 When a person goes to a web page like their computer sends a HTTP Request to a server. The server finds the appropriate HTML document and sends it back to the user's computer where a web browser interprets the page and displays it on the user's screen. This video does a good job of explaining.
HTML stands for Hypertext Markup Language. HTML documents form the majority of the content on the web. HTML documents contain text content which describes "what you see" and markup which describes "how it looks". This video gives a good overview.
 This makes them very unforgiving since a small mistake by a programmer can cause huge problems in a program.
HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them.
HTML elements are either inline or block. Block elements form an "invisible box" around the content inside of them.
The "tree-like structure" comes from the fact that HTML elements can have other elements inside of them. You can draw this relationship like a family tree. My mother had multiple children. So did her mother, and so on...
In a browser, this structure shows up as a series of nested boxes. There are boxes inside of boxes inside of boxes, and so on...
When you read an HTML document as text, you see a wave of changing indentations going up and down the left side of the document. The more indented an element is, the more deeply nested it's corresponding "box" is.
HTML controls the "structure" of a web page.
CSS controls the "style" of a page (how it looks).
CSS stands for "Cascading Style Sheets. CSS defines how HTML elements are to be displayed. Inheritance is a key feature in CSS, it relies on the ancestor-descendant relationship to operate. Inheritance is the mechanism by which properties are applied not only to a specified element, but also to its descendants.
CSS can be contained within an html file, by writing the css in the head of the html file. This method is good for very small projects.
To do this:
1. At the top of your HTML document, add a head element.
2. Inside the head element, add a style element.
3. Write your CSS in the style element and then put the HTML for the rest of your page below.
Or you can Link your HTML to a separate CSS file. his adds another step, but it lets you stay more organized when working on larger projects.
To do this: 1. Write all of your structural HTML in one html file.
2. In a separate css file put all of your css code.
3. Add a head element to the top of the HTML.
4. Add a link tag inside the head element.
5. Add the following attributes to the html in the head element:
<link>: * rel="stylesheet" * type="text/css" * href="main.css"
Selectors are what allows you to target specific HTML elements and apply style to them.
Types of selectors include(find examples here):
Class Selectors
Tag selectors
Positional
Attribute
Pseudo
HTML elements are boxes and each box has 4 components.
There are two techniques you can use to help deal with sizing issues.
1. Set sizes in terms of percents rather than pixels.
2. Set the box-sizing attribute to border-box for every element.
Different browsers work slightly differently. Sometimes this causes different browsers to display the same code differently.
Divs are block elements (as opposed to inline), so by default they take up the entire width of a page.
Adding the rule display: flex; to the appropriate CSS will override this behavior and let divs appear next to each other.


