Resonators are key Ingress items for building Portals. They are used to gain
control of and level up Portals for your Faction. Every resonator contains XM
energy, and with every level it contains more XM. Each Portal
has 8 slots for resonators and you can deploy or upgrade them. They are the only
items that decay (lose XM) over time. Like every item it can be dropped, picked
up or recycled. They are
obtained by Hacking Portals or
using Passcodes.
Resonators can range from level 1 to level 8. Each resonator level is visually different
and they all have different
amount of energy that they can hold. The Portal level depends on deployed resonators,
they also affect how far you can link.
All Resonators are classified as very common type, meaning your odds of hacking
them are very high. Maybe in the future we will see some other types of
resonators, but as of now it seems very unlikely.


Welcome, You can download the APK from the link bellow. Att. Juan Patricio Di Bacco
Or you can try the app in your Browser


Photo: Priyanka Chopra enjoys paan as she tackles the cold weather of Delhi in a sweater
by TIMESOFINDIA.COMPriyanka Chopra has been juggling between India and the US for her work commitments. Now, the actress is back in her home country and enjoying her time in Delhi.
The actress took to her Instagram to share a picture of herself enjoying a paan along with fighting the capital city's winter by wearing a sweater. In the photo, the actress looked cute in a black outfit with a brown hoodied sweater over it.  Paan tasting!" and tagged the location as New Delhi.
Check out the post here:
Priyanka who landed recently in Delhi took to her Instagram to share a selfie sporting a mask as she was fighting the toxic air in the city.
Meanwhile, on the work front, Priyanka was last seen in Shonali Bose's 'The Sky Is Pink' co-starring Farhan Akhtar, Zaira Wasim and Rohan Saraf. The film was released on October 11, 2019.


Labeling this number of sequences as 'positive' was found by AME to be optimal (or your primary sequence file contains this number of sequences and you also provided a control sequence file). The sequences were chosen as described above under "Labeling positives".
Labeling this number of sequences as 'negative' was found by AME to be optimal (or you provided a control sequence file containing this number of sequences). The sequences are those remaining after the 'positive' sequences were chosen as described above under "Labeling positives".
The number (percentage) of sequences labeled as 'positive' (Pos) and classified as 'positive' (TP),
%TP = 100 * TP/Pos.
Labeling of sequences as 'positive' is described in the help bubble for "Pos".
Classification is described above under "Classifying positives".
The number (percentage) of sequences labeled as 'negative' (Neg) but classified as 'positive' (FP),
%FP = 100 * FP/Neg.
Labeling of sequences as 'negative' is described in the help bubble for "Neg".
Classification is described above under "Classifying positives".
The (optimal) score threshold for labeling sequences as 'positive' determined by AME. (See the note about "Labeling positives" in the section above.)
The (optimal) score threshold for classifying sequences as 'positive' determined by AME (or provided by
you using --hit-odds-fraction
or --fasta-threshold
).
By default, the true positive and false positive sequences are those with sequence motif scores ≥ the value shown.
If you specified --poslist pwm
, then the true positive and false positive sequences are those with
FASTA scores ≤ the value shown.
The value of Pearson's correlation coefficient (ρ). It varies between -1 and +1. High values of ρ indicate strong correlation between the sequence motif scores and the sequence FASTA scores. Note that positive ρ means that sequence motif scores go up when sequence FASTA scores go down. The p-value of this method is the probability that ρ is at least as large as the observed value assuming there is no relation between the scores.
The value of Spearman's rank correlation coefficient (ρ). It varies between -1 and +1. High values of ρ indicate strong correlation between the sequence motif score ranks and the sequence FASTA score ranks. Note that positive ρ means that sequence motif scores go up when sequence FASTA scores go down. The p-value of this method is the probability that ρ is at least as large as the observed value assuming there is no relation between the scores.
The value of the mean squared error of the linear regression of X vs Y.
The slope of the regression line: Y = mX + b.
The Y-intercept of the regression line: Y = mX + b.
For further information on how to interpret these results or to get a copy of the MEME software please access http://meme-suite.org.
If you use AME in your research, please cite the following paper:
Robert McLeay and Timothy L. Bailey,
"Motif Enrichment Analysis: A unified framework and method evaluation",
BMC Bioinformatics, 11:165, 2010, doi:10.1186/1471-2105-11-165.
[full text]
| Logo | Database | ID | Alt ID | p-value | E-value | Pos Thresh | Pos | Neg | TP Thresh | TP (%) | FP (%) | Pearsons's CC (ρ) | Spearman's Rank CC (ρ) | Mean squared error | m | b |
|---|
| Database | Source | Motif Count |
|---|


