Heading 1
Heading 2
This is a paragraph.
This is another paragraph.
This is some important text!
Add classes to elements
Remove class from elements
Toggle class
This is a heading
This is a paragraph.
This is a paragraph.
This is a paragraph.
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Set background-color of p
Set background-color of p and incr font


TL;DR: Working with db transactions in Go should be simple, so I made some utilities to help with that. You can see the gist containing all the code here.
The database/sql
package has everything you need in order to work with db
transactions for most vendors. Typically, you’ll write something like this:
While this code works, there are a few of things I think could be better about it.
Every transaction has to worry about the details
The flexibility of handling everything yourself can be nice, but its also a burden sometimes. For example, if one statement fails, the next move is to rollback, almost always.
Similarly, I would be worried about forgetting to commit the transaction at some point (wouldn’t be the first time).
This kind of code seems like it should be a primitive in your codebase
When you deal with common error scenarios (timeouts, retries, etc.) it’s generally a good practice to introduce some primitives in your codebase to handle these.
For example, a function like FetchJSON(url string, params ...interface{}) error
can be used to set open/request
timeouts and handle any application specific errors in the same way everywhere. Then you just call this method whenever
you need to fetch some JSON.
Managing db transactions seems to fit this case well.
Not all “errors” are covered
In the example above, what happens if one of the calls panics? This might not be a problem, but depending on the
IsolationLevel
for the transaction, failure to explictly rollback could be an issue.
A better solution would recover from the panic, rollback, and then re-panic.
The first thing we’ll want to do is create a func
to wrap the transaction logic so we don’t need to do this
everywhere. There are several ways to do this, but I generally use something like this.
Transaction Helper
Updated code for main
This is better (IMO), but as we copy/paste this around the codebase, there’s an aweful lot of repetition still happening.
Ideally, we’d be able to supply a set of commands to be run, and have them all take place in the same transaction.
We can create the concept of a pipeline here that will run statements one by one within a transaction and chain the last insert id along with each call.
Updated code for main
This seems much easier to read, and is certainly simple to replicate across your application. Obviously, this doesn’t cover every possible scenario, but putting something like this in place can really make your application’s code nicer to read and ensure that you’re handling error cases consistently.
I’m definitely interested in opinions, so please leave a comment if you’ve got one!


Ex-Golden Knight James Neal enjoys resurgence with Oilers
by Ben GotzJames Neal admitted he still loves feeling a plane’s wheels hit the ground in Las Vegas, a place that always will be special to him.
The former Golden Knight loves his game these days, too. Neal, one of the Knights’ leaders during their magical inaugural season, is experiencing a resurgence in his first year with the Edmonton Oilers.
He struggled last season after signing a five-year contract with the Calgary Flames. Now he’s back to his old self, and he and the rest of the Pacific Division-leading Oilers (14-7-3) should present a stiff test for the Knights (11-9-4) at 7 p.m. Saturday at T-Mobile Arena.
“I think just having a great summer and coming in with a different mindset (helped),” said Neal, who has 16 points (13 goals, three assists).  Great players. I’ve been able to find the back of the net early.”
Neal, 32, could be forgiven for having a somber mindset this offseason. The right wing, who had at least 20 goals in each of his previous 10 seasons, scored seven in 63 games with Calgary. He received the lowest average ice time (14:53) of his career, and the Flames clearly weren’t happy with him or his $28.75 million contract.
So Neal dedicated the summer to living up to that price tag. He had his first full offseason in three years after playing in the 2017 and 2018 Stanley Cup Final with the Nashville Predators and Knights and took advantage.
“He had a great summer working out,” said Knights coach Gerard Gallant, who ran into Neal on Prince Edward Island in Canada during the offseason. “He said he was working his tail off.”
Neal also was working hard because he was traded to the Oilers on July 19 for physical left wing Milan Lucic. Suddenly, he had to prove himself to a fourth team in four years. He’s done that, earning minutes on the second line and the first power-play unit.
The latter gives him the chance to work with superstars Connor McDavid and Leon Draisaitl, the NHL’s two leading scorers with 44 points apiece.
“It’s unbelievable,” Neal said. “They’re so good. Their ability to move pucks and draw guys to them and find the open man is the best in the league. They’re special players. When they get going, there’s no one that can stop them.”
That open man often is Neal.
As a result, eight of his goals have come on the power play. McDavid has an assist on seven of them and Draisaitl on three. The three have helped produce the NHL’s top-ranked power play at 31.4 percent.
“You try to get them the puck as much as possible and get free,” Neal said. “They do a great job of finding guys.”
Neal’s success has caught the attention of his former Knights teammates, who are happy for their ex-assistant captain. The Neal who played at T-Mobile Arena last season was struggling. The one who returns Saturday is back to his old tricks.
“I’ll be super happy to see Jimmy Neal tomorrow,” left wing Jonathan Marchessault said. “Obviously, he’s doing good. That’s what we always wish when (players) go somewhere else. We want them to do good, just not against us.”
More Golden Knights: Follow at reviewjournal.com/GoldenKnights and @HockeyinVegas on Twitter.
Game day
Who: Oilers at Golden Knights
When: 7 p.m. Saturday
Where: T-Mobile Arena
TV: AT&T SportsNet (Cox 313/1313, DirecTV 684, CenturyLink 760/1760, U-verse 757/1757, Dish 414/5414)
Radio: KRLV (98.9 FM, 1340 AM)
Line: Knights -170; total 6½ u-115
–––
Notable former Knights
James Neal, RW, Oilers
Has 16 points (13 goals, three assists) in his first season in Edmonton. He had 19 points in Calgary last season.
David Perron, RW, Blues
The leading scorer for the reigning Stanley Cup champions with 21 points (10 goals, 11 assists).
Erik Haula, C, Hurricanes
Has 11 points (eight goals, three assists), but injuries have limited him to 16 games. He’s out indefinitely with a knee injury.
Pierre-Edouard Bellemare, C, Avalanche
Has eight points (four goals, four assists). He had 15 last season.
Colin Miller, D, Sabres
The offensive-minded defenseman has struggled with just four assists. He’s been scratched in four of the Sabres’ last five games.
Ryan Carpenter, LW, Blackhawks
Has six assists and is a lineup regular as a fourth-line player.
Tomas Tatar, LW, Canadiens
The Canadiens’ leading scorer with 20 points (seven goals, 13 assists).
Nick Suzuki, C, Canadiens
The rookie has 10 points (six goals, four assists).
Erik Brannstrom, D, Senators
Unexceptional rookie season with two assists and a minus-4 plus-minus.


Hi there! We’re opening up a very limited beta playtest for Neocense to get some feedback before we release. If you’re interested in getting one of our keys to our playtest event please register below: Thanks you so much! The Neocense team Registration


--- title: Open GoPro layout: splash header: overlay_color: '#000' overlay_filter: '0.5' overlay_image: /assets/images/splash.jpg actions: - label: 'View on Github' url: 'https://github.com/gopro/OpenGoPro' excerpt: 'An open source interface specification to communicate with a GoPro camera with accompanying demos and tutorials.' read_time: false classes: main__splash ---
The Open GoPro API is the primary way for users to interact with a GoPro camera. The camera provides interfaces to WiFi and Bluetooth Low Energy that allow users to perform command, control, and query actions including, but not limited to:
Detailed Bluetooth Low Energy (BLE) and WiFi Interface Specifications.
BLE Specs →Walk-through tutorials in different languages / frameworks for getting started.
✏ Tutorials →


Sixteen Summers
Issue 4 / July 2016 / 14 Articles


An individual's unique assemblage of psychological traits over time
Just like intelligence, there is no consensus
Personality is defined by the particular empiricial concepts which are a part of the theory of personality employed by the observer
Refers to a short-term, ephemeral trait
How do you feel during a test? At the doctor's?
Often the person supplies information about their personality (self-reporting)
May be from diaries or an interview and can elicit very private information
Self-report measures self-concept (e.g. Beck Self-Concept Test)
What are some measurement issues with this?
Lots of measurement issues though!!!
Biased leading to be too generous or severe
May want to rate person as "normal", i.e. in the middle
Something may overshadow (the halo effect)
Context important
Other problems?
May measure thoughts, feelings, behaviors
Can also measure response style (characteristic response pattern independent of the content)
Are they being honest? Are they responding in a socially desirable way? These are validity issues Attempts to measure this on a test - validity scale
Myriad of formats used: traditional test, interviews, performance tasks, etc
They may be structured or unstructured
Most important is a clear definition of the construct of interest!
Now, in the past, in the future
How I see myself, how others see me, etc
Q-sort technique, ranking of variables based on some instruction
"Most how I feel about this course" to "Least how I feel about this course"
Based on Q-factor analysis
Adjective checklist and sentence completion
Range from simple summing of responses to complex algorithms requiring experts
Depends on test makers approach to personality
Is it a nomothetic vs. idiographic approach?
Is scoring normative or ipsative?