College Algebra II represents a significant opportunity for students to discover the beauty and practical power of mathematics. Concepts and skills are taught while at the same time a sense of algebra's utility in the real world is imparted. This course provides indepth coverage of college algebra topics that students continuing in mathematics will require. This is the kind of mathematics that students will use the rest of their lives in many fields. MATH 022 is a preparatory course intended to provide mathematical background in algebra with a function/graph emphasis required in calculus courses. Linear, polynomial, rational, exponential and logarithmic functions and their graphs provide necessary models for mathematical applications.
Upon successful completion fo the course, students should be able to:
College Algebra, 2nd Edition by John W. Coburn (McGraw-Hill, 2010) pre-packaged with one-semester access to ALEKS
| Unit | Topic(s) |
|---|---|
| 1 | Extensive review of intermediate algebra topics. The review will include: solving linear equations and inequalities, fundamental concepts associated with quadratic equations, solving equations and inequalities with absolute values and other miscellaneous equations involving rational expressions and radical expressions. An introduction to complex number arithmetic is also included. |
| 2 | General concepts associated with relations and functions. Topics such as the algebra of functions, function composition and range and domain are studied. The graphical representation of linear and quadratic functions are also presented. The algebraic and graphical representation of circles will also be studied as it pertains to terminology associated with relations in general. Basic graphical transformations are introduced. |
| 3 | Polynomial and rational functions are studied in detail. In particular, ideas associated with zeros of polynomials, end-behavior, and graph sketching are discussed. The unit includes synthetic division, the remainder theorem, the conjugate root theorem, horizontal and slant (oblique) asymptotes, and vertical asymptotes. |
| 4 | Exponential and logarithmic functions are studied in detail. Specific skills entail working with the rules of exponents and logarithms and solving exponential and logarithmic equations. Concepts associated with one-to-one functions and the existence of inverses are also studied. Sketching and recognizing the graphical representation of exponential and log functions is included. |
Algebraic skills will be primarily developed through work on ALEKS. ALEKS is a web-based tutorial system which provides students with ongoing skills based assessments and tracks progress. The midterm and final exams will also entail skills based problems.
As necessary, lectures will be done via BlackBoard Collaborate. The midterm and final exams will administered via Canvas.
Conceptual Skills will be incorporated in lectures, reading, and activities. Lectures and activities will be conducted approximately every other week. A typical lecture and/or activity will tie skill based ideas together to provide a larger overview of the topics. Lectures will be done via Elluminate and activities will be based upon discussion board prompts and/or assigned tasks from the text. The final exam will also incorporate questions which relate to the conceptual tasks.
Essentially all of the skills based work will be conducted through ALEKS. The ALEKS system will do ongoing assessments of skills based problems and track student progress. ALEKS is a major component of the course and a typical college algebra student will need to plan on 4-6 hours of online work each week (50-60 hours for the course). Skills based points will be earned by measuring mastered objective milestones and skills based ALEKS quizzes.
he text will be primarily used to tie ideas to concepts and skills together. This will be done through assigned readings and suggested problems. Occasionally, a problem may be selected to emphasize a specific skill that ALEKS did not adequately incorporate. Some of the activities will stem from the text. Finally, the text will support the ALEKS work in that explanations within ALEKS will reference specific areas from the text for further reading.
Elluminate is the web-conference utility where the virtual classroom resides. This may also be used for more "face-to-face" group correspondence with students.
The total number of points for the course will be 1000 points. Students will have an opportunity to earn 200 points in each of the following three areas: 1) Basic Skills (ALEKS work), 2) Conceptual Understanding (Activities and quizzes), and 3) the Final Exam (incorporates both basic skills and conceptual understanding). The final grade will be determined by the sum of these three areas. The minimum of the three area scores and the maximum of the three area scores will be incorporated twice and thus each of these two areas will will contribute to 40% of your overall grade as each will be worth 400 points. This rubric emphasizes that all three areas are of utmost importance.
| Conceptual Assessment | Basic Skills | Final Exam | Minimum 3 Areas | Maximum 3 areas |
|---|---|---|---|---|
| 4 Activities (5 pts / each) | 4 ALEKS Units (40 pts / each) | 1 Proctored Exam (200 pts) | ||
| 4 Quizzes (20 pts / each) | Final Set (40 pts) | |||
| 2 Midterm Exams (50 pts / each) | ||||
| 200 pts | 200 pts | 200 pts | 200 pts | 200 pts |
| Letter Grade | % Score | Total Points |
|---|---|---|
| A | 90-100 | 896-1000 |
| B | 80-89 | 796 -895 |
| C | 70-79 | 696-795 |
| D | 60-69 | 596-695 |
| F | 0-59 | 50-595 |
You will need to secure a proctor in order to take exams in this course. A proctor will not automatically be assigned to you; rather, you must make the necessary contacts to secure a professional who will serve in this capacity.


--- layout: post title: Once upon a time, code I wrote… date: '2008-10-05T14:43:00.001+01:00' tags: modified_time: '2008-10-05T14:43:54.794+01:00' thumbnail: http://lh4.ggpht.com/TheTechnologist/SOjEmUUi6bI/AAAAAAAAADg/ZbklG617Me4/s72-c/nifViewer3_thumb%5B10%5D.png?imgmax=800 blogger_id: tag:blogger.com,1999:blog-4015568221071268916.post-5313185741617021096 comments: true blogger_orig_url: http://serialseb.blogspot.com/2008/10/once-upon-time-code-i-wrote.html ---
After installing Windows Live Photo Gallery Beta, I noticed that it automatically indexed files shared on my big tera-server. And below is a nugget from code I’ve lost a long time ago, back when I was helping write a failed emulator for Dark Age of Camelot. A C# OpenGL reader that could render models of the game.