Draw Photo Histograms: D3 + Canvas
The Finished Product
Histograms Redux
We’ve previously discussed histograms in this tutorial . Today, we’ll use that knowledge and apply it to making a histogram that shows the RGB distribution of an image the user uploads in the browser. We’ll graph all three components' frequencies with D3 and allow the user to highlight individual components. Sound like a lot of work? It’s not too bad actually!
High-Level Structure
Good code is like good plumbing. The memory doesn’t leak. Things flow in the direction you’d expect. Pipes do not simply terminate. Coding an app could be described simply as collating streams of data and creating pipes to make the data interact in meaningful ways to solve a task.
So let’s go with that: the task we want to solve is to display a histogram of a user-uploaded image. The image does not need to persist, so we can do it all in the browser.
We need to load the image from the user and pipe it into something that will allow us to extract its pixel data. For this, we’ll use HTML5 canvas.
Then, we need to process that pixel data and pipe it into something that will allow us to display it. For this, we’ll use D3.
So the pipes are User -> Canvas -> Pixel Data -> Histogram Data -> D3.
Pipe 1: User -> Canvas
We’ll use a file input to allow the user to upload an image.
<input type="file" id="imageFile" accept=".png, .jpg, .jpeg"></input>
You can restrict the types of files a user can upload using the input. In this case, we’ll limit it to images.
We add an event listener to the input element for a change
event.
document.getElementById("imageFile").addEventListener("change", handleFiles);
Then we write a function to handle the file upload when it occurs.
function handleFiles() {
var theImage = document.getElementById("imageFile").files[0];
var img = new Image();
var reader = new FileReader();
reader.addEventListener("load", function () {
img.src = reader.result;
});
img.onload = function () {
calcAndGraph(img);
};
if (theImage) {
reader.readAsDataURL(theImage);
}
}
The files are passed into the files
array. We only are going to grab the first file, if the user selects more than one.
Once we have the file, we have to use a FileReader in order to asynchronously read its contents into memory.
The FileReader returns a “load” event when the file is successfully read. The resulting data is present in the result
property of the FileReader.
We make sure that the user actually entered a file in the box (if (theImage)
). If so, we initialize the FileReader with readAsDataUrl
(Docs)
. This reads the contents of the file and returns a result formatted as a base64 encoded data string. This string can be displayed directly as the src
of an image tag.
We create this image tag, and when the file is successfully read, we set its src to the reader.result
. When the image is successfully loaded, we pass it into our calcAndGraph
function.
Pipe 2: Canvas -> Histogram Data
Now that we have our image, we are going to put it into the canvas and generate a histogram from canvasData.
We set our canvas height and width to that of the image’s height and width. Then, we draw the image to the canvas with drawImage
. Once the image is on the canvas, we can call getImageData
, which will return an ImageData
object (Docs)
. The object contains three properties: height, width, and data.
Data is a Uint8ClampedArray . This sounds complex, but it’s quite simple. It’s an 8-bit Unsigned Integer array. 8 bits can store 2^8 or 256 unique values (0-255). Unsigned means it cannot store negative numbers: all of the bits are used to store numeric values: there is no sign bit.
Clamped means that attempting to place a non-integral entry in the array will round the value to the nearest integer (3.4 -> 3, 3.51 -> 4), and that all values below 0 will be set to zero. Similarly, all values greater than 255 will be set to 255.
If you think about the RGB color model , this is the perfect data structure. Each pixel in the image is represented by four entries in the array: a R, G, B, and A (alpha) value. To read sequential pixels' RGBA values, you skip ahead by 4.
Now that we have the data, we create three dictionaries that we’ll use to hold the number of occurrences of a red, green, and blue pixel of a given intensity, rD, gD, and bD, respectively.
We instantiate the dictionaries by setting the initial frequency of every pixel to 0. We do this because it’s possible that an image might not have all 256 values for a given color component.
Finally, we iterate through the array, moving forward pixel by pixel (i+=4
), assigning the i-th, i+1th, and i+2th pixel to the red, green, and blue dictionaries, respectively. The i+3th value is the alpha, or opacity. In our case, we’re ignoring this channel.
function calcAndGraph(img) {
let rD = {},
gD = {},
bD = {};
let cv = document.getElementById("C");
let ctx = cv.getContext("2d");
cv.width = img.width;
cv.height = img.height;
ctx.drawImage(img, 0, 0);
const iD = ctx.getImageData(0, 0, cv.width, cv.height).data;
for (var i = 0; i < 256; i++) {
