackers, 4 defenders and 1 king. This can be easily accomplished with different colored glass gems. These are commonly found in hobby stores for decorative purposes. Hobby Lobby is a great resource with a variety of colors and sizes. The standard size (around 18mm) will fit the 1 inch squares pretty nicely.
You now have a complete and playable Brandub set.
Stay tuned for the next article and upgrade your set by casting a king piece in resin.


Notebooks
Story: A Bushman belief about a moth called |goro, the coming of which to the fire at night foretells the slaying of an ostrich (or the finding of ostrich eggs) by one of the men
A Bushman belief about a moth called |goro, the coming of which to the fire at night foretells the slaying of an ostrich (or the finding of ostrich eggs) by one of the men
Title
A Bushman belief about a moth called |goro, the coming of which to the fire at night foretells the slaying of an ostrich (or the finding of ostrich eggs) by one of the men
Collection
Summary
Successful Ostrich hunting foretold by the coming of a moth (Celœna renisigma, Walker). In the Katkop dialect, by Díä!kwãin (L V.-18. 5390-5405). About the 'black ostrich's' moth, /goro, which foretells the killing of ostrich (or the finding of ostrich eggs) when it comes to burn in the people's fires at night.
Comments
1) p.5389v: this story was told to Dia!kwain by his mother (≠kamme-an); the finding of eggs is not part of the story, only the title; the Latin name for the |goro moth (Celoena renisigma?), 2) See also About a moth called by the Bushmen !num-!num and said to pour lice upon them, 3) This story is found in Book V-18
Contributors
Date
8 October 1875 (started)
Categories
Custom and daily life, Plants and animals, Portention
Keywords
insect (the |goro moth which foretells the killing of ostriches) , moth (|goro) , moth (Celœna renisigma, Walker) , moth ('black ostrich's') , moth (ostrich's) , moth (the habits and actions of) , moth (knows when meat is coming) , moth (knows things before people do) , moth (foretells the successful killing of ostrich) , moth (foretells the finding of food) , moth (follows the game) , moth (resembles the ostrich) , moth (its appearance) , moth (teachings about) , moth (burns in the fires) , hunting (predictions of successful) , hunting (and the |goro moth that foretells the killing of ostrich) , ostrich (and its moth, the |goro) , ostrich (predictions about finding) , ostrich (the |goro moth foretells the killing of) , ostrich (and finding its eggs) , ostrich (the hunting of) , game (its moths) , game (its actions) , game (predictions about finding) , game (moths that foretell the killing of) , game (things that know about) , game (things that follow) , food (the prediction of) , food (moths which foretell the killing of ostriches) , foretell (moths which foretell the killing of ostriches) , name (of a moth which foretells the killing of ostriches) , name (the |goro or ostrich's moth) , teaching (about the |goro or ostrich's moth which foretells the killing of ostriches) , education (the |goro or ostrich's moth foretells the killing of ostriches) , fire (the |goro or ostrich's moth burns in the people's, predicting the successful killing of ostrich or finding of eggs)
Story Pages
5390-5405


My role at Copirite as a web developer has seen me building highly customised websites and web applications using the Ruby on Rails framework. This included work on existing rails apps as well as new rails apps and provisioning of servers for those web applications.
As an addition to development with Ruby on Rails, I have been building websites and applications with technologies such as WordPress, Ember.js, Backbone, express and more
Working with the amazing design team, we have been able to produce websites of the highest quality, resulting in winning a Macca Media Award for the Oscar Oscar Salons website.
My time at iOnline was primarily spent building websites with their in house CMS iCMS.
Additional to my role of taking designs and building the front-end as well as some parts of the back-end for custom features, I lead development on two SaaS products, ElevationHQ a CRM/Project Management hybrid and XOcashflow, an outstanding invoice follow up system for Xero which was created as a partner add-on.
Further to my development role, I also brought Foundation, a front-end CSS Framework as well as version control with git into the workflow to normalize and speed up development with the developers.
Bringing Foundation into the mix as well as git allowed the team to build and maintain websites more efficiently as well as stopping from any feet being tread on when multiple team members were working on the same project.
Jaymie, over the time you've been here you have completely and utterly flipped my web dev world upside down. You are an absolute inspiration to me - Jordan Gardiner, web developer
You've taught me so many things that I now think of as essential knowledge in the business (git, etc) - Bentley O'Kane Chase, web developer
Thanks for all of the knowledge and passion you brought to the team - Joshua Smith, Director
During my time at Xtreme I was first introduced to Drupal in the very early 5.x versions of the Framework.
Designing and developing Drupal websites was my primary job but I also got to design and develop for other various platforms such as Magento as well as other custom and pre-built systems.
Many of the websites consisted of industry leading holiday parks for families, but also for other various industries within Queensland as well as some other states.
To add to my roles I managed the hosting and billing side of hosting services which also included the management of DNS for those properties using the service.
My role at Web Circle revolved around setting up and making modifications through plugins and customising plugins to Joomla based websites.
Further to this, simple style changes and the building of accompanying SEO reports.