clndr.default ~ Default settings.
clndr.adjacent ~ test showAdjacentMonths and adjacentDaysChangeMonth.
edges of other months should be visible & clicking them should switch the month.
clndr.passInATemplate ~ Pass in a template.
clndr.passInEvents ~ Pass in events.
clndr.callbacks ~ Test the clickEvent callbacks. Logs in the console.
clndr.multiday ~ Show multi-day events (12th - 17th, 24th - 27th). Logs in the console.
clndr.multidayLong ~ Show multi-day events (12th - 17th, 24th - 27th). Logs in the console.
clndr.constraints ~ Test start & end constraints. (4th of this month to the 12th of next month). Logs in the console.
clndr.startConstraint ~ Test start constraint. (4th of this month). Logs in the console.
clndr.endConstraint ~ Test end constraint. (12th of next month). Logs in the console.
clndr.api ~ Test calling the API (next, previous, setMonth, setYear) with/without { withCallbacks: true } as an argument. onMonthChange and onYearChange will log only if withCallbacks is true.
clndr.sixRows ~ Test the forceSixRows option, which should make a six-row calendar regardless of the length of each month.
clndr.multipleMonths ~ Test length.months option, which should display three months.
clndr.oneWeek ~ Test length.days option. Should display two weeks, and next & previous buttons should advance in one week intervals.


Recipe 5.13. Finding Subsequences
Credit: David Eppstein, Alexander Semenov
Problem
You need to find occurrences
of a subsequence in a larger sequence.
Solution
If the sequences are strings (plain or
Unicode), Python strings' find
method and the standard library's
re module are the best approach. Otherwise, use
the Knuth-Morris-Pratt algorithm (KMP):
def KnuthMorrisPratt(text, pattern):
''' Yields all starting positions of copies of subsequence 'pattern'
in sequence 'text' -- each argument can be any iterable.
At the time of each yield, 'text' has been read exactly up to and
including the match with 'pattern' that is causing the yield. '''
# ensure we can index into pattern, and also make a copy to protect
# against changes to 'pattern' while we're suspended by `yield'
pattern = list(pattern)
length = len(pattern)
# build the KMP "table of shift amounts" and name it 'shifts'
shifts = [1] * (length + 1)
shift = 1
for pos, pat in enumerate(pattern):
while shift <= pos and pat != pattern[pos-shift]:
shift += shifts[pos-shift]
shifts[pos+1] = shift
# perform the actual search
startPos = 0
matchLen = 0
for c in text:
while matchLen == length or matchLen >= 0 and pattern[matchLen] != c:
startPos += shifts[matchLen]
matchLen -= shifts[matchLen]
matchLen += 1
if matchLen == length: yield startPos
Discussion
This recipe implements the Knuth-Morris-Pratt algorithm for finding
copies of a given pattern as a contiguous subsequence of a larger
text. Since KMP accesses the text sequentially, it is natural to
implement it in a way that allows the text to be an arbitrary
iterator. After a preprocessing stage that builds a table of shift
amounts and takes time that's directly proportional
to the length of the pattern, each text symbol is processed in
constant amortized time. Explanations and demonstrations of how KMP
works can be found in all good elementary texts about algorithms. (A
recommendation is provided in See Also.)
If text and
pattern are both Python strings, you can
get a faster solution by suitably applying Python built-in search
methods:
def finditer(text, pattern):
pos = -1
while True:
pos = text.find(pattern, pos+1)
if pos < 0: break
yield pos
For example, using an alphabet of length 4 ('ACGU'
. . .), finding all occurrences of a pattern of length 8 in
a text of length 100000, on my machine, takes about 4.3 milliseconds
with finditer, but the same task takes about 540
milliseconds with KnuthMorrisPratt
(that's with Python 2.3; KMP is faster with Python
2.4, taking about 480 milliseconds, but that's still
over 100 times slower than finditer). So remember:
this recipe is useful for searches on generic
sequences, including ones that you cannot keep in memory all at once,
but if you're searching on strings,
Python's built-in searching methods rule.
See Also
Many excellent books cover the fundamentals of algorithms; among such
books, a widely admired one is Thomas H. Cormen, Charles E.
Leiserson, Ronald L. Rivest, Clifford Stein, Introduction
to Algorithms, 2d ed. (MIT Press).
|


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DIYRENO.sg is the first and only platform that connect residential and commercial property owners directly with contractors or suppliers of specialized trade bypassing the middleman to fit out their property, thus, resulting in lower cost for owners and higher profit for contractors and suppliers.