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device_reply_server
Function - Handle incoming data from kernel device driver.
SYNOPSIS
boolean_t device_reply_server (mach_msg_header_t request_msg, mach_msg_header_t reply_msg);
PARAMETERS
- request_msg
-
[pointer to in structure]
The device driver message received from the
kernel.
- reply_msg
- [out structure] A reply message. No messages from a device driver expect a direct reply, so this field is not used.
DESCRIPTION
The device_reply_server function is the MIG generated server handling function to handle messages from kernel device drivers. Such messages were sent in response to the various device_..._request... calls. It is assumed when using those calls that some task is listening for reply messages on the port named as a reply port to those calls. The device_reply_server function performs all necessary argument handling for a kernel message and calls one of the device server functions to interpret the message.
RETURN VALUES
- TRUE
-
The message was handled and the appropriate function was called.
- FALSE
- The message did not apply to this device handler interface and no other action was taken.
RELATED INFORMATION
Functions: ds_device_open_reply, ds_device_write_reply, ds_device_write_reply_inband, ds_device_read_reply, ds_device_read_reply_inband, ds_device_read_reply_overwrite.


:: FRA
France has been represented at every Olympic Winter Games and hosted the event on three occasions, including the first Games in Chamonix in 1924.


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Demo Date Time Picker
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Find user guide and learn how to customise your date time picker at rainforestNET
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Sensor unit
Sensor measurements quantity
Sensor active
Sensor max value threshold
Sensor min value threshold


A LogEvent
is a single unit of data that should be logged. LogEvents
are
usually created by a Logger, and then processed by Appenders.
x <- LogEvent$new(logger, level = 400, timestamp = Sys.time(), caller = NA, msg = NA, ...) x$clone(deep = FALSE) x$.logger x$caller x$level x$level_name x$logger x$msg x$timestamp x$values
The arguments to LogEvent$new()
directly translate to the fields stored in
the LogEvent:
level
integer
: the log_level / priority of the LogEvent
timestamp
POSIXct
the time when then the
LogEvent was created
caller
character
. The name of the calling function
msg
character
. A message
logger
character
scalar. Name of the Logger that created the
event (.logger$full_name
)
user
character
scalar. User as set for the Logger
that created this event (.logger$user
)
.logger
a Logger
. A reference to the Logger that created the
event
...
All named arguments in ...
will be added to the LogEvent
as custom fields. You can store arbitrary R objects in LogEvents
this way, but not all Appenders will support them.
See AppenderJson for
an Appender that supports custom fields quite naturally.
Usually the above values will be scalars, but (except for "logger"
) they
can also be vectors if they are all of the same length (or scalars that will
be recycled). In this case the event will be treated by the Appenders and
Layouts as if several separate events.
LogEvents contain some some active bindings that make it easier to retrieve commonly used values.
level_name
character
: the log_level / priority of the
LogEvent labelled according to getOption("lgr.log_levels")
values
list
: All values stored in the LogEvent (including
all custom fields, but not including event$logger
)
logger_name
character
scalar: The name of the Logger that
created this event, equivalent to event$logger$name
)
logger_user
character
scalar: The user of the Logger that
created this event, equivalent to event$logger_user
)
l <- Logger$new("dummy logger", appenders = NULL) l$error("foo bar")#> ERROR [12:43:08.616] foo bar# The last LogEvent produced by a Logger is stored in the last_event field l$last_event # formatted by default#> ERROR [2019-03-25 12:43:08] foo barl$last_event$values # values stored in the event#> $level #> [1] 200 #> #> $timestamp #> [1] "2019-03-25 12:43:08 CET" #> #> $logger #> [1] "dummy logger" #> #> $caller #> [1] "eval" #> #> $msg #> [1] "foo bar" #># Also contains the Logger that created it as .logger l$last_event$logger#> [1] "dummy logger"# equivalent to l$last_event$.logger$name#> [1] "dummy logger"# This is really a reference to the complete Logger, so the following is # possible (though nonsensical) l$last_event$.logger$last_event$msg#> [1] "foo bar"#> [1] TRUE


Elton John 'drank a bottle of whisky a day' when alcoholism was at its worst
Elton John used to drink ‘a bottle of whisky a day’ when he struggled with alcoholism.
The music icon battled an addiction to drugs and alcohol at the height of his career.
He also suffered from the eating disorder bulimia, where he would binge eat after three days of going without food.
The singer confessed that he also lost friendships during this troubling time, lashing out at them when they pleaded with him to seek help.
He eventually checked himself into a rehab facility in Chicago for six weeks which Sir Elton felt was the turning point in his life.
Speaking in a special interview with Graham Norton for the BBC, he was asked what made him want to get help.
Elton explained: ‘Not only was I a drug addict I was an alcoholic. I used to drink a bottle of Johnnie Walker Black every day.
Advertisement Advertisement
‘I used to smoke joints and I was bulimic.  It was a very lonely existence and I just thought, “am I gonna die or am I gonna live? What do you wanna do?”‘
He went on: ‘When you use drugs for that amount of time they don’t have the same effect. You just take them for the sake of it. They’re not making you happier they’re making you worse and I just sat there one night and said, “This is ridiculous”.
‘I couldn’t ask for help. People had told me time and time again that I needed help. All my closest friends. No. I screamed and shouted at them, “how dare you tell me?”. I wouldn’t talk to them for a year or so.
‘Then at the end of the day I’d had enough and I asked for help. I went to a place in Chicago which treated all three diseases, alcoholism, drug addiction and bulimia, all at the same time. Stayed there for six weeks, came home to England and had great after care and took a year off. I spent a long time working as hard as I did to being sober as I did to being an addict.’
Advertisement Advertisement
In the programme, which airs next week, he also discusses his friendship with Rod Stewart, his turbulent relationship with his late mother and being a parent to sons Elijah and Zachary.
Elton John: Uncensored airs 28 November on BBC One.
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


This info came from the Jaguar FAQ, and The Atari Enhanced Joystick Ports FAQ.
The Jaguar pad is basically four sets of buttons. Three of those sets have five buttons, and the fourth has six. Each set has one pin that is pulled low to read the buttons within that set on five (or six) other pins. It is useful to think of the Jagpad as a matrix of twenty-one buttons.
| Column address pin (outputs from console) |
|||||
|---|---|---|---|---|---|
| 4 | 3 | 2 | 1 | ||
| Row data pin (inputs to console) |
6 | Pause | |||
| 10 | A | B | C | Option | |
| 11 | East | 1 | 2 | 3 | |
| 12 | West | 4 | 5 | 6 | |
| 13 | South | 7 | 8 | 9 | |
| 14 | North | * | 0 | # |
Each of the four column pins is pulled low one at a time, and any buttons in that column that are pressed will cause a low signal to appear on its corresponding row pin.
High-density 15-pin male D-sub connector on the controller.
A Jagpad can only be read one column at a time, and that's done by pulling the column address pin low while leaving the other three high. Since each column address pin has two digital states, and there are four pins, you end up using only four of the total sixteen possible binary combinations of those address pins.
That's where the Team Tap comes in. Each binary combination of the four address pins corresponds to one of the four columns on one of the four controllers that can be attached.
Again, looking at it in tabular format helps. Each cell represents the console's digital output of pins 1-4 needed to address each column (1-4) of each pad (A-D).
| 1=high 0=low |
Column | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | ||
| Pad | A | 1110 | 1101 | 1011 | 0111 |
| B | 0000 | 0001 | 0010 | 0011 | |
| C | 0100 | 0101 | 0110 | 1000 | |
| D | 1001 | 1010 | 1100 | 1111 |
Jay Tilton
06/28/98


AWS account ID: {{$ctrl.cluster.aws_account_id}} not set yet
AWS region: {{$ctrl.cluster.aws_region}} not set yet
Costs bucket: {{$ctrl.cluster.costs_bucket}} not set yet
Skip tokens sync? {{$ctrl.cluster.skip_sync}}
Kube API URL: {{$ctrl.cluster.api_url}} not set yet
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This cluster has not been used in any robot tokens yet
This cluster has not been used in any user tokens yet