Penny for your thoughts? How about a stack of cash for your best idea?
iNet and Awesome Inc are teaming up to create the largest student-only business pitch competition ever at the University of Kentucky. Participation is ridiculously easy. Use your computer or pokédex to record your pitch, upload it to Youtube, share the link with us via the pitch application on this site, and you could win $1000 in prize money!
Rules
- Pitches should be no more than 150 seconds (2:30 minutes). We will stop watching/caring at the 2:30 minute mark, so keep it simple and to the point.
- Pitches must be given by a current student enrolled at the University of Kentucky. Both full time and part time students are eligible.
- Group pitches are allowed as long as all participants are students enrolled at UK.
- All pitches should be submitted by December 12
- See the Help page for Pitch Tips.


https://i.redd.it/ok2bmawsihe11.png | Original frame is from The Big Bang Theory sitcom.
PyTest, Unit testing, Code tests...
what it is all about?
Murphy's Law:
"Anything that can go wrong will go wrong"
Statement:
"Programmers produce bugs"
Conclusion:
Something somewhere will go wrong when you write a code ¯\_(ツ)_/¯
and tests help minimise the size of the disaster
The documentation can be found on docs.pytest.org
enough of theory, time fortests and code
Test Driven Development - a software development technique classified as agile methodologies. Programming with this technique is distinguished by the fact that the programmer begins by writing tests to a function that has not yet been written then implements the function.
Questions?


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class |
MultiArrayType
Represents an array of MultiType instances. |
class |
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MultiType represents an unresolved type. |
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Type.BOGUS
Represents a non-accessible value. |
static Type |
Type.BOOLEAN
Represents the boolean primitive type |
static Type |
Type.BYTE
Represents the byte primitive type |
static Type |
Type.CHAR
Represents the char primitive type |
static Type |
Type.CLONEABLE
Represents the java.lang.Coneable reference type |
static Type |
Type.DOUBLE
Represents the double primitive type |
static Type |
Type.FLOAT
Represents the float primitive type |
static Type |
Type.INTEGER
Represents the integer primitive type |
static Type |
Type.LONG
Represents the long primitive type |
static Type |
Type.OBJECT
Represents the java.lang.Object reference type |
static Type |
Type.RETURN_ADDRESS
Represents an internal JVM return address, which is used by the RET instruction to return to a JSR that invoked the subroutine. |
static Type |
Type.SERIALIZABLE
Represents the java.io.Serializable reference type |
static Type |
Type.SHORT
Represents the short primitive type |
static Type |
Type.THROWABLE
Represents the java.lang.Throwable reference type |
static Type |
Type.TOP
A placeholder used by the analyzer for the second word position of a double-word type |
static Type |
Type.UNINIT
Represents an unknown, or null type. |
static Type |
Type.VOID
Represents the void primitive type |
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Type.get(CtClass clazz)
Obtain the Type for a given class. |
Type |
Type.getComponent()
Returns the array component if this type is an array. |
Type |
MultiType.getComponent()
Always returns null since this type is never used for an array. |
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MultiArrayType.getComponent()
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Returns the local varaible table entry at index. |
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Frame.getStack(int index)
Returns the type on the stack at the specified index. |
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Type.merge(Type type)
Finds the common base type, or interface which both this and the specified type can be assigned. |
Type |
MultiType.merge(Type type)
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Frame.peek()
Gets the top of the stack without altering it |
Type |
Frame.pop()
Alters the stack to contain one less element and return it. |
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Type.isAssignableFrom(Type type)
Determines whether this type is assignable, to the passed type. |
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Frame.push(Type type)
Alters the stack by placing the passed type on the top |
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Type type)
Sets the local variable table entry at index to a type. |
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Frame.setStack(int index,
Type type)
Sets the type of the stack position |
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Here you can import data from other sources. Two formats are currently supported, HTTP Archive (HAR) and Postman json.
HAR is a format that can be exported directly from your favourite browser's network tools. This means you can do a request you would like to recreate or alter at a later date, and import it to your collected requests. See this link for details.
An option to import data from Postman (Chrome extension)
is also provided. In order to fetch a collection from
postman, click the three dots next to a postman collection,
click download
, and copy the file's contents into
the field below.