Im a very open minded person. I agree that ,I made a clone of the jekyll theme Jekyll Theme
I'm free open source user FOSS
I'm also a volunteer member of a FREE SOFTWARE FOUNDATION OF TAMILNADU FSFTN and also a volunteer member of SJIT(GNU-LINUX-USERS-GROUP)
Im a orater in both tamil and English
Im creating a projects on Ruby on rails(framework)
MY Projects are displayed in GITHUB
You can visit my upcoming and present projects on my github page.
Visit Github PageYou can visit our collabarative Blog
Visit Common BlogAttendent the freesoftware
Attendent the Ethical Hacking workshop
Attendent the Iot geek meet(RASPERI PI)
Attendent the ANDDROID-Robotics Workshop


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Hello. What can I do for you?
I'm just looking around.
Will you tell me something about yourself?
Are you there? That time!
Where?
OK, my name is Limingqiang. I like singing, playing basketballand so on.
You wait for notice.
Consectetuorem ipsum dolor sit?
OK?
OK!


TFIDF
Threshold
Use these tokens for TFIDF calculation:
lemmas
words
TF
natural: tf
t,d
logarithm: 1 + log(tf
t,d
)
augmented: 0.5 + (0.5 * tf
t,d
) / (max
t
(tf
t,d
))
boolean: {1 if tf
t,d
> 0; 0 otherwise }
log ave: (1 + log(tf
t,d
)) / (1 + log(avg
t∈d
(tf
t,d
))
IDF
inverse frequency: log(N/n
t
)
Recalculate
<
/
>


I am an experienced Java developer who loves to engineer highly scalable and distributed applications in the cloud. My experience includes leveraging many AWS services which allow my teams to focus on business value without worrying too much about infrastructure.
I have a background in data warehousing and am skilled at identifying opportunities to translate business process management into software components. I thrive on real-time communication and rich analytics and I emphasize development speed, release quality, and an enjoyable work environment.
Senior Java engineer at a technology incubator, specializing in Microservices architecture and Continuous Integration/Deployment.
Integral member of a start-up technology shop, wearing all of the hats needed to keep the company online and growing.
Short engagement while this company was redefining their business strategy. I made great connections like Chad Walters, accelerated my introduction to Austin’s startup scene, and learned how spoiled an engineer can be.
Engaged in continual 2-week sprints with daily Scrum. Primarily tasked to a team of four that focuses on internal tool creation/maintenance and provides technical support/training to other teams. Constantly interacting with remote Unix machines over SSH for database administration and management, shell scripting, cron job maintenance, user management, and application deployment.
Front and back-end development for a large-scale insurance web application. Modernization effort to replace a series of disparates mainframe applications with a single J2EE web application. I am proud to have been one of the last four working on my team that was originally staffed with over 20 developers.
Studied a Java based Computer Science curriculum in a Unix environment. The courses emphasized object oriented programming, data structures, algorithms, design patterns, and testing with elements of software engineering and collaborative development.
I am alumni of the Computer Science House: special interest housing where students spend their free time working with technology.