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close()
- Method in class ionic.Msmq.
Queue
Close the queue.
create(String, String, boolean)
- Static method in class ionic.Msmq.
Queue
Create a queue by name, with the given queue label and transactional access.
D
delete(String)
- Static method in class ionic.Msmq.
Queue
Delete a queue by the given name.
G
getBody()
- Method in class ionic.Msmq.
Message
Gets the message body.
getBodyAsString()
- Method in class ionic.Msmq.
Message
Gets the message body, as a string.
getCorrelationId()
- Method in class ionic.Msmq.
Message
Gets the correlation Id on the message.
getCorrelationIdAsString()
- Method in class ionic.Msmq.
Message
Gets the correlation Id on the message, in the form of a string.
getFormatName()
- Method in class ionic.Msmq.
Queue
Gets the formatname on the queue.
getHighPriority()
- Method in class ionic.Msmq.
Message
Gets whether the message will be treated with high priority.
getLabel()
- Method in class ionic.Msmq.
Message
Gets the message body.
getLabel()
- Method in class ionic.Msmq.
Queue
Gets the label on the queue.
getName()
- Method in class ionic.Msmq.
Queue
Gets the name of the queue.
getValue()
- Method in enum ionic.Msmq.
TransactionType
H
hresult
- Variable in exception ionic.Msmq.
MessageQueueException
I
ionic.Msmq
- package ionic.Msmq
isTransactional()
- Method in class ionic.Msmq.
Queue
Gets the transactional setting for the queue.
M
Message
- Class in
ionic.Msmq
The Message class models a message that is sent to or receive from an MSMQ queue.
Message(String)
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using a string argument for the contents of the body, and empty values for the label and correlation ID.
Message(String, String, String)
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using string arguments for the contents.
Message(String, String, byte[])
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using a string for the body and label, and a byte array for the correlation ID.
Message(byte[])
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using a byte array for the message body, and empty values for the label and correlation ID.
Message(byte[], String, String)
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using a byte array for the body, and a string for the label and correlation ID.
Message(byte[], String, byte[])
- Constructor for class ionic.Msmq.
Message
Creates a Message instance, using a byte array for the body the correlation ID, and a string for the label.
MessageQueueException
- Exception in
ionic.Msmq
An exception type to wrap any problems that occur during MSMQ operations.
MessageQueueException(int)
- Constructor for exception ionic.Msmq.
MessageQueueException
MessageQueueException(String, int)
- Constructor for exception ionic.Msmq.
MessageQueueException
P
peek()
- Method in class ionic.Msmq.
Queue
Peek at the queue and return a message without dequeueing it.
peek(int)
- Method in class ionic.Msmq.
Queue
Peek at the queue and return a message without dequeueing it,
Q
Queue
- Class in
ionic.Msmq
The Queue class represents a message queue in MSMQ.
Queue(String)
- Constructor for class ionic.Msmq.
Queue
Call this constructor to open a queue by name for SEND and RECEIVE operations.
Queue(String, Queue.Access)
- Constructor for class ionic.Msmq.
Queue
Call this constructor to open a queue with the specified access
Queue.Access
- Enum in
ionic.Msmq
The Queue.Access enum provides options for access to the MSMQ Queue: Either Receive, Send, or both.
R
receive(int)
- Method in class ionic.Msmq.
Queue
Poll the queue to receive one message, with the given timeout.
receive()
- Method in class ionic.Msmq.
Queue
Poll the queue to receive one message, with an infinite timeout.
S
send(Message, boolean, TransactionType)
- Method in class ionic.Msmq.
Queue
Send a Message, with the given transaction type, and with the given setting for high priority.
send(Message, TransactionType)
- Method in class ionic.Msmq.
Queue
Send a Message, with the given transaction type.
send(Message, boolean)
- Method in class ionic.Msmq.
Queue
Send a Message, with the given value for high priority.
send(Message)
- Method in class ionic.Msmq.
Queue
Send a Message on the queue.
send(String)
- Method in class ionic.Msmq.
Queue
Send a string as a Message, using UTF-8 encoding.
send(byte[])
- Method in class ionic.Msmq.
Queue
Send a byte array as a Message.
setBody(byte[])
- Method in class ionic.Msmq.
Message
Sets the message body.
setBodyAsString(String)
- Method in class ionic.Msmq.
Message
Sets the message body, as a string.
setCorrelationId(byte[])
- Method in class ionic.Msmq.
Message
Sets the correlation Id on the message.
setCorrelationIdAsString(String)
- Method in class ionic.Msmq.
Message
Sets the correlation Id on the message.
setHighPriority(boolean)
- Method in class ionic.Msmq.
Message
Sets whether the message should be trated as high priority or not.
setLabel(String)
- Method in class ionic.Msmq.
Message
Sets the message label.
T
toString()
- Method in exception ionic.Msmq.
MessageQueueException
Produce the string version for the given exception, including the string mnemonic for the HR wrapped by the exception.
TransactionType
- Enum in
ionic.Msmq
Specifies the transaction type to use when enqueuing messages into a transactional queue.
V
valueOf(String)
- Static method in enum ionic.Msmq.
Queue.Access
Returns the enum constant of this type with the specified name.
valueOf(String)
- Static method in enum ionic.Msmq.
TransactionType
Returns the enum constant of this type with the specified name.
values()
- Static method in enum ionic.Msmq.
Queue.Access
Returns an array containing the constants of this enum type, in the order they are declared.
values()
- Static method in enum ionic.Msmq.
TransactionType
Returns an array containing the constants of this enum type, in the order they are declared.
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Welcome Last Name, First Name | Logout
* This wireframe provide the workflow, the structure, and the interaction between some specific pages and sections in the eScreening project and it's covered all the interaction between the application features and behaviours.
* Some of the interaction between pages may require using a specific rules and steps in order to demonstrate.
* This wireframe still in progress.
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Average Scores for Veterans by Clinic
Generate PDF Report
Select VistA Clinics *
Select Modules *
Back to Reports
From Date *
To Date *
Report Type
Display Option
1
602 | 603 Select VistA Clinics
in case of Program Evaluator Role
U.S. Department of Veterans Affairs | eScreening Health Assessment (Ver 1.0)
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HariHara Sudhan Sivakkumar
Front End Web Developer
I am a Undergraduate Student with holistic knowledge of software development and design. I am also experienced in Working with different Machine Learning Projects That will be simplistic and benefit for the Society.
Working on Projects related to all software domains and helping the juniors to work on projects. Creating the chapter website using both front end and backend. Conducted 6-7 Webinars on different domains of Software.
#savvy #motivated #communicative #hard-working #cool-headed #organized #easy-going #funny
HTML5 - 97%
CSS3 - 94%
JavaScript - 86%
Bootstrap 4 - 90%
#Organization #TimeManagement #Communication
Name : S.Harihara Sudhan
Reg no : 19BCE0742
Phone no:<PHONE_NUMBER>
Address : fof4,justice Surya Residency, Aruna nagar, Puthur
January 2020 - Present
September 2019 - Present
April 2018 - Present
Tiruchirappalli (also called Tiruchi or Trichy) is an ancient city in India's southern Tamil Nadu state. The Kaveri and Kollidam rivers flow around Srirangam Island, which is known for sacred Hindu sites Sri Ranganathaswamy Temple.
Front End Developer
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Year
Motor Vehicle Violation
Equipment Violation
Investigative Stop
2013-2014
87.92%
9.91%
2.17%
2014-2015
88.54%
9.52%
1.94%
2015-2016
89.29%
8.88%
1.83%


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BL3 SDK
0.0.1
Borderlands 3 Class Documentation
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#include <BL3_UMG_classes.hpp>
Public Member Functions | |
| void | SetZOrder (int InZOrder) |
| void | SetSize (const struct FVector2D &InSize) |
| void | SetPosition (const struct FVector2D &InPosition) |
| void | SetOffsets (const struct FMargin &InOffset) |
| void | SetMinimum (const struct FVector2D &InMinimumAnchors) |
| void | SetMaximum (const struct FVector2D &InMaximumAnchors) |
| void | SetLayout (const struct FAnchorData &InLayoutData) |
| void | SetAutoSize (bool InbAutoSize) |
| void | SetAnchors (const struct FAnchors &InAnchors) |
| void | SetAlignment (const struct FVector2D &InAlignment) |
| int | GetZOrder () |
| struct FVector2D | GetSize () |
| struct FVector2D | GetPosition () |
| struct FMargin | GetOffsets () |
| struct FAnchorData | GetLayout () |
| bool | GetAutoSize () |
| struct FAnchors | GetAnchors () |
| struct FVector2D | GetAlignment () |
| Public Member Functions inherited from UObject | |
| std::string | GetName () const |
| std::string | GetFullName () const |
| bool | IsA (UClass *cmp) const |
| void | ProcessEvent (class UFunction *function, void *parms) |
| void | ExecuteUbergraph (int EntryPoint) |
Static Public Member Functions | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UPanelSlot | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UVisual | |
| static UClass * | StaticClass () |
| Static Public Member Functions inherited from UObject | |
| static FChunkedFixedUObjectArray & | GetGlobalObjects () |
| template<typename T > | |
| static T * | FindObject (const std::string &name) |
| static UClass * | FindClass (const std::string &name) |
| template<typename T > | |
| static T * | GetObjectCasted (std::size_t index) |
| static UClass * | StaticClass () |
Public Attributes | |
| struct FAnchorData | LayoutData |
| bool | bAutoSize |
| unsigned char | UnknownData00 [0x3] |
| int | ZOrder |
| unsigned char | UnknownData01 [0x8] |
| Public Attributes inherited from UPanelSlot | |
| class UPanelWidget * | Parent |
| class UWidget * | Content |
| Public Attributes inherited from UObject | |
| void * | VTableObject |
| int32_t | ObjectFlags |
| int32_t | InternalIndex |
| class UClass * | Class |
| FName | Name |
| class UObject * | Outer |
Additional Inherited Members | |
| Static Public Attributes inherited from UObject | |
| static FUObjectArray * | GObjects |
| struct FVector2D GetAlignment | ( | ) |
| struct FAnchors GetAnchors | ( | ) |
| bool GetAutoSize | ( | ) |
| struct FAnchorData GetLayout | ( | ) |
| struct FMargin GetOffsets | ( | ) |
| struct FVector2D GetPosition | ( | ) |
| struct FVector2D GetSize | ( | ) |
| int GetZOrder | ( | ) |
| void SetAlignment | ( | const struct FVector2D & | InAlignment | ) |
| void SetAnchors | ( | const struct FAnchors & | InAnchors | ) |
| void SetAutoSize | ( | bool | InbAutoSize | ) |
| void SetLayout | ( | const struct FAnchorData & | InLayoutData | ) |
| void SetMaximum | ( | const struct FVector2D & | InMaximumAnchors | ) |
| void SetMinimum | ( | const struct FVector2D & | InMinimumAnchors | ) |
| void SetOffsets | ( | const struct FMargin & | InOffset | ) |
| void SetPosition | ( | const struct FVector2D & | InPosition | ) |
| void SetSize | ( | const struct FVector2D & | InSize | ) |
| void SetZOrder | ( | int | InZOrder | ) |
|
inlinestatic |
| bool bAutoSize |
| struct FAnchorData LayoutData |
| unsigned char UnknownData00[0x3] |
| unsigned char UnknownData01[0x8] |
| int ZOrder |