Events UK downhill series returns for 2019 with four rounds in the North East and Wales.
Find Out More
The original and classic British Mountain Bike Event, now in its 5th year.
Promoted & organised by FreeRiders Cycling Club.
Our event is aimed at experienced riders – routes of 100km, 75km or 50km and run over challenging routes
across the hills, using a combination of roads, tracks and bridleways, with significant off-road sections.
Our Prize Draw aims to encourage young riders to enter and we have again teamed up with the great guys.
Professionals will test your bike and give advice on what needs to be improved.
A medal will be awarded to all finishers.
There are a lot of special tracks, qualified staffs, relaxing rooms and mobile-cafes.
Check our website for more recent details and contacts when it appears. We work hard to make it all on time. Good luck and see you at our event!
+44 20 7946 0182
Coming Soon


Overall Goal
Determine whether the intermediate representations of convolutional neural networks identify molecular motifs whose importance was not previously appreciated.
Background.
(Hirohara et al., 2018) demonstrated that a convolutional neural network trained on the SMILES representation of a molecule
- could classify that molecule as toxic or not
- developed a latent representation (“fingerprint”) of toxicity
Approach
- Specific Aim 1: Extract Names, 2-Dimensional Structures, and Effects from Social Media of Novel Psychoactive Substances
- Specific Aim 2: Train a Convoluational Neural Network to Classify Substancts as Belonging to 1 of 5 Classes of Novel Psychoactive Substances
- Specific AIm 3: Assess how (2) classifies and represents (1)
Specific Aim 1
- We developed a custom named entity recognizer using the Python plugin SpaCy. How train, test, validate it?
Specific Aim 2
- We extended Hirohara et al. How test and validate it? How introspect latent representations?
To train the CNN, I developed a library of chemical substances (Extended Discussion)
Creation of Library
Specific Aim 3
- We did this. How validate it?
Bibliography
- 1.Hirohara, M., Saito, Y., Koda, Y., Sato, K. & Sakakibara, Y. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC bioinformatics 19, 83–94 (2018).


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Value statement that explains the site in a short sentence
One
Two
Three
Free
Basic
Pro


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The Varnish Developers Guide
This is the deliberately short and to the point list of things
Varnish Developers should know.
Behaviour
- Be sensible.
- If in doubt, think.
- If still in doubt, ask.
- Admit your mistakes, it's faster that way.
- Thou SHALL not paint bikesheds.
- We will toss you out of the project rather than add another rule.
Technical stuff
- Our coding style guideline is FreeBSD's
style(9)
- See autogen.des script for developer options to the toolchain.
- We always -Werror, there are no harmless warnings, only source code
that does not express intent well enough.
- We prefer the source code, rather than the comments explain what is
going on, that way tools like FlexeLint and Coverity also gets a chance.
- Our reference platforms are Ubuntu and FreeBSD.
- Asserts have negative cost, they save developer time next time around.
- Our license is BSD 2-clause or looser, no GPL or LGPL.
- We havn't had a major security issue in 10 years, and we're not going
to start having them now.
Bugs, issues, feature requests & VIPs
Bugs, issues and feature requests start out as github issues.
We do a "bug-wash" every monday at 13:00 EU time, where new and otherwise
worthy of discussion issues are pow-wowed.
Tickets we cannot do anything about are closed.
If feature-requests make sense, they get moved to a wiki/VIP page until
somebody implements them.
Varnishtest cases for bugs is the norm, not the exception.
Architectural stuff
These rules are imported from the X11 project:
- It is as important to decide what a system is not as to decide what it is.
- Do not serve all the world's needs; rather, make the system extensible so
that additional needs can be met in an upwardly compatible fashion.
- The only thing worse than generalizing from one example is generalizing
from no examples at all.
- If a problem is not completely understood, it is probably best to provide
no solution at all.
- If you can get 90 percent of the desired effect for 10 percent of the work,
use the simpler solution.
- Isolate complexity as much as possible.
- Provide mechanism, rather than policy.