I am a versatile programmer with knowledge and interest to learn and code, who specialise in Web and Android development.
"To succeed, I believe that it is important to enjoy the work you’re doing. When developing I am happier, more engaged and I perform at the top of my game" - Fey Ijaware.
I am aspiring to be a Full Stack Web and Android Developer by the end of the year.
I am currently working as a Front-End Developer.
My passion for Software Development started at the Co-op, when my manager gave me a website project to look at, to see what needed updating.
I ended up doing some aspect of Front End Web Development for a year. I learned on the job, but only the aspects that I needed to get the job done.
I graduated from Android Basic Nanodegree by Google via Udacity in July, 2017, thanks to a Google scholarship I received at the start of the year in January.
In just the six months I was learning Android Development, I have created over 10 apps, and the repositories are on GitHub.
From January, I took time off work to focus on learning and developing my digital skills. I have been studying via Udacity and a variety of online schools and resources, including W3Schools, FreeCodeCamp and CodeAcademy.
Now I am putting all these knowledge and skills into action in my new role as a Front-End Developer.
I think what makes me competent at this languages and frameworks is that I am able to look up documentations, I utilize TDD, but most importantly I am building and developing these skills and languages every day.
I am familiar with the Amazon Web Services Route 53, S3, Lambda and DynamoDB.
Used it in an app for login solution, using Account Kit and Facebook Login.
Used it to create assets for my projects, using it to edit and create image assests.
Used it to create a draft design for my web and mobile application before development.
I am familiar with the Amazon for Developer and am currently trying to build a skill for ALexa.
Used it to create a static website and currently using it at work.
Used it to help create and update PDF (Portable Document Format) files.
| Experience with | Languages | Frameworks |
|---|---|---|
| Version Control (Git and GitHub) | HTML (HTML5) and CSS (CSS3) | BootStrap |
| Command Line (Linux Server) | PHP | SASS/SCSS |
| Android Studio | Javascript | jQuery |
| CMS (Content Management Systems) | XML | ReactJS |
| Google Analytics and Data Studio | Java | Jekyll |
| Test-driven Development (TDD) | Python |
My projects are below and the repo for each project can be found on GitHub.
This is a 6 page website, which contains information about me, my resume, education, portfolio, stories, and ways to stay in touch or contact me. It was built using 84.1% HTML and 15.9% CSS.
It's a one page personal portfolio website. It was built using 88% HTML and 12% CSS. It uses BootStrap and Jquery to make it responsive and you can access all of the portfolio webpage's content just by scrolling. It contains links to my social media account and also my projects repositories.
It's a one page Tribute site to the victims of the Manchester Attack, to the 22 people killed at Manchester Arena at an Ariana Grande concert, on 22 May 2017. It was built using 93.6% HTML and 6.4% CSS.
An Android educational app. App includes a button for the user to submit their answers and receive a score, via a toast which accurately displays the results of the quiz. It was built using 100% Java. The app gracefully handles displaying all the content on screen when rotated and contains an if/else Control Statement.
An Android app that track scores between two Rugby teams. It was built using 100% Java. Each score button updates the score TextView in its column by adding the correct number of points and the reset button resets the scores on both of the score TextViews.
An Android inventory app, it uses SQLite database to store and present information. Built using 100% Java. App updates information based on user inputs, and creates intents to other apps using stored information. The app also includes logic to validate that no null values are accepted and no external libraries where used for core functionality.
An Android app which allows users to get a list of published books on a given topic, via an HTTP request from the Google Books API, fetches results and display them to the user. It was built using 100% Java. The network call occurs off the UI thread using an AsyncTask and it populates the information parsed from the JSON response, no external libraries where used for core functionality.
An Android tour guide app. It was built using 100% Java. The app presents relevant information to a user who’s intrested in visiting the Island. The app uses intents, ViewPager and Fragments, contains my own custom class and object for storing location information and uses a custom adapter to populate the layout with views based on instances of the custom class.
An Android app that displays information about a small business. Built using 100% Java. App is being updated to make it fully functional and it implements Facebook SDK for Android and uses Facebook Login and Account kit.
An Android habit tracking app which allows users to store and track information. Built using 100% Java. The app uses SQLite database to store and present information. The app is able to populating tables with new entries, modify the entries and display the contents of the table to users, and no Content Providers or external libraries where used for core functionality.
An Android news feed app that connects to the Internet to provide news articles via an HTTP request and API key from the Guardian API, it uses Intents as clicking on a story opens the story in the user’s browser. Built using 100% Java. The network call occurs off the UI thread using a Loader and it populates the information parsed from the JSON response, no external libraries where used for core functionality.


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If all else fails you can try checking out our Store or the Community Directory.
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If all else fails you can try checking out our Store or the Community Directory.