rD[i] = 0;
gD[i] = 0;
bD[i] = 0;
}
for (var i = 0; i < iD.length; i += 4) {
rD[iD[i]]++;
gD[iD[i + 1]]++;
bD[iD[i + 2]]++;
}
histogram({ rD, gD, bD });
}
Once we create these three pixel frequency dictionaries, we’re done with the canvas. We pass the data into our histogram function for display.
Pipe 3: Histogram Data - > D3
The histogram function is pretty typical D3. histogram
wraps an inner function that graphs individual histogram components. We establish base dimensions for the graph and margins as well as the y-axis labels. We size the svg component in the HTML accordingly. Then we call graphComponent
, our inner function, with each of the three datasets and the desired color to display.
graphComponent
does the main work of the graphing: it removes existing values (remember, the user can upload photo after photo, and we don’t want old data to persist) as well as resets the yAxis. We only need to draw the y-axis once per data set, since we’re overlaying all three bar groups. We set the flag once we’ve drawn it, and reset the flag once we draw a new histogram’s worth of data.
We map the data to an index (object key) and frequency (object value).
Then we create bars for each data element. Pretty easy.
function histogram(data) {
let W = 800;
let H = W / 2;
const svg = d3.select("svg");
const margin = { top: 20, right: 20, bottom: 30, left: 50 };
const width = W - margin.left - margin.right;
const height = H - margin.top - margin.bottom;
let q = document.querySelector("svg");
q.style.width = width;
q.style.height = height;
if (yAxis) {
d3.selectAll("g.y-axis").remove();
yAxis = false;
}
function graphComponent(data, color) {
d3.selectAll(`.bar-${color}`).remove();
var data = Object.keys(data).map(function (key) {
return { freq: data[key], idx: +key };
});
var x = d3
.scaleLinear()
.range([0, width])
.domain([
0,
d3.max(data, function (d) {
return d.idx;
}),
]);
var y = d3
.scaleLinear()
.range([height, 0])
.domain([
0,
d3.max(data, function (d) {
return d.freq;
}),
]);
var g = svg
.append("g")
.attr("transform", `translate(${margin.left}, ${margin.top})`);
if (!yAxis) {
yAxis = true;
g.append("g")
.attr("class", "y-axis")
.attr("transform", "translate(" + -5 + ",0)")
.call(d3.axisLeft(y).ticks(10).tickSizeInner(10).tickSizeOuter(2));
}
g.selectAll(".bar-" + color)
.data(data)
.enter()
.append("rect")
.attr("class", `bar-${color}`)
.attr("fill", color)
.attr("x", function (d) {
return x(d.idx);
})
.attr("y", function (d) {
return y(d.freq);
})
.attr("width", 2)
.attr("opacity", 0.8)
.attr("height", function (d) {
return height - y(d.freq);
});
}
graphComponent(data.gD, "green");
graphComponent(data.bD, "blue");
graphComponent(data.rD, "red");
}
User Interaction
We add a few buttons that lets the user focus in on one of the three color components, as well as reset to the initial blend. All that this is doing is changing the opacity of the selected bars.
<button id="red" class="focuser">++R</button>
<button id="green" class="focuser">++G</button>
<button id="blue" class="focuser">++B</button>
<button id="blend" class="focuser">Blend</button>
document.querySelectorAll("button.focuser").forEach((button) => {
button.addEventListener("click", amplify);
});
We create an event listener for the buttons that sends all clicks to our amplify
handler.
function amplify(e) {
const colors = ["red", "green", "blue"];
const boost = e.target.id;
if (boost == "blend") {
document.querySelectorAll("rect").forEach((bar) => {
bar.style.opacity = 0.7;
});
} else {
activeColor = boost;
const deaden = colors.filter((e) => e !== boost);
document.querySelectorAll(".bar-" + boost).forEach((bar) => {
bar.style.opacity = 1.0;
});
deaden.forEach((color) => {
document.querySelectorAll(".bar-" + color).forEach((bar) => {
bar.style.opacity = 0.2;
});
});
}
}
Amplify sets the current active color. If it’s blend, we set all opacities equal. Otherwise, we set the active color opacity to 1 and the background opacities to 0.2. Since we’ve set a unique class for each of our bar sets, this is simple “select bars + change values”.
Making It Pretty
Finally, to make things pretty, so that the user sees results immediately, we’ll pass in a base64-encoded image of a cat, stored in a string, and run our histogram code on it.
document.addEventListener("DOMContentLoaded", function (event) {
var img = new Image();
img.onload = function () {
calcAndGraph(img);
};
img.src = catImg;
addListeners();
});
Us versus Apple Preview
Here’s the original image opened in Preview compared with our app. Nice. Preview displays a few other components on the graph, hence the slightly differing on the right.
🐱 TIL
Histograms are pretty simple, and so is combining simple components to make a bigger product. Now that we know how to grab an image from the user and access its pixel data, think about all the other cool things we could do:
- calculating summary statistics on the image data distribution
- create an
isOverexposed
function - create an “Auto-Adjust” to automatically smooth out the pixel distribution
Exercises for the reader. Experiment & grow!