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Because the web is becoming unreadable. Papyrus seeks to render the web in Safari's reader mode all of the time.
Papyrus removes all styling from a page and then intelligently adds new CSS based on content. This is currently extremely alpha and will likely break a lot of sites. It also doesn't currently handle SPAs because it only does its work on initial render.
Built with <3 by @4lpine


sublist(xs, @optional start = 0, end = length(xs))
Return a new list with elements from the list xs starting at index start (inclusive) and ending at index end (exclusive).
xs
start
end
sublist([1, 2, 3, 4, 5], 2, 4) // [3, 4]
Core Module Index | Contents


--- layout: page title: Shared Garden in Paris. subtitle: Here you can find the location and type of shared garden in Paris (Using leaflet library). use-site-title: true ---


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As a child, I grew up playing tennis and fanatically watching Grand Slam matches regularly with my family. So, when I stumbled upon the ATP Matches, 1968 to 2017 dataset on Kaggle I was instantly interested. The dataset encompasses match data regarding EVERY ATP tour tennis match from 1968 to 2017. As one can imagine, this posed as an obstacle. Due to the unreasonably large amount of computation time required to process the data, I decided to limit myself to Grand Slam matches between 1995 and 2016. So, it must be kept in mind when interpretating the graphs that some statistics contain discrepancies on a player's performance, as some player's careers exceed the span of 1995 to 2016 (e.g., Pete Sampras).
Everything starts at the search bar. Here, you can enter a desired player's first and last name (case unsensitive), or simply type "random" and render the first diagram.
Upon searching a (random) player a tennis-ball-like icon (node) appears, from which two links extend. Each of these links end in nodes, which when clicked on, extend into even more nodes! The upper half of the fully extended dendrogram contains the player's personal information, i.e., nationality, height and handedness. Whereas the lower three end nodes contain performance statistics calculated from the extensive dataset. The lower three end nodes (match statistics) update the bar chart below when clicked on.
A bar chart appears below the dendrogram, containing information on a selected player's performance at the four grand slams (in order of occurrence in a year). The contents of said bar chart can be updated in two ways: by clicking on the aforementioned end-nodes in the dendrogram, or by selecting the statistic of interest in the dropdown menu in the upper left corner. Furthermore, when one of the bar's is clicked, the donut chart is rendered.
An interesting comparison might be how players of different nationalities perform at a specific grand slam (on average). Hence, the donut chart. This chart depicts the average performance statistic of a certain nationality (determined by IOC codes) at a specific grand slam.
The aim of my application is to visually represent some well-known aspects of a tennismatch. Starting with the most evident one: making a lot of double faults in a game decrease you're chances of winning a game.
Losing points by making double faults means you're losing points in you're own turn to serve, which typically has the greatest probability of being you're wining game, as you get to be the first player to set the pace of the game.
Thus, a recurring pattern is seeing players with high average numbers of double faults having low match winning rates, and vice versa.
The exact opposite relationship holds for the number of aces players make. Namely, scoring a lot of aces in a game significantly increases your chances of winning that game and therefore the match.
However, this pattern is a lot less clear in the bar charts, as it is often expected that a player wins their serve game. This justifies the well-known saying in tennis "Tennis is a game of errors".
Meaning that players who make few errors are players who win matches.
Take a look, for instance, at the double fault rates of players like Roger Federer, Rafael Nadal, Novak Djokovic, Andre Agassi and other greats. They all seem to have low double fault rates, accompanied by high match winning rates.
Finally, my visualizations also set out to debunk the theory of homefield advantages, a phenomenon observed in many teamsports. The donut chart clearly depicts how there seems to be no overlap between the nationalities of single succesful players and the top performing nationalities.
Furthermore, players who share nationalities with the hosting country of a Grand Slam seem to have no advantage based on their country of origin.