Jon SanPedro
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Analog class activities. The 3 main activities involve dice, poker chips and wooden fish, to explore different types of sampling, sampling distributions, the distinction between sample and population, resampling…
library(tibble)
library(ggplot2)
We use dice to simulate sampling with replacement from an infinite population of participants and trials.
This exercise provides an opportunity to learn about:
- the distinction between population and sample;
- sampling with replacement;
- hierarchical sampling;
- running simulations;
- estimation;
- the distinction between finite and infinite populations.
Material:
- 3 bags of dice
- 3 trays (optional)
Each bag contains a selection of dice with 4 to 20 facets, forming 3 independent populations. In this activity, we sample from each population and then use the samples to try to make inferences about the populations.
Here is the setup we will use in class:
These values are then entered into a text file and shared with the rest of the class. The text files are opened in R, and the main question is: is there evidence that our 3 samples of 10 participants x 5 trials were drawn from different populations? To simplify the problem, in a first step we average over trials, so we are left with 10 values from each group. The second step is to produce some graphical representation of the data. Then we can try various inferential statistics.
The exercise is performed in classes 1-4, to see how much variability we get between simulated experiments. During the last class, the populations and the sampling distributions are revealed.
Also, in this exercise, because the dice are sampled with replacement, the population has an infinite size. The content of each bag defines the probability of sampling each type of dice, but it is not the entire population, unlike in the faux fish activity (see below).
Enter the data in R or a text file: 5 rows (trials) and 10 columns (participants).
# enter data in matrix with 5 rows, 10 columns:
dice_res <- matrix(NA, nrow = 5, ncol = 10)
dice_res[,1] <- c(,,,,)
dice_res[,2] <- c(,,,,)
dice_res[,3] <- c(,,,,)
dice_res[,4] <- c(,,,,)
dice_res[,5] <- c(,,,,)
dice_res[,6] <- c(,,,,)
dice_res[,7] <- c(,,,,)
dice_res[,8] <- c(,,,,)
dice_res[,9] <- c(,,,,)
dice_res[,10] <- c(,,,,)
# Save in a txt file:
write.table(dice_res, file = "./data/dice_res_blue.txt", row.names=FALSE, col.names=FALSE)
write.table(dice_res, file = "./data/dice_res_green.txt", row.names=FALSE, col.names=FALSE)
write.table(dice_res, file = "./data/dice_res_red.txt", row.names=FALSE, col.names=FALSE)
Ignore this chunk if you have class data; use the next chunk instead. These examples were created before class to demonstrate what the results could look like and to provide an example of graphical representation (see below “Illustration of example data”).
nt <- 5 # number of trials
np <- 10 # number of participants
res_red <- matrix(scan("./data/res1.txt"), nrow=nt, byrow=TRUE)
res_green <- matrix(scan("./data/res2.txt"), nrow=nt, byrow=TRUE)
res_blue <- matrix(scan("./data/res3.txt"), nrow=nt, byrow=TRUE)
res_red <- matrix(scan("./data/dice_res_red.txt"), nrow=nt, byrow=TRUE)
res_green <- matrix(scan("./data/dice_res_green.txt"), nrow=nt, byrow=TRUE)
res_blue <- matrix(scan("./data/dice_res_blue.txt"), nrow=nt, byrow=TRUE)
For each bag, we compute the mean across trials/throws for each participant/dice:
m.red <- apply(res_red, 2, mean)
m.green <- apply(res_green, 2, mean)
m.blue <- apply(res_blue, 2, mean)
foo <- median # name of summary statistic
set.seed(1) # reproducible scatterplot
# make data frame
df <- tibble(obs = c(m.red, m.green, m.blue),
gp = factor(c(rep("Red",np),rep("Green",np),rep("Blue",np)))
)
# preserve factor order
df$gp <- as.character(df$gp)
df$gp <- factor(df$gp, levels=unique(df$gp))
ggplot(df, aes(gp, obs)) + theme_linedraw() +