Half-way down the Exuma chain, Staniel Cay offers the opportunity to do modest reprovisioning (see comment below on missing mail boat) and charter flights can be arranged here.
Staniel Cay has been used in lots of movies, most notably the James Bond film “Thunderball”, and is a very popular tourist destination.
Don't miss the Thunderball Grotto.
Position 24° 10'N, 76°26'W.
There is a government dock at 24° 10.11'N, 076° 26.69'W. This is convenient for accessing two of the stores on the island.
The third store which is is on the other side of the inlet (at 24° 10.11'N, 76° 26.50'W) has its own dock. It also has some hardware and marine items.
An excellent anchorage can be had on the west side of Big Majors Spot, towards the northern end, just south east of
Fowl Cay Resort. The holding is good and there is shelter from NW through N to SE. You will need a fast dinghy to visit Staniel Cay from there.
The anchorage in the south east corner of Big Majors Spot, opposite Big Rock Cut has good holding except for a patch to the south of this area. This is very sheltered and out of the current.
Anchoring close to Staniel Cay is possible if you position yourself just out of the channel, but the tidal stream is fierce.
Last updated January 2015.


Verification Process
Each tutor needs to create a profile before they can use the system. The profile should show off their skills, level of experience, availability, photo of each tutor is required for the profile, however to increase the security of the app and the safety of the children an external verification will be carried out before the registration process is completed.
Meet with our staff
1. An email confirming the date, time and location of the meeting will be sent to your email address (to change the date, send an email back to our staff stating your availability for the verification)2. Bring your passport, student card, Nino and DBS certificate ( All documents must be original documents, not photocopies)
3. If the external verification was successful you can log in and edit your profile