--- title: "GWAS data visualization in R" author: - Lucas Kopecky Bobadilla date: 2020-03-18T21:13:14-05:00 categories: ["Bioinformatics"] tags: ["Genomics"] ---
GWAS (Genome-wide association studies) is a common study when you want to check genetic variability in a genomic scale. GWAS will mainly focus on the association between single-nucleotide polymorthisms (SNPs) and traits such as herbicide resistance or some sort of disease. GWAS can be applied to any organisms and species where you want to study variation between different phenotype. GWAS studies investigate the entire genome, in contrast to methods that specifically test a small number of pre-specified genetic regions.
In other words, GWAS is not made to identify gene-specific candidate genes. GWAS studies can be a valuable approach in weed science to help us to identify traits and weed characteristics that could help us better understand how to control it and even how to use some other traits for breeding purposes. GWAS is also a hypothesis-free approach since you are looking for clues in the entire genome to support your potential hypothesis about your trait of interest.
GWAS data is commonly analyzed using a Manhattan plot which is a type of scatter-plot used to display significant SNPs. In a Manhattan plot the x-axis will display the genomic coordinates per chromosome and the y-axis will display the association p-value for each single-nucleotide. This post will focus in the design of a Manhattan plot using ggplot.
Here are the packages that will use for creating the Manhattan plot:
library(tidyverse) # tidyverse packages
library(RColorBrewer) # complement to ggplot
library(ggrepel) # complement to ggplot
library(kableExtra) # table layout
A GWAS experiment have many steps. Since this post will focused in how create a nice visualization plot for your GWAS data, I will cover only the basics. For this example I will use a adapted available data from a Alzheimer’s disease study. Why not use a plant study? I will discuss this later, for a GWAS study a reference genome is required so we will use a human data due to the easy access to the genome (there are ways to go around this, but for the sake of this post lets just assume the genome of reference issue).
To conduct a GWAS here are some basic steps and key points to consider before visualization of the data:
As most of our weed science studies, the first step is to select individuals (or plants) that does not have the trait of interest. In this data we have 188 controls and 176 cases. There are other approaches that you can use for your GWAS study.
As mentioned previously, for this analysis we used the reference human genome GRCh38.p13 that can be found at NCBI. We will use this genome to calculate the odds-ratio and p-values for each SNP from both cases.
Before calculation of association is important to check for linkage-disequilibrium, population structure or any other confounding variable that can affect your results. Population structure is defined by the organization of genetic variation and is driven by the combined effects of evolutionary processes that include recombination, mutation, genetic drift, demographic history, and natural selection. Linkage-disequilibrium refers to the non-random association of alleles at different loci in a given population. It is important to account for this factors when designing your GWAS studies
The initial GWAS statistical analysis needs to be done prior plotting the data. This initial steps will not be done here due to the time to run the programs and also because this analysis was not conducted in R. For this analysis, here are some options you can use:
Plink: I decided to use this software based on Linux system. This software is well known for GWAS studies and I recommend it due to the speed of processing data on it.
Tassel: Tassel is commonly used in plant breeding studies, specially in Maize. You can also used it in R!
After running the data in Plink here are the data-set results obtained from plink:
Click here to download the data: https://drive.google.com/file/d/1OPbKktDUN4izxhcrcA1EtgAdhvWThDF8/view?usp=sharing
# load data
plink.result <- read_table2("/Users/maxwelco/Documents/OpenWeedSci/static/analysis1.assoc.logistic") # change to your root directory
head(plink.result) # get first 5 lines
## # A tibble: 6 x 10
## CHR SNP BP A1 TEST NMISS OR STAT P X10
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 rs3094315 792429 C ADD 339 1.47 1.76 0.0791 <NA>
## 2 1 rs3094315 792429 C COV1 339 0.417 -0.420 0.674 <NA>
## 3 1 rs3094315 792429 C COV2 339 0.00531 -2.46 0.0137 <NA>
## 4 1 rs3094315 792429 C COV3 339 1.82 0.293 0.770 <NA>
## 5 1 rs3094315 792429 C COV4 339 0.446 -0.396 0.692 <NA>
## 6 1 rs3094315 792429 C COV5 339 2.71 0.490 0.624 <NA>
glimpse(plink.result) # check data stucture
## Rows: 1,485,344
## Columns: 10
## $ CHR <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ SNP <chr> "rs3094315", "rs3094315", "rs3094315", "rs3094315", "rs3094315"…
## $ BP <dbl> 792429, 792429, 792429, 792429, 792429, 792429, 819185, 819185,…
## $ A1 <chr> "C", "C", "C", "C", "C", "C", "G", "G", "G", "G", "G", "G", "G"…
## $ TEST <chr> "ADD", "COV1", "COV2", "COV3", "COV4", "COV5", "ADD", "COV1", "…
## $ NMISS <dbl> 339, 339, 339, 339, 339, 339, 341, 341, 341, 341, 341, 341, 341…
## $ OR <dbl> 1.474000, 0.417400, 0.005308, 1.820000, 0.446000, 2.712000, 1.4…
## $ STAT <dbl> 1.75600, -0.42040, -2.46500, 0.29290, -0.39620, 0.49010, 1.4240…
## $ P <dbl> 0.07913, 0.67420, 0.01369, 0.76960, 0.69200, 0.62410, 0.15440, …
## $ X10 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
As you can see, this is a very big data-set where we have almost 1.5 million observations and 10 variables:
Now lets do some data cleaning to transform some variables to factor and remove the last column that does not mean anything. We are also filtering the data only for TEST = ADD
to only plot the data points corresponding to SNP effect:
df_clean <- plink.result %>%
filter(TEST == "ADD") %>%
select(-X10) # Remove all rows that don’t correspond to testing the SNP effect
fac <- c("CHR","SNP", "A1", "TEST") # select columns to be factor
df_clean[fac] <- lapply(df_clean[fac], factor) # transform columns to factor
glimpse(df_clean) # check new data stucture
## Rows: 246,663
## Columns: 9
## $ CHR <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ SNP <fct> rs3094315, rs4040617, rs4075116, rs6603781, rs3766180, rs660379…
## $ BP <dbl> 792429, 819185, 1043552, 1198554, 1563420, 1586208, 1596068, 16…
## $ A1 <fct> C, G, G, T, G, G, A, T, C, G, C, A, A, A, A, A, A, C, C, A, A, …
## $ TEST <fct> ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD, ADD…
## $ NMISS <dbl> 339, 341, 341, 336, 342, 341, 341, 338, 342, 342, 328, 342, 342…
## $ OR <dbl> 1.4740, 1.4160, 0.9771, 1.4910, 0.8282, 0.8004, 0.8130, 0.7624,…
## $ STAT <dbl> 1.756000, 1.424000, -0.139800, 1.755000, -1.098000, -1.321000, …
## $ P <dbl> 0.07913, 0.15440, 0.88880, 0.07933, 0.27230, 0.18660, 0.22470, …
Now we have a smaller (still big) data-set with almost 250,000 observations and we can go forward and create our Manhattan plot.
To create the Manhattan plot, I will create a function that takes two arguments (df = dataframe and threshold of SNP significance) was created following three steps:
Compute the cumulative position of SNP (BPcum) and add it to the data set. This was done by grouping the filtered data by chromosome and summarizing the Max BP for each chromosome. After that, using mutate I calculated total length by subtracting the cumulative chromosome length by the chromosome length and them use left join to add this into the data. A final step was arranging by chromosome and base pairs and create another column named BPcum to get the cumulative position of SNPs.
The second step was to prepare the x-axis to be used in the plot. By centering each chromosome to the middle point of the BPcum.
The final step is to create the plot. To do this I used ggplot where I specify the x as BPcum and y as the -log10 of the P-values. After that, I created a geom_point
layer by mapping color to Chromosome; color was defined by using the scale_color_manual
function where each chromosome was map in two colors. To use the previous created x-axis we defined it using the function scale_x_continuous
. Other aesthetic values were defined using the theme
function and geom_rapel
from the package ggrapel
was used to annotate the significant SNPs with another geom_point
function to highlight those points as well. Dashed line represent the defined threshold.
Lets implement our plot function:
manh_plot <- function(df, threshold) {
### 1. Compute the cumulative position of SNP ###
plot_data <- df %>%
# Compute chromosome size
group_by(CHR) %>%
summarise(chr_len=as.numeric(max(BP))) %>%
# Calculate cumulative position of each chromosome
mutate(tot=cumsum(chr_len)-chr_len) %>%
select(-chr_len) %>%
# Add this info to the initial dataset
left_join(df_clean, ., by=c("CHR"="CHR")) %>%
# Add a cumulative position of each SNP
arrange(CHR, BP) %>%
mutate( BPcum=as.numeric(BP+tot))
### 2. Generate x-axis ###
axisdf <- plot_data %>%
group_by(CHR) %>%
summarize(center=(max(BPcum) + min(BPcum)) / 2 )
### 3. create plot ###
plot <- ggplot(plot_data, aes(x=BPcum, y=-log10(P))) +
#specify the y and x values
geom_point( aes(color=as.factor(CHR)), alpha=0.8, size=1.3) +
# create scatterplot colored by chromosome
scale_color_manual(values = rep(c("#E2709A", "#CB4577",
"#BD215B", "#970F42",
"#75002B"), 22)) +
# set a colour pattern
scale_x_continuous(label = axisdf$CHR, breaks= axisdf$center) +
# scale the x-axis
scale_y_continuous(expand = c(0, 0)) +
# remove space between plot area and x axis
ylim(0,20) +
theme_light() +
theme(legend.position="none",
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.line = element_line(color = "black")) +
xlab("Chromosome") +
# add x label
geom_label_repel( data=plot_data %>% filter(P < threshold), # add annotation value
aes(label=SNP), size=3) + # add annotation
geom_point(data= plot_data %>% filter(P < threshold), # add annotation value
color="orange", size=2) + # Add highlighted points
geom_hline(yintercept = -log10(threshold), linetype="dashed") # threshold line
return(plot) # return the final plot
}
Now lets run our function! For our function we will set our threshold for SNP negative log p-value significance of \(10^{-10}\). This value is defined by you based on your data.
plot <- manh_plot(df_clean, threshold = 10^-10) # run function
plot
Great! It works looks like we have two major SNPs in our dataset. It seems that those SNPs are located in the chromossome 19. Lets take a look by zooming on it using the ggplot function coord_cartesian()
.
However, before doing it we need to identify the coordinates limits of each chromosome in the our genomic data. To do that lets use some tidyverse functions such as group by
and summarize:
# Identify max and min values for BPcum ##
kable(df_clean %>%
# Compute chromosome size
group_by(CHR) %>%
summarise(chr_len=as.numeric(max(BP))) %>%
# Calculate cumulative position of each chromosome
mutate(tot=cumsum(chr_len)-chr_len) %>%
select(-chr_len) %>%
# Add this info to the initial dataset
left_join(df_clean, ., by=c("CHR"="CHR")) %>%
# Add a cumulative position of each SNP
arrange(CHR, BP) %>%
mutate( BPcum=as.numeric(BP+tot)) %>%
group_by(CHR) %>%
summarize(`Upper limit` = max(BPcum),
`Lower limit` = min(BPcum))) %>%
kable_styling()
| CHR | Upper limit | Lower limit |
|---|---|---|
| 1 | 245330435 | 792429 |
| 2 | 488060817 | 245421949 |
| 3 | 687382885 | 488109420 |
| 4 | 878688928 | 687452420 |
| 5 |<PHONE_NUMBER> | 878770877 |
| 6 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 7 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 8 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 9 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 10 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 11 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 12 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 13 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 14 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 15 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 16 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 17 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 18 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 19 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 20 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 21 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 22 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 23 |<PHONE_NUMBER> |<PHONE_NUMBER> |
| 25 |<PHONE_NUMBER> |<PHONE_NUMBER> |
It seems that our chromosome is on the<PHONE_NUMBER> and<PHONE_NUMBER> limit lets plot it!
## plot only chromossome 19 according to the above values
plot +
coord_cartesian(xlim = c(2639657374,<PHONE_NUMBER>))
With this codes you can create your own (and customizable) Manhattan plot! That are many other analysis that you can do in a GWAS studies; it is a vast and amazing research approach that weed scientist can (and should) use!