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nips nips2013 nips2013-327 nips2013-327-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
Abstract: We introduce the Randomized Dependence Coefﬁcient (RDC), a measure of nonlinear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-R´ nyi Maximum Correlation Coefﬁcient. RDC is deﬁned in e terms of correlation of random non-linear copula projections; it is invariant with respect to marginal distribution transformations, has low computational cost and is easy to implement: just ﬁve lines of R code, included at the end of the paper. 1
[1] F. R. Bach and M. I. Jordan. Kernel independent component analysis. JMLR, 3:1–48, 2002.
[2] L. Breiman and J. H. Friedman. Estimating Optimal Transformations for Multiple Regression and Correlation. Journal of the American Statistical Association, 80(391):580–598, 1985.
[3] H. Gebelein. Das statistische Problem der Korrelation als Variations- und Eigenwertproblem und sein Zusammenhang mit der Ausgleichsrechnung. Zeitschrift f¨ r Angewandte Mathematik u und Mechanik, 21(6):364–379, 1941.
[4] I.I. Gihman and A.V. Skorohod. The Theory of Stochastic Processes, volume 1. Springer, 1974s.
[5] A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch¨ lkopf, and A. Smola. A kernel two-sample o test. JMLR, 13:723–773, 2012.
[6] A. Gretton, O. Bousquet, A. Smola, and B. Sch¨ lkopf. Measuring statistical dependence with o Hilbert-Schmidt norms. In Proceedings of the 16th international conference on Algorithmic Learning Theory, pages 63–77. Springer-Verlag, 2005.
[7] W. K. H¨ rdle and L. Simar. Applied Multivariate Statistical Analysis. Springer, 2nd edition, a 2007.
[8] D. Hardoon and J. Shawe-Taylor. Convergence analysis of kernel canonical correlation analysis: theory and practice. Machine Learning, 74(1):23–38, 2009.
[9] T. Hastie and R. Tibshirani. Generalized additive models. Statistical Science, 1:297–310, 1986.
[10] L. K. Jones. A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training. Annals of Statistics, 20(1):608– 613, 1992.
[11] Q. Le, T. Sarlos, and A. Smola. Fastfood – Approximating kernel expansions in loglinear time. In ICML, 2013.
[12] K. V. Mardia, J. T. Kent, and J. M. Bibby. Multivariate Analysis. Academic Press, 1979.
[13] P. Massart. The tight constant in the Dvoretzky-Kiefer-wolfowitz inequality. The Annals of Probability, 18(3), 1990.
[14] R. Nelsen. An Introduction to Copulas. Springer Series in Statistics, 2nd edition, 2006.
[15] B. Poczos, Z. Ghahramani, and J. Schneider. Copula-based kernel dependency measures. In ICML, 2012.
[16] A. Rahimi and B. Recht. Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. NIPS, 2008.
[17] A. R´ nyi. On measures of dependence. Acta Mathematica Academiae Scientiarum Hungarie cae, 10:441–451, 1959.
[18] D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher, and P. C. Sabeti. Detecting novel associations in large data sets. Science, 334(6062):1518–1524, 2011.
[19] B. Sch¨ lkopf and A.J. Smola. Learning with Kernels. MIT Press, 2002. o `
[20] A. Sklar. Fonctions de repartition a n dimension set leurs marges. Publ. Inst. Statis. Univ. Paris, 8(1):229–231, 1959.
[21] L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt. Feature selection via dependence maximization. JMLR, 13:1393–1434, June 2012.
[22] M.L. Stein. Interpolation of Spatial Data. Springer, 1999.
[23] G. J. Sz´ kely and M. L. Rizzo. Rejoinder: Brownian distance covariance. Annals of Applied e Statistics, 3(4):1303–1308, 2009.
[24] G. J. Sz´ kely, M. L. Rizzo, and N. K. Bakirov. Measuring and testing dependence by correlae tion of distances. Annals of Statistics, 35(6), 2007.
[25] K. Zhang, J. Peters, D. Janzing, and B.Sch¨ lkopf. Kernel-based conditional independence test o and application in causal discovery. CoRR, abs/1202.3775, 2012. 9