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Special Projects
This story is part of a series of specially designed stories that represents some of the best journalism The Post has to offer. Check out the rest of the special projects here.
Ashton Nichols / For The Post
Paul Mass is not the usual professor. He does not have a doctoral degree, nor does he have much experience teaching. He is, however, a lawyer and an entrepreneur.
Mass, director of Ohio University’s Center for Entrepreneurship and “Entrepreneur-in-Residence” of OU’s College of Business, teaches Introduction to Entrepreneurship, which is part of the entrepreneurship certificate program and the entrepreneurial consulting class. Mass first got his start in entrepreneurship after being a practicing lawyer for 11 years.
“I went to Duke University undergrad and Emory University law school,” Mass said. “I then started my first company, which was a cable television company. We diversified into radio and data and was fortunate enough to pretty successful in those businesses, which I sold by the year 2000.”
He came to OU in August 2016 from San Diego, where he ran two companies: GovX and ClosingCorp.
“I spent the last 10 years in San Diego building GovX, which is an online retail website that services the military and policemen and firemen,” Mass said. “It’s like Amazon — a wide range of goods at a big discount. And then another database company called Closing Corp, which serves in the real estate industry.”
“It’s fun for me because it’s what I do. It’s what I’ve spent the last 30 years doing."– Paul Mass
Last year, Mass and Paul Benedict, associate director for the Center for Entrepreneurship, shared an office. Both are named Paul, left-handed, teach entrepreneurship in the College of Business, and have invested in and operated startup companies. Benedict said in the year and a half they have worked together, Mass has brought his experience to both students and Benedict.
“He’s got a lot of good stories of things that were successful and lessons that were learned along the way from losses,” Benedict said. “All of that stuff is really cool.”
Mass said he came to OU to help build the Center for Entrepreneurship, which he said is a resource for the entire university.
“We try to help students who aspire of starting their own business and working for themselves,” he said. “It’s fun for me because it’s what I do. It’s what I’ve spent the last 30 years doing. I’m hoping to build the center so that all students here at OU that might think about doing something on their own or for themselves will have a support system.”
Kurt Kissinger, a junior studying marketing and management information systems, said he believes Mass is the coolest college director because of his personality and his thorough experience in entrepreneurship.
“He comes to work every day with a bright personality, and it shines through in the job that he does and the way that he treats everyone,” Kissinger said in an email. “Paul has helped me learn not only about what it means to be a great entrepreneur, but how to be the most well rounded business professional.”
Mass often shares his experiences with his students and gives them insight on traits and the kind of people he would hire, which greatly helps students, Kissinger said.
“Paul has not directly helped me with my college path, but indirectly, he has shared with me his experience … this insight has helped me in interviews and has also helped me position myself more effectively to employers,” Kissinger said in an email. “Once again, this is not something that can be taught in a classroom but it (is) priceless to know.”
Photo by: Matt Starkey / Photo Editor
Development by: Taylor Johnston / Digital Production Editor
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Write a C program that stamps images.
This should generate a file called outputfile.ppm with a duck, circle, turkey, and square stamped on it. (You can use whatever shapes you wish; these are merely ideas.)
simple_image_machine -o outputfile.ppm duck 40 100 circle 90 500 turkey 600 600 square 200 200
Start breaking it down into pieces.
If any of those ideas are still unclear on how to implement, break them down:
Keep breaking it down until you get down to something that you can code up.
Install ImageMagick to help manipulate these images to get them ready to load into your program.
Mac with HomeBrew: brew install imagemagick
Arch Linux: sudo pacman -S imagemagick
Use convert
like so to generate an RGB image:
convert foo.jpg -compress none foo.ppm
Then the file will be in the format specified by the plain PPM format specification. This is an ASCII format that you might have an easier time reading in C.
If you want to read the data in binary format, use:
convert foo.jpg foo.ppm
and use fread()
to read entire rows of the image into arrays
at once. This is easier to code than parsing the ASCII version, but it
does involve learning about reading binary data in C (not rocket science).
After you generate your final PPM image, you can convert it back to a format that’s friendly to image viewers:
convert myimage.ppm myimage.png


ArtearProcessor()
This is the start point of this library.
There is a file, in resources folder, META-INF.services, called Processor that you must to set this class for the annotation processor take your class and init the routine.
The main process is executeProcess.
IMPORTANT: this a beta version of how create classes with an annotation processor. If any class is modify in WebWrapper maybe you must change some generated code classes in this library.
Author
David Tolchinsky


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Drag a file containing output from
npm list --all
and drop it here, or click to choose the file.
Your file stays in the browser.
Could not process your file. Please make sure it was generated by
and try again.