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Please indicate which pattern best describes the basic word order of your language in matrix (main) clauses:
Note: Modules for V2 order (finite verb second, non-finite
verb clause-finally) and differing word order between matrix and
subordinate clauses are currently under development.


In a network, a hub is the key device that connects every workstation or other network device together. Unlike a switch or router a hub has no way to send data across each connection but they can detect basic error in the network such as collision. Furthermore, the role of a switch is to control the signal flow. What this mean is that it can allow connection to be open or close. A router is one of the main hardware device to use the network. It was bulit to recive and analyze and move data to the correct network.
A network topology is a structure of a network which can be arrange into physical and logical. we will focus on the most common one down below.
To start off with the most basic, bus network topology. In this network all computer are connected to a single main cable in a linear style called bus. Then, the connection is work like a bus station, stopping at each computer one by one.
Next is the star topology, in this topology, as the name suggest the network is connect in a geometry of a star. Every device is connect to a central network device (Hub,Switch and Computer) which represent as a server and the device itself as a client.
The Ring topology or ring network is a network structure that is connected in a circular manner. The data is sent around the ring until it reach its destination. This type of structure is commonly use in place such as schools and offices.
In a mesh topology, all workstation and network device is connected with one another, allowing the transmissions to be broadcast to the fullest even if one of the connect shuts down. This topology is most common use in wireless networks.
A tree topology, like the name suggest again is a special structure which many element are arrange in branches. In this network structure there are one connection between two data. This is due to two data have only one mutual connection, which form a Hierarchica database.
Finally, the hybrid topology is made up out of two or more other network topologies. This included bus topology, mesh topology, ring topology, star topology, and tree topology.
Take the quiz to see what you've learn
1. What is the name of topology that is structure in a circular geometry?
2. What is the adventage of a Bus topology?
3. What does a switch do?
4. Which Topologies is commonly use in schools and offices?
5. What is the disadventage of a Mesh topology?
Question 1: The correct answer is Ring Topology.
Question 2: The correct answer is Doesn't require much cable.
Question 3: The correct answer is Control the signal flow.
Question 4: The correct answer is Ring Topology.
Question 5: The correct answer is Waste alot of time to built and maintain.
You answered them all correctly!


Methods to convert RGB to hex, hex to RGB, alter colour brightness, lightness, intensity, also generate random 'nice' colours.
More...
|
| class | ColourGenerator |
| | Generates random colours close to primary colours as hex string and doesn't repeat a colour More...
|
| |
Methods to convert RGB to hex, hex to RGB, alter colour brightness, lightness, intensity, also generate random 'nice' colours.
◆ AdjustBrightness()
| static Bitmap JBToolkit.Images.ColourHelper.AdjustBrightness |
( |
System.Drawing.Image |
image, |
|
|
float |
brightness |
|
) |
| |
|
static |
Change the brightness of an image
◆ ChangeColorBrightness()
| static Color JBToolkit.Images.ColourHelper.ChangeColorBrightness |
( |
Color |
color, |
|
|
float |
correctionFactor |
|
) |
| |
|
static |
Change the brightness of a colour
◆ ColorToHexString()
| static string JBToolkit.Images.ColourHelper.ColorToHexString |
( |
Color |
color | ) |
|
|
static |
System.Drawing.Color to hex string
◆ ConsoleColorToColor()
| static Color JBToolkit.Images.ColourHelper.ConsoleColorToColor |
( |
ConsoleColor |
consoleColor | ) |
|
|
static |
Console color to System.Drawing.Color
◆ ExtractHexDigits()
| static string JBToolkit.Images.ColourHelper.ExtractHexDigits |
( |
string |
input | ) |
|
|
static |
Extract the hex color digits from a string
◆ HexStringToColor()
| static Color JBToolkit.Images.ColourHelper.HexStringToColor |
( |
string |
hexColor | ) |
|
|
static |
Color hex to System.Drawing.Color
◆ LightenImage()
| static Bitmap JBToolkit.Images.ColourHelper.LightenImage |
( |
Bitmap |
bmp | ) |
|
|
static |
Lighten (the gamma) of an image
◆ RgbColorStringToHex()
| static string JBToolkit.Images.ColourHelper.RgbColorStringToHex |
( |
string |
colorString | ) |
|
|
static |
Change RGB color string to hex string
The documentation for this class was generated from the following file:
- D:/OneDrive/Documents/Work/Repositories/JB.Toolkit/JB.Toolkit/Imaging/ColourHelper.cs


CPS takes no further action in case of girl found dead after family argument
Amber Peat, 13, was found hanged in June 2015.
No further action will be taken by authorities over the death of a 13-year-old girl following a review by the Crown Prosecution Service (CPS).
An inquest previously heard several opportunities were missed to help teenager Amber Peat, who was found hanged near her home in Mansfield, Nottinghamshire, in June 2015 following a family argument.
A coroner at the hearing in February concluded that agencies had missed 11 opportunities which could have prevented the youngster’s death, while a serious case review in March found her death “could not have been predicted”.
On Friday, the CPS said there was “insufficient evidence” to bring any prosecutions forward over the case.
Amber’s body was found three days after she went missing from her home after a row with her mother over household chores.
Her inquest at Nottingham Coroner’s Court heard that she had told her teachers she had to “scrub the floor” until 1.30am and that her stepfather had forced her to wear a “ridiculous” outfit to school.
Assistant coroner Laurinda Bower said Amber’s parents, Kelly and Danny Peat, were “not concerned in the slightest” after the teenager left her home in Bosworth Street, Mansfield, at about 5.10pm on May 30 2015.
Despite the youngster having a history of running away, Mrs Peat only called police to report her missing nearly eight hours later, at 12.56am the following day.
She admitted she should have called sooner when she gave evidence at the inquest.
Although up to 400 police staff were involved in the search for Amber, her body was only found on June 2.
The serious case review, commissioned by Nottinghamshire Safeguarding Children Board, suggested the teenager believed “she was to blame for many of the difficulties within the family”, while a lack of planning led to gaps in the care she received.
Despite discussing her problems with two of her previous schools and with a youth worker, practitioners admitted their understanding of Amber’s life was very limited.
The review suggested the family frequently moving house, a lack of recording in primary schools, a failure in following policies and procedures, and a weakness in transferring information led to the lack of communication between agencies.
A CPS spokesman said: “Following the inquest into the tragic death of Amber Peat, we have reviewed the case once more in light of the coroner’s findings and further material gathered by Nottinghamshire Police.
“After careful consideration of all the available evidence, we have concluded there is insufficient evidence to prosecute anyone for criminal offences relating to Amber’s treatment up to the time of her death.
“Amber’s relatives have been updated about the decision. Our thoughts remain with them.”


|
| Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
When mounting a NAS volume on multiple hosts, the same remoteHost and remotePath values should be used on every host, otherwise it will be treated as different datastores. For example, if one host references the remotePath of a NAS volume as "/mnt/mount1" and another references it as "/mnt/mount1/", it will not be recognized as the same datastore.
Properties
| Name | Type | Description |
|---|---|---|
| accessMode | xsd:string |
Access mode for the mount point.
Mounting in read-write mode would be successful irregardless on how the mount point is exported or access permissions. For example, mounting a volume that is exported as read-only as readWrite will succeed. Hence, that a readWrite mount succeeds should not be taken as an indication that all files on a mount is writable. If a file system is mounted readOnly, the system cannot create or modify any files on the file system. This is mostly useful for storing ISO images and templates, since a virtual machine cannot be powered on from a readOnly volume. The access mode of a mounted NFS volume can be obtained at accessMode.
|
| localPath | xsd:string |
The localPath refers to the name of the NAS datastore to be created
using this specification.
In the case of ESX Server, the datastore name is a component in the file system path at which the NAS volume can be found. For example, if localPath is set to "nas_volume" the created NAS datastore will be named "nas_volume" and it can be accessed via the file system path "/vmfs/volumes/nas_volume".
In the case of VMware Server, the localPath will also be used as the
datastore name, but the datastore name may not necessarily be
reflected in the file system path where the NAS volume may be
accessed.
|
| password* | xsd:string |
If type is CIFS, the password to use when connecting to the
CIFS server. If type is NFS, this field will be ignored.
Since VI API 2.5 |
| remoteHost | xsd:string |
The host that runs the NFS v3 or CIFS server. For NFS v4.1
and beyond use remoteHostNames defined later. The field remotehost
may be deprecated in future for NFS, so clients should plan to use the
property remoteHostNames to send in the host name(s) for both NFS v3
and v4.1
|
| remoteHostNames* | xsd:string[] |
Hostnames or IP addresses of remote NFS server. In case
of NFS v4.1 this may have multiple entries. For NFS v3 the
input should be same in both remoteHost and remoteHostNames.
Since vSphere API 6.0 |
| remotePath | xsd:string |
The remote path of the NFS mount point.
|
| securityType* | xsd:string |
Provided during mount indicating what security type,
if any, to use
See HostNasVolumeSecurityType
Since vSphere API 6.0 |
| type* | xsd:string |
Specifies the type of the the NAS volume.
Supported types are
CIFS,
NFS,
NFS41
If not specified, defaults to
NFS
Since VI API 2.5 |
| userName* | xsd:string |
If type is CIFS, the user name to use when connecting to the
CIFS server. If type is NFS, this field will be ignored.
Since VI API 2.5 |
| Properties inherited from DynamicData | ||
| None |
| Top of page | Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