geom_point(aes(colour = gp), size = 3,
position = position_jitter(width = 0.1)) +
scale_color_manual(values=c("red", "green4", "blue")) +
theme(legend.position = "none",
axis.text = element_text(size = 14),
axis.title = element_text(size = 16)) +
labs(x = "Groups", y = "Observations") +
coord_cartesian(ylim = c(0, 12)) +
scale_y_continuous(breaks = seq(0, 12, 2)) +
geom_errorbar(stat = "summary",
fun.y = foo, fun.ymin = foo, fun.ymax = foo,
colour = "grey", width = .55, size = 2, alpha = 0.5) +
geom_errorbar(stat = "summary",
fun.y = foo, fun.ymin = foo, fun.ymax = foo,
colour = "black", width = .5, size = 0.75, alpha = 0.5)
We use poker chips to demonstrate sampling with replacement, as done in the bootstrap.
This exercise provides an opportunity to learn about:
- sampling with replacement;
- bootstrap sampling;
- running simulations.
A bag contains 8 poker chips, representing the outcome of an experiment. Each chip is like an observation.
First, we demonstrate sampling with replacement by getting a random chip from the bag, writing down its value, and replacing the chip in the bag. Second, we demonstrate bootstrap sampling by performing sampling with replacement 8 times, each time writing down the value of the random chip selected from the bag. In R, sampling with replacement is done using the sample
function.
The core mechanism of the bootstrap is sampling with replacement, which is equivalent to simulating experiments using only the data at hand. Let’s say we have a sample that is a sequence of integers, from 1 to 8.
n <- 8 # sample size
samp <- 1:n
samp
## [1] 1 2 3 4 5 6 7 8
To make bootstrap inferences, we sample with replacement from that sequence using the sample()
function. That’s the engine under the hood of any bootstrap technique. Let’s generate our first bootstrap sample:
set.seed(21) # for reproducible results
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 7 3 6 2 8 8 1 2
This call to the sample
function is equivalent to getting a random poker chip from the bag, writing down the number, replacing the chip in the bag, and starting the process again, for a total of n times. The equivalent of selecting only one poker chip from the bag is to call sample(samp, size = 1)
, but that’s unnecessarily time consuming.
We do it again, getting a different bootstrap sample:
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 8 7 6 8 1 5 2 1
Third time:
sample(samp, size = n, replace = TRUE) # sample with replacement
## [1] 5 3 4 6 1 5 7 8
We could also generate our 3 bootstrap samples in one go:
set.seed(21) # reproducible example
nboot <- 3
matrix(sample(samp, size = n*nboot, replace = TRUE), nrow = nboot, byrow = TRUE)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 7 3 6 2 8 8 1 2
## [2,] 8 7 6 8 1 5 2 1
## [3,] 5 3 4 6 1 5 7 8
As is apparent from these 3 examples, in a bootstrap sample, some observations are sampled more than once and others are not sampled at all. So each bootstrap sample is like a virtual experiment in which we draw random observations from our original sample.
We sample with replacement from a finite population of faux fish to demonstrate the effect of sample size on the shape of sampling distributions.
The faux fish activity is mentioned in Steel, Liermann & Guttorp (2019), with pictures of class results. The activity is described in detail in Kelsey & Steel (2001).
This exercise provides an opportunity to learn about:
- the distinction between population and sample;
- sampling with replacement;
- running simulations;
- estimation;
- sampling distributions.
Material:
- two sets of 97 faux fish = fish-shaped bits of paper or other material
- two containers = ponds
- two large blank sheets of paper
- x axis = ‘Mean weight (g)’
- y axis = ‘Number of experiments’
- titles = ‘n=3 replicates’ / ‘n=10 replicates’
Here is the code to generate empty axes for printing:
df <- tibble()
p <- ggplot(df) + geom_point() + theme_linedraw() +