Java Jersey Database with Jersey 2.26 and JDBI
In the previous blog we just created an empty Jersey project to have a hello world service but it is quite useless. To make it more realistic I will add database connection and implement some methods with JDBI. You can utilize other options like MyBatis or Hibernate but I really don’t like giving all control to the ORM and have a system that increases productivity compare to having raw JDBC. If you haven’t used JDBI before it is the default JDBC Api on Dropwizard and actually it is quite similar to the Spring JDBC Template. Since we will be using simple stuff you will get the idea very quickly so don’t worry about that.
How does this really work? We don’t have a database connection at this stage and how do we start the db connection with application start and close it when the application is closed?
I will be using a ServletContextListener
for the web app which enables us to have methods on context initialized and destroyed. It is basically gives access to the start and end of your application. Also we could be using @PostConstruct
and @PreDestroy
for RestApplication
class and as expected it will run after app is initialized and before getting destroyed.
When you try to implement ServletContextListener you will notice it is not in our project scope yet. Add javax.servlet-api
to your pom to be able to use ContextListener
properly. Next will be defining a custom listener and adding it to our web.xml
file.
ServletContextListener
has two methods and one is contextInitialized, the other is contextDestroyed. You can check the logs to see where these methods run. Also after defining the custom listener we need to add it to web.xml
.
You web.xml file only had a simple webapp definition before and now it includes the context listener.
You can try running the app and see where these methods print the messages we put for now temporarily.
We can’t use a single db connection for a real application. It would require to have a proper connection pool which we will use HikariCP for that. Details for HikariCP can be found at https://github.com/brettwooldridge/HikariCP. Also as I mentioned before, JDBI is the JDBC wrapper we will be using. Add both of these dependencies to your pom file.
Now I want to have a database manager class for just storing the HikariDataSource
and Jdbi
object for database access.
With this manager class, we will start the connection contextInitialized
and close it with contextDestroyed
, and access to database connection with getJdbi()
method. The connection information will be stored in context xml or you can just set in start method with HikariConfig
object. Finally we can update context
methods to start and stop database connection pool.
As you might notice I get the connection details from the context lookup but haven’t done anything about it. First create a folder for the context.xml
just next to WEB-INF
as META-INF
and put the context.xml
in it.
These are specific HikariConfig details which you can update as you want but it should be enough for now to run our application. Also need to define this context in web.xml
. After adding it to web.xml
it should be something like below.
If you run without a proper database connection you will get the error Caused by: java.net.ConnectException: Connection refused (Connection refused)
.
I will run mysql on my local with docker. Also my default database name will be jersey
so make sure you created a schema as create database jersey;
.
docker run -d --name=jersey-mysql -e MYSQL_ROOT_PASSWORD=mypassword -v mysql:/var/lib/mysql mysql
Create a db package under com.blog.api
and have two classes in it as User
and UserDao
. User
is our resource model and make sure you implement empty constructor and getters-setters. Otherwise JDBI will fail to initialize the model while mapping it.
UserDao
is the JDBI interface where you just define your application logic. You can read JDBI specific details from http://jdbi.org.
Last thing will be removing the Test resource we had created in previous post and create a UserResource
class in rest package.
I will implement two rest calls for User
as get all users and get a specific user. To make sure you have all the data required put the default models as below in the RestApp
class. It will keep inserting new users as we re-run again but doesn’t really matter. You access the JDBI calls jdbi.onDemand(Dao.class)
and have your method call on it. It will close the connection automatically and will use the pool properly with Hikari.
These should be all we need to have a proper resource call on user table. First try a specific user call as /users/{userId}
and it should return a single user object.
http://localhost:8080/users/2
Next you can check all the users with /users/
call and see the list of users.
http://localhost:8080/users/
You can check the project from https://github.com/msdalp/jersey-jdbi-sample. Next I will convert Dao classes to annotation and inject it to resources automatically.


(For new projects, you should use the new networking system introduced in 5.1. This information is for legacy projects using the old networking system.)
The Network.Instantiate function offers a straightforward way to instantiate a prefab on all clients, similar to the effect of Object.Instantiate on a single client. The instantiating client is the one that controls the object (ie, the Input class is only accessible from scripts running on the client instance) but changes will be reflected across the network.
The argument list for Network.Instantiate() is as follows:
static function Instantiate (prefab : Object, position : Vector3, rotation : Quaternion, group : int) : Object
As with Object.Instantiate, the first three parameters describe the prefab to be instantiated along with its desired position and rotation. The group parameter allows you to define subgroups of objects to control the filtering of messages and can be set to zero if filtering is not required (see the Communication Groups section below).
Behind the scenes, network instantiation is built around an RPC call which contains an identifier for the prefab along with the position and other details. The RPC call is always buffered in the same manner as other RPC calls, so that instantiated objects will appear on new clients when they connect. See the RPC page for further details about buffering.
Communication groups can be used to select the clients that will receive particular messages. For example, two connected players might be in separate areas of the game world and may never be able to meet. There is thus no reason to transfer game state between the two player clients but you may still want to allow chat communication between them. In this case, instantiation would need to be restricted for gameplay objects but not for the objects that implement the chat feature and so they would be put in separate groups.