The Finite Difference Method and Schrodinger's Equation
Introduction
The Schrodinger equation gives the time-evolution of some wavefunction due to some Hamiltonian:
\[\begin{equation} i\hbar \frac{\partial}{\partial t} \left | \psi \right \rangle = \hat{H} \left | \psi \right \rangle \end{equation}\]If we separate the time-dependent part of the wavefunction from the spatial part of the wavefunction, we can analyze the eigenvalues of the spatial wavefunction using the time-independent Schrodinger equation:
\[\begin{equation} \hat{H} \left | \psi \right \rangle = E \left | \psi \right \rangle \end{equation}\]We can find exact solutions for the time-independent Schrodinger equation for simple potentials (square wells, square barriers, harmonic oscillators, etc.). However, we usually cannot find an exact solution for more complicated potential distributions. In these cases, we have to turn to numerical methods. In this post, we are going to use the finite difference method to solve the one-dimensional, time-independent Schrodinger equation.
The Schrodinger Equation
Recall that the Hamiltonian operator acting on the wavefunction of the particle yields the energy of the particle:
\[\begin{equation} \hat{H} \left | \psi \right \rangle = E \left | \psi \right \rangle \end{equation}\]The Hamiltonian is simply the sum of the kinetic and potential energies:
\[\begin{equation} \left [ \hat{K} + \hat{V} \right ] \left | \psi \right \rangle = E \left | \psi \right \rangle \end{equation}\]We know that the kinetic energy of the particle is \(\hat{K} = \hat{p}^2/2m\) and the potential energy of the particle is \(\hat{V} = V(\hat{x})\).
Recall that the momentum operator is \(\hat{p} = -i\hbar \nabla\) for a position dependent wavefunction. We can then expand the Hamiltonian:
\[\begin{align} \left [ \frac{\hat{p}^2}{2m} + V(\hat{x}) \right ] \left | \psi \right \rangle &= E \left | \psi \right \rangle \\[0.1cm] \left [ -\frac{\hbar^2}{2m} \nabla^2 + V(x) \right ] \left | \psi \right \rangle &= E \left | \psi \right \rangle\\[0.1cm] \left [ -\frac{\hbar^2}{2m} \frac{d^2}{d x^2} + V(x) \right ] \left | \psi \right \rangle &= E \left | \psi \right \rangle\\[0.1cm] \end{align}\]The Finite Difference Method
We can find an approximate solution to the Schrodinger equation by transforming the differential equation above into a matrix equation. If we divide the x-axis up into a grid of n equally spaced points \((x_1, x_2, ..., x_n)\), we can express the wavefunction as:
\[\begin{equation} \left | \psi \right \rangle = \begin{bmatrix} \psi(x_1)\\[0.1cm] \psi(x_2)\\[0.1cm] \vdots\\[0.1cm] \psi(x_n) \end{bmatrix} \end{equation}\]where each \(\psi(x_i)\) gives the value of the wavefunction at the point \(x_i\). Now, all we have to do is express the Hamiltonian as a matrix operator.
Let’s look at the kinetic energy term first. Suppose that the distance between each grid point is \(\ell\). We remember that the first derivative of a function is just the slope of the function. If we are on some grid point, we can find the forward slope (between the current grid point and its neighbor to the right) or the backward slope (between the current grid point and its neighbor to the left).
\[\begin{align} \frac{d \psi(x_i)}{dx}\bigg|_{forward} = \frac{\psi(x_{i+1}) - \psi(x_i)}{\ell} \end{align}\] \[\begin{align} \frac{d \psi(x_i)}{dx}\bigg|_{backward} = \frac{\psi(x_i) - \psi(x_{i-1})}{\ell} \end{align}\]The second derivative is simply the derivative of the first derivative. We can find the second derivative of the wavefunction at a given grid point by using the forward and backward derivatives above:
\[\begin{align} \frac{d^2 \psi(x_i)}{dx^2} &= \frac{\frac{d\psi(x_i)}{dx}\bigg|_{forward} - \frac{d\psi(x_i)}{dx}\bigg|_{backward}}{\ell}\\[0.1cm] &=\frac{\frac{\psi(x_{i+1}) - \psi(x_i)}{\ell} - \frac{\psi(x_i) - \psi(x_{i-1})}{\ell}}{\ell}\\[0.1cm] &= \frac{\psi(x_{i+1}) - 2\psi(x_i) + \psi(x_{i-1})}{\ell^2} \end{align}\]Thus, in our case, we can write a kinetic energy operator as:
\[\begin{equation} \hat{K} = -\frac{\hbar^2}{2m\ell^2} \begin{bmatrix} -2 & 1 & 0 & \dots & \dots & 0\\[0.1cm] 1 & -2 & 1 & \dots & \dots & 0\\[0.1cm] 0 & 1 & -2 & \dots & \dots & 0\\[0.1cm] \vdots & \vdots & \vdots & \ddots & & \vdots\\[0.1cm] \vdots & \vdots & \vdots & & \ddots & 1\\[0.1cm] 0 & 0 & 0 & \dots & 1 & -2 \end{bmatrix} \end{equation}\]We note that if we multiply this kinetic energy operator and the wavefunction vector, we get the correct expression for the second derivative at every grid point.
If we have periodic boundary conditions, we need to make sure that we connect the first and last gird points with an off-diagonal term:
\[\begin{equation} \hat{K} = -\frac{\hbar^2}{2m\ell^2} \begin{bmatrix} -2 & 1 & 0 & \dots & \dots & 1\\[0.1cm] 1 & -2 & 1 & \dots & \dots & 0\\[0.1cm] 0 & 1 & -2 & \dots & \dots & 0\\[0.1cm] \vdots & \vdots & \vdots & \ddots & & \vdots\\[0.1cm] \vdots & \vdots & \vdots & & \ddots & 1\\[0.1cm] 1 & 0 & 0 & \dots & 1 & -2 \end{bmatrix} \end{equation}\]Next we can focus on the potential energy operator. If there were no kinetic energy in the system, we would expect the energy at each grid point \(x_i\) to equal the potential energy \(V(x_i)\). Thus, the matrix V must be diagonal with the potential energy at each point along the diagonal:
\[\begin{equation} V(\hat{x}) = \begin{bmatrix} V(x_1) & 0 & \dots & \dots & \dots & 0\\[0.1cm] 0 & V(x_2) & 0 & \dots & \dots & 0\\[0.1cm] \vdots & 0 & \ddots & & & \vdots\\[0.1cm] \vdots & \vdots & & \ddots & & \vdots\\[0.1cm] \vdots & \vdots & & & \ddots & 0\\[0.1cm] 0 & 0 & \dots & \dots & 0 & V(x_n) \end{bmatrix} \end{equation}\]The Hamiltonian is then the sum of the kinetic and potential energies. Once we have the Hamiltonian, we can use an eigenvalue solver to determine the final solution for the wavefunction.
Matlab Code
I created a short Matlab script to calculate the three lowest energy states of the Schrodinger equation given some potential energy distribution. It plots the wavefunction onto the potential energy distribution so that we can see how the changing potential affects the probability of finding the particle at a certain position.
The source code can be found on the Cupcake Physics GitHub page. All of the instructions for changing the parameters of the simulation are included in the script. Feel free to play around with the code and create your own custom potentials!