labs(x = 'Mean weight (g)', y = 'Number of experiments') +
theme(title = element_text(size = 20),
axis.title = element_text(size = 18),
axis.text = element_text(size = 16)) +
scale_x_continuous(minor_breaks = seq(17, 40), limits = c(17, 40)) +
scale_y_continuous(minor_breaks = seq(1, 30), limits = c(0, 30)) +
ggtitle('n=3 replicates')
p
ggsave(file="template_n3.pdf", width = 420, height = 297, units = "mm")
p <- p + ggtitle('n=10 replicates')
p
ggsave(file="template_n10.pdf", width = 420, height = 297, units = "mm")
Each faux fish has a weight in grams written on it.
The frequencies of the weights is given in Kelsey & Steel (2001).
The fish population is stored in a box. Two identical populations are available, so that two groups can work in parallel.
The first goal of the exercise is to produce sampling distributions by sampling with replacement from a population. The second goal is to evaluate the effect of the sample size on the shape of the sampling distribution. The third goal is to experiment with a digital version of the analog task, to gain familiarity with simulations.
Unlike the dice activity, this activity involves a finite size population: each box contains the full population under study.
Setup:
- two groups of participants;
- each group is assigned a box;
- participants from each group take turn sampling from the box n=3 or n=10 faux fish (depending on the group), without looking inside the box;
- each participant averages the numbers, writes down the answer and marks it on the large sheet of paper assigned to each group;
- this is repeated until a sufficient number of simulated experiments have been carried out to assess the shape of the resulting sampling distribution.
To speed up the exercise, a participant picks n fish, writes down the weights, puts all the fish back in the box, and passes the box to the next participant. While the next participant is sampling fish from the box, the previous participant computes the mean and marks the result on the group graph.
Once done, we discuss the results by comparing the sampling distributions.
We can do the same in R, but much quicker!
ffpop <- c(17, 17, 18, 19, 19, 21, 21, 22, 22, 22, 22, 23, 23, 23, 24, 24, 24, 24,
25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26,
27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29,
30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31,
32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34,
35, 35, 35, 36, 36, 36, 36, 36, 36, 37, 37, 38, 39, 40)
set.seed(21) # reproducible example - change the seed to get a different random distribution
n <- 3 # sample size
nexp <- 100 # number of experiments
ffsp <- matrix(NA, nrow = nexp, ncol = n) # declare matrix of results
# sampling distribution
for(E in 1:nexp){ ffsp[E,] <- sample(ffpop, n, replace = FALSE) }
ffsp <- apply(ffsp, 1, mean)
df <- tibble(x = ffsp) # make data frame
ggplot(df, aes(x = x)) + theme_bw() +
# superimpose population mean
geom_vline(xintercept = mean(ffpop)) +
geom_dotplot(binwidth = 1, fill = "white",
dotsize = 1, method = "histodot") +
labs(x = 'Mean weight (g)', y = 'Number of experiments') +
theme(title = element_text(size = 20),
axis.title = element_text(size = 18),
axis.text = element_text(size = 16)) +
scale_x_continuous(minor_breaks = seq(17, 40), limits = c(17, 40)) +
scale_y_continuous(minor_breaks = seq(1, 30), limits = c(0, 30)) +
coord_fixed(ratio = 1) +
ggtitle(paste0('n=',n,' replicates'))
# save figure
# ggsave(file=paste0('simres_n',n,'.pdf'), width = 6, height = 6)
Kelsey, Kathryn, and Ashley Steel. The Truth about Science: A Curriculum for Developing Young Scientists. NSTA Press, 2001.
Steel, E. Ashley, Martin Liermann, and Peter Guttorp. Beyond Calculations: A Course in Statistical Thinking. The American Statistician 73, no. sup1 (29 March 2019): 392–401. https://doi.org/10.1080/00031305.2018.1505657.
Guillaume A. Rousselet, 2019, University of Glasgow.