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YJ’s remark: This section is outdated because I have found a more accurate form of the equations of the guiding center motion, as given in Sec. 1. Besides to be more accurate, the new form is compact and suitable for numerical implementation
The equations for the guiding center motion are given by (refer to my note “collisionless_drift_kinetic_equation.tm”)
|
| (147) |
and
|
| (148) |
where μ ≡ mv⊥2∕(2B), b = B∕B. The term v∥κ⋅ in Eq. (148) can be further simpliﬁed by using Eq. (147), which gives
|
| (149) |
where use has been made of that the curvature κ is perpendicular to b.
[Benchmark: In GTC simulation (refer to H. Zhang’s paper[6]), the time derivative of v∥ is given by
|
| (150) |
|
| (151) |
Next, I prove Eq. (149) is equivalent to Eq. (151). Using κ ≡ b⋅∇b = −b×∇×b, the second term on the right-hand side of Eq. (149) is written as
where in obtaining the last equality, use has been made of that ∇× b ⋅ b ≈ 0 (note that this is correct to the order considered here, I will discuss this later). Using Eq. (152) in Eq. (149) yields|
| (153) |
which is identical with Eq. (151).]
The tokamak equilibrium magnetic ﬁeld can be written
|
| (154) |
In my code, the values of the two free functions, Ψ = Ψ(R,Z) and g(Ψ), which specify the magnetic ﬁeld, is read from the output ﬁle “G-eqdsk-ﬁle” of EFIT code. Using Eq. (154), the axisymmetric equilibrium magnetic ﬁeld can be written as
|
| (155) |
|
| (156) |
|
| (157) |
The partial derivative of the component of the magnetic ﬁeld is written as
|
| (158) |
|
| (159) |
|
| (160) |
|
| (161) |
|
| (162) |
|
| (163) |
|
| (164) |
In my numerical code, the numerical data of the poloidal ﬂux function Ψ(R,Z) and toroidal ﬁeld function g(Ψ) are read in from the output G-eqdsk ﬁle of the EFIT code. Then all the partial derivatives are calculated by using central ﬁnite-diﬀerence. The linear interpolation is used to evaluate the various quantities that are needed at the instantaneous location of guiding-centers to push the orbits.
When I began to write the guiding center orbit code, in order to avoid the numerical interpolating, I use Solovev’s analytic equilibrium. (The latest version of my code constructs magnetic ﬁeld by reading the output G-eqdsk ﬁle of the EFIT code and thus can treat general tokamak magnetic ﬁeld.) The Solovév equilibrium is an analytic equilibrium in which the poloidal ﬂux function Ψ is given by
|
| (167) |
where B0, R0, κ0, q0 are constant parameters. Using Eq. (167), the partial derivatives are written as
|
| (168) |
|
| (169) |
|
| (170) |
|
| (171) |
The toroidal ﬁeld function g is a constant function, g = cgR0B0, where cg is a dimensionless constant.


Debugger
back
b>
Device Properties:
Acceleration:
getCurrentAcceleration()
Geolocation:
getLocation()
Network:
checkConnection()