---
layout: post
title: Lazy loading social buttons and other performance tweaks at work today.
date: '2013-07-30T15:31:00.002+01:00'
author: John Smith
tags:
modified_time: '2013-07-30T15:31:49.527+01:00'
blogger_id: tag:blogger.com,1999:blog-31446756.post-7623599890744255537
blogger_orig_url: http://www.solrevdev.com/2013/07/lazy-loading-social-buttons-and-other.html
---
The end result means things are a little faster although I still have lots of optimizations to do. Getting there though!
webtoolsweekly - a good round up of web tools...sent weekly https://t.co/vNJcLOTLR8
— john smith (@solrevdev) July 30, 2013


Match Play. Red trails 0-2 to 7. How should Red play 65?
pips Red=131 White=117
(CorrectAnswer= 0/0, ErrorScore= 0)
The safe but timid play is 20/15, 20/14. At DMP that would be the correct play. However, gammons count double in backgammon and the only play that is not a double blunder or worse is 9/3*, 8/3.
That raises the gammons won from 14% with 20/15, 20/14 to more than 35%.
The problem is no brainer once you see that giving up the full prime is correct but inbuilt caution sometimes gets in the way of clear thinking. You have been warned!
That raises the gammons won from 14% with 20/15, 20/14 to more than 35%.
The problem is no brainer once you see that giving up the full prime is correct but inbuilt caution sometimes gets in the way of clear thinking. You have been warned!
1. Cubeful 0-ply 9/3* 8/3 Eq.: +1.082 0.854 0.383 0.009 - 0.146 0.036 0.001 2. Cubeful 0-ply 20/15 20/14 Eq.: +0.883 (-0.199) 0.875 0.159 0.004 - 0.125 0.026 0.001 3. Cubeful 0-ply 20/9 Eq.: +0.862 (-0.221) 0.856 0.180 0.004 - 0.144 0.025 0.001 4. Cubeful 0-ply 20/14 13/8 Eq.: +0.839 (-0.243) 0.846 0.181 0.004 - 0.154 0.028 0.001 5. Cubeful 0-ply 20/15 13/7 Eq.: +0.836 (-0.247) 0.840 0.192 0.005 - 0.160 0.030 0.002


class DialogJsFactory
Factory for transforming dialog.js to DialogJs.
DialogJsFactory(js: String, componentId: String)
Factory for transforming dialog.js to DialogJs. |
fun parse(): DialogJs?
Parse javascript to DialogJs. |
fun String.removeAnonFunctions(): String
Removes anonymous functions like this: |


{% extends 'base.html' %} {% block content %}
Add one line of code, and Fair Work will ensure that your Mechanical Turk workers are paid at least minimum wage. To start, get the code snippet for your Mechanical Turk account.
Fair Work adds a question to the bottom of your HIT asking the workers to self-report how long the task took them. Reports are then combined to estimate the effective rate for your tasks. Workers are auto-bonused to bring the payment up to a minimum wage of ${{MIN_WAGE}}/hr. Roughly 75% of AMT workers are in the U.S., so paying a reasonable minimum wage — over federal minimum wage — is the right thing to do as researchers to demonstrate that we take the high road.
Behind the scenes, the system tracks HIT acceptances, then aggregates the reported times at regular intervals to audit effective HIT wages. If a HIT is found to be underpaid, the system automatically bonuses the worker to compensate.
If we identify any workers who break faith by consistently biasing their estimates or colluding, we will remove them from bonus eligibility.
Yes. You can run a copy of the server yourself. By doing so, you are committing that you will 1) Not lower the minimum wage in the code, and 2) Run the payment scripts on their required schedule so that workers are paid promptly. The code can run on a standalone server; however, it is also packaged to be immediately deployable to Heroku.
A belief that we should be paying workers fairly.
Yes. But you are using this tool because you want to help ensure fair treatment to workers, right? Fair treatment starts with trusting workers. They will make good faith estimates.
Yes. But you are using this tool because you want to help ensure fair treatment to workers, right? Fair treatment starts with trusting workers. They will make good faith estimates.
Yes. But you are using this tool because you want to help ensure fair treatment to workers, right? Fair treatment starts with trusting workers. They will make good faith estimates.
Download the paper here, and cite this work as:
Whiting, Mark E., Grant Hugh, and Michael S. Bernstein. "Fair Work: Crowd Work Minimum Wage with One Line of Code." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. Vol. 7. No. 1. 2019.
Or with BibTeX:
@inproceedings{whiting2019fair, title={Fair Work: Crowd Work Minimum Wage with One Line of Code}, author={Whiting, Mark E and Hugh, Grant and Bernstein, Michael S}, booktitle={Proceedings of the AAAI Conference on Human Computation and Crowdsourcing}, volume={7}, number={1}, pages={197--206}, year={2019} }{% endblock %}


Have you looked into Davinci Color Grading software?
No, but I hear its nice. I am using Adobe Speed Grade. Should I switch?
Honestly, maybe.
Your partner is typing…


We're sorry but reddit account analyser doesn't work properly without JavaScript enabled. Please enable it to continue.


TWICE's 'Dance the Night Away' is bringing up the heat with 200 million views
by drifterTWICE now has 200 million views on "Dance the Night Away".
The girls' summer bop was released in July 2018, and it took just a little over a year for the girls to achieve the feat. "Dance the Night Away" now joins the girls' many, many MVs with over 200 million views, including "Fancy", "What is Love?", "Likey", "TT", "Like OOH-AHH", "Heart Shaker", "Yes or Yes", "Cheer Up", "Knock Knock", and "Signal".
Congratulations to TWICE! You can check out "Dance the Night Way" again below.


Buy local to support job creation - WAD 21 Nov 2019, 07:15 by Loide Jason Home \ National \ Buy local to support job creation - WAD - New Era Live


LONDON
UNDERGROUND
BERLIN
TRAM
BERLIN
WALL
BERLIN
UBAHN
Paint with different caps and pens.
Variable spray distance.
Coming soon: the all new 3D mode!


Data Scientist, Backend developer, Machine learning engineer, Blockchain enthusiast
I'm just a simple Computational math MSC student, Mechatronic engineer, backend developer, #MachineLearning and #Gamedev lover, I have delusion of youtuber and now streamer, There is no challenge big enough not to try, My code will change the world some day.
Web development with PYTHON / DJANGO / RUST also Kafka, Celery, AWS, GraphQL and elastic search.
Fast iteration over a B2C EdTech software.
Microservices creation, ETLs, Abstract libraries, etc.
Technical growth weekly meetups manager.
August 2020 - Now
February 2021 - Now
Team management, recluitment and Growth.
Fast iteration in Fintech software.
Web development with PHP / MYSQL / AWS also Python, Pytorch, Pandas and data cleaning.
Leader of the migration to django and flask microservices.
Data driven decision making development.
From Junior to Senior in 1 Year.
February 2019 - July 2020
Web, videogames, Machine learning, Android development, etc.
Software development with C# / MYSQL / PYTHON / AWS / Unity.
January 2017 - February 2019
AI, Gamedev, Software development an other topics.
January 2018 - Now
Honorable mention: Classification of data obtained through multisensorial devices for use in flexible Machine Learning platform with the ability to diagnose diseases.
June 2015 - September 2019
This is a machine learning project made in Unity3d and C#, using neural networks and genetic algorithms to create autonomous intelligences in video games.
This project is a minimalist pytorch-like machine learning framework useful for dynamic calculation of gradients in a dynamic computational graph system.
Twitter-Like web app using Decentralized technologies in this case a Blockchain.
Flask api that can read and write in a blockchain based database for login
Generic Chatbot made with Machine learning; Seq2Seq based algorithms made purely using C#.
A machine learning algorithm that stores all the actions of a user within a given environment and then trains one neural network with that data.
This is a twitter bot; it can follow, search, like, and ignore people from twitter.


I fish, and swim, and a couple other things. Still figuring it out tho
GPA: I'm in my first semester, give me time
Worked as lifeguard at community pool and private club; swim instructor to 4- and 5-year-olds.
Assisted start-up firm of 4 with all elements of Maker product launch; programmed in Java, JavaScript, Linux; manufactured circuit boards.
| Event | Time | Meet | Date |
|---|---|---|---|
| 100 Y Free | 47.58 | 2016 Eastern Zone Speedo Short Course Super Sectionals | Apr 03, 2016 |
| 200 Y Free | 1:38.12 | 2018 NCAA Division III Championships | Mar 22, 2018 |
| 400 L Free | 4:12.66 | 2016 Niagara LSC LCM Championships | Jul 30, 2016 |
| 500 Y Free | 4:28.23 | 2018 NCAA Division III Championships | Mar 21, 2018 |
| 1000 Y Free | 9:22.68 | 2017 NCSA Spring Junior National Championship | Mar 14, 2017 |
| 1650 Y Free | 15:32.96 | 2018 NCAA Division III Championships | Mar 24, 2018 |


btw: this pen can only be used for minimal tasks such as surfing the web.
Grab a small eraser and press the eraser inside the pencil tip, so that the eraser is secured tightly inside the tip of the pencil.
Grab some aluminum foil and cut it into smaller pieces if needed and then wrap it around the eraser until it covers the bottom and the sides of it and also a portion of the pencil. Make sure to press firmly, so that the aluminum foil doesn't come off of the eraser.
Grab some tape and cut small pieces and place them around the top of the eraser (on the side where the eraser meets the pencil) so that the eraser doesn't come off of the pencil.
Your DIY Stylus is now complete! To use the pen, make sure one of your fingers is touching the aluminum foil because of sciency stuff.