These are some of the projects I've been a part of, or I've personally built.
Created a Todo App with Crud functionalities. This application will create a new todo item, edit the chore, cross it out, and delete the item.
We created an app that will essentially, from a medical view, merge a patient portal with a practice' EMR across a group of doctors for the purpose of streamlining the data of patients who need to see multiple providers. With this app we can hopefully minimize the effects of several doctors prescribing too much or conflicting medications to a single patient. Also patient data can be streamlined across the board.


Honk is designed to replace native javascript alert and confirm dialogs. Callbacks are supported on both alerts and confirms. jQuery is required. SaSS files are included and utilize Bourbon.io, but the compiled css file could be used as well. Honk is released under the MIT license.
honk.alert('Your message goes here!', function(){ //Alerts & Confirms support callbacks if needed. });
honk.confirm('Your message goes here!', function(){ //Alerts & Confirms support callbacks. });


Survey - 3D Point Cloud Fusion
-
Tag:
- survey
3D Point Cloud Fusion
The essential key to 3D point cloud fusion is to find 3D point correspondences, based on which similarity transformation can be estimated to roughly align point clouds and then adopt algorithms like ICP to register them accurately. To robustly determine the 3D point correspondences between noisy point clouds, expressive descpritors are very important. There are two main types of 3D feature descriptors to date:
- Multiview texture based descriptor [1].
-
Local geometry based descriptor. 3D geometry can be described in terms of different native 3D formats, including voxel grid and polygon mesh. This method is more suitable for dense and watertight point clouds.
2.1 Voxel grid. Use binary voxel representation, e.g., 3D ShapeNets [5].
2.2 Polygon mesh. Use norm (1st order), surface curvature (2nd order), etc, e.g., [2].
2.3 Combination of both above.
Multi-View Fusion
- View pooling [1]: The variant local structures are overlayed.
Reference
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Aligning Point Cloud Views using Persistent Feature Histograms
- Comparison of 3D interest point detectors and descriptors for point cloud fusion
- Multi-View 3D Object Detection Network for Autonomous Driving
- 3D ShapeNets: A Deep Representation for Volumetric Shapes
- Location Recognition Using Prioritized Feature Matching


We seek to tell our story through the food we serve
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Makes Stubby RPC calls to a SigningService server.
| def google.appengine.api.app_identity.app_identity_service_pb._SigningService_ClientBaseStub.GetAccessToken |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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Make a GetAccessToken RPC call.
Args:
request: a GetAccessTokenRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The GetAccessTokenResponse if callback is None. Otherwise, returns None.
| def google.appengine.api.app_identity.app_identity_service_pb._SigningService_ClientBaseStub.GetDefaultGcsBucketName |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a GetDefaultGcsBucketName RPC call.
Args:
request: a GetDefaultGcsBucketNameRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The GetDefaultGcsBucketNameResponse if callback is None. Otherwise, returns None.
| def google.appengine.api.app_identity.app_identity_service_pb._SigningService_ClientBaseStub.GetPublicCertificatesForApp |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a GetPublicCertificatesForApp RPC call.
Args:
request: a GetPublicCertificateForAppRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The GetPublicCertificateForAppResponse if callback is None. Otherwise, returns None.
| def google.appengine.api.app_identity.app_identity_service_pb._SigningService_ClientBaseStub.GetServiceAccountName |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a GetServiceAccountName RPC call.
Args:
request: a GetServiceAccountNameRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The GetServiceAccountNameResponse if callback is None. Otherwise, returns None.
| def google.appengine.api.app_identity.app_identity_service_pb._SigningService_ClientBaseStub.SignForApp |
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self, |
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request, |
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rpc = None , |
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callback = None , |
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response = None |
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) |
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Make a SignForApp RPC call.
Args:
request: a SignForAppRequest instance.
rpc: Optional RPC instance to use for the call.
callback: Optional final callback. Will be called as
callback(rpc, result) when the rpc completes. If None, the
call is synchronous.
response: Optional ProtocolMessage to be filled in with response.
Returns:
The SignForAppResponse if callback is None. Otherwise, returns None.
The documentation for this class was generated from the following file:
- code/googleappengine-read-only/python/google/appengine/api/app_identity/app_identity_service_pb.py