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Gephi Toolkit Javadoc | ||||||||
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public interface PreviewModel
The Preview Model contains all items and all preview properties.
Items are the visual elements built from the Graph
by ItemBuilder
implementations and can be retrieved from this class. Each item has a type and
default types are Item.NODE
, Item.EDGE
, Item.NODE_LABEL
and Item.EDGE_LABEL
.
A preview model is attached to it's workspace and can be retrieved from the
PreviewController
.
Item
,
Renderer
| Method Summary | |
|---|---|
java.awt.Dimension |
getDimensions()
Returns the width and height of the graph in the graph coordinates. |
Item |
getItem(java.lang.String type,
java.lang.Object source)
Returns the item attached to source and with the type
type . |
Item[] |
getItems(java.lang.Object source)
Returns all items attached to source . |
Item[] |
getItems(java.lang.String type)
Returns all items with type as type. |
Renderer[] |
getManagedEnabledRenderers()
Returns managedRenderers Renderers that are enabled, or null if managedRenderers is null. |
ManagedRenderer[] |
getManagedRenderers()
Returns currently managed renderers, or null. |
PreviewProperties |
getProperties()
Returns the preview properties attached to this model. |
java.awt.Point |
getTopLeftPosition()
Returns the top left position in the graph coordinate (i.e. not the preview coordinates). |
void |
setManagedRenderers(ManagedRenderer[] managedRenderers)
Sets an user-defined array of managed renderers to use when rendering. |
| Method Detail |
|---|
PreviewProperties getProperties()
Item[] getItems(java.lang.String type)
type
as type.
Default types are Item.NODE
, Item.EDGE
, Item.NODE_LABEL
and Item.EDGE_LABEL
.
type
- the item's type
Item[] getItems(java.lang.Object source)
source
.
The source is the graph object behind the item (e.g.
Node
or Edge
). Multiple items can be created from the same
source object. For instance both Item.NODE
and
Item.NODE_LABEL
have the node object as source.
source
- the item's source
source
as sourceItem getItem(java.lang.String type, java.lang.Object source)
source
and with the type
type
.
The source is the graph object behind the item (e.g.
Node
or Edge
) and the type a default or a custom type.
Default types are Item.NODE
, Item.EDGE
, Item.NODE_LABEL
and Item.EDGE_LABEL
.
type
- the item's typesource
- the item's source object
null
if not foundManagedRenderer[] getManagedRenderers()
Returns currently managed renderers, or null.
If managedRenderers
is set to null, all renderers will be executed when rendering, in default implementation order.
void setManagedRenderers(ManagedRenderer[] managedRenderers)
Sets an user-defined array of managed renderers to use when rendering.
Only the renderers marked as enabled will be executed when rendering, and respecting the array order
If the input array does not contain a managed renderer for some renderer existing implementation, a new not enabled managed renderer will be added to the end of the input array
If managedRenderers
is set to null, all renderers will be executed when rendering, in default implementation order.
managedRenderers
- Managed renderers for future renderingsRenderer[] getManagedEnabledRenderers()
managedRenderers
Renderers that are enabled, or null if managedRenderers
is null.
java.awt.Dimension getDimensions()
java.awt.Point getTopLeftPosition()
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fun movingAverage(source: PIDSource, taps: Int): LinearDigitalFilter
Creates a K-tap FIR moving average filter of the form: yn = 1/k * (xk + xk-1 + ...
This filter is always stable.
source
- The PIDSource object that is used to get values
taps
- The numbers of samples to average over. Higher = smoother but also slower.
IllegalArgumentException
- if the number of taps is less than 1


Dreaming consists of any mental activity, usually involuntary, that takes place during sleep. Such activity can involve images, sounds, ideas, emotions, and many other sensations. Dreams primarily occur during rapid eye movement (REM) sleep, when brain activity is high. Dreams may occur during other stages of sleep, but they are typically less vivid or memorable.
The nature of dreams varies greatly, as content can range from normal and ordinary to frightening and bizarre, or even sexual. The events in dreams are typically out of the dreamer’s control, with the exception of lucid dreaming, where the dreamer becomes self-aware.
Interpretations
The content and purpose of dreams are not definitively understood, though they have been a subject of scientific, as well as philosophical and religious, inquiry for centuries. The Greeks and Romans believed that dreams were directed messages from one or more deities, or from deceased persons, and that they predicted the future. Other prominent theories suggest that dreaming plays a substantial role in memory formation, problem solving, and gaining insight.
Most people today, instead, endorse Freud’s theory of dreams. Known as the father of psychoanalysis, Sigmund Freud posited that repressed wishes, emotions, fears, etc. from far back in the mind are released when one dreams. However, the content of these dreams must be censored, or at least distorted, because their disturbing nature would result in awakening or even degradation of self esteem.
Freud’s theory distinguishes between two types of content: ‘manifest content,’ what the dreamer subjectively experiences, and ‘latent content,’ or the deep-rooted meaning of a dream. In The Interpretation of Dreams (1899), Freud developed a psychological technique to interpret and understand the symbols and motifs present in dreams. Unfortunately, Freud claimed that only he was able to identify the underlying meaning of his patients’ dreams. Therefore, his theory is not testable, but also cannot be proven wrong.