[gecode-users] int vs. unsigned int for Matrix dimensions
Gregory Crosswhite
gcross at phys.washington.edu
Mon Jan 24 21:12:38 CET 2011
Well then, I am glad that I got the patch ready over the weekend. :-)
The patch has been attached to this e-mail as the file "patch"; it is
the result of running "svn diff" against revision 11556.
I have modified "gecode/kernel/array.hpp" along the lines that we
discussed, and have added a new test to your suite under
"test/array.cpp" to verify that it works as expected.
While I was add it, I added another feature. The patch includes a file
"gecode/iter/wrap-val.hpp" (and a patch to "gecode/iter.hh" to include
this file) which contains an iterator class "WrapVal" that wraps another
iterator and upon dereferencing returns the the result of calling
".val()" on the dereferenced wrapped iterator, as well as a function
"wrap_val" that is a convenience function for constructing a "WrapVal"
(since this causes the template parameters to automatically be inferred).
That might sound complicated, but the point of it is to allow someone to
do the following:
#include <algorithm>
#include <gecode/iter.hh>
#include <iterator>
#include <vector>
...
using namespace Gecode;
using namespace Gecode::Iter;
using namespace std;
...
IntVarArray a;
...
vector<int> v;
copy(wrap_val(a.begin()),wrap_val(a.end()),back_inserter(x));
which has the effect of copying all of the values from the array a into
the vector v, since a.begin() returns an iterator that dereferences to
an IntVar and wrap_val(a.begin()) returns a wrapped iterator that
returns the result of calling .val() on the IntVar.
The basic use case of the iterators is as follows:
IntVarArgs a;
...
for(IntVarArgs::iterator i = a.begin(); i != a.end(); ++i) {
rel(space,*i,IRT_EQ,0); }
Of course, you could more easily do this with rel(space,a,IRT_EQ,0). A
more interesting example is:
IntVarArgs a;
...
for(IntVarArgs::iterator i = a.begin(); i != a.end()-1; ++i) {
rel(space,*i > *(i+1)+2); }
More powerfully, iterators give you the ability to work with STL algorithms:
#include <algorithm>
...
void f(IntVar& v) { ... }
...
IntVarArgs a;
...
std::for_each(a.begin(),a.end(),f);
The iterators returned by a.begin() and a.end() dereference to a
reference to an IntVar/BoolVar (or a view in the case of ViewArray). If
you are interested in iterating over the *values* of the array --- that
is, the value currently assigned to to each variable then you can use
the function wrap_val to wrap these iterators so that dereferencing them
returns the value assigned to the variable. For example, the last two
lines in the following have exactly the same result:
#include <algorithm>
#include <iostream>
...
void f(IntVar& v) { std::cout << v.val() << " "; }
void g(int i) { std::cout << i << " "; }
...
IntVarArgs a;
...
std::for_each(a.begin(),a.end(),f); std::cout << std::endl;
std::for_each(wrap_val(a.begin()),wrap_val(a.end()),g); std::cout
<< std::endl;
Or if you want to copy the values assigned to the variables into a
std::vector:
#include <algorithm>
#include <iterator>
#include <vector>
...
IntVarArray a;
...
std::vector<int> v;
std::copy(wrap_val(a.begin()),wrap_val(a.end()),std::back_inserter(x));
Or if you are interested in computing the sum of the values in the
variables:
#include <numeric>
...
IntVarArray a;
...
int sum = std::accumulate(wrap_val(a.begin()),wrap_val(a.end()),0);
Or alternatively if you are interested in counting the number of zeros
in the variables:
#include <algorithm>
...
IntVarArray a;
...
unsigned int number_of_zeros =
std::count(wrap_val(a.begin()),wrap_val(a.end()),0);
===
Anyway, I hope that this patch can make it into Gecode this week; you
have my official permission to release it under whatever license Gecode
uses.
Let me know if you have any questions! :-)
Cheers,
Gregory Crosswhite
On 01/24/2011 10:54 AM, Christian Schulte wrote:
> Christian Schulte<cschulte at ...> writes:
>
> Hi again,
>
> There is one thing I forgot to say: we intend to release end of this week
> (hopefully). So if your patch arrives until then, it'll be included in the
> next version.
>
> Cheers
> Christian
>
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
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newChild
to the end of the list of children
of this node.
Attr
interface represents an attribute in an
Element
object.Attr
.
Attributes
interface, which includes Namespace support.AttributeList
;
that interface has been replaced by
Attributes
,
which is implemented in the
AttributesImpl
helper class.CDATASection
.
CharacterData
interface extends Node with a set of
attributes and methods for accessing character data in the DOM.CharacterData
and represents the
content of a comment, i.e., all the characters between the starting '
<!--
' and ending '-->
'.Comment
.
Attr
of the given name.
CDATASection
node whose value is the specified
string.
Comment
node given the specified string.
DocumentFragment
object.
DocumentType
node.
EntityReference
object.
ProcessingInstruction
node given the specified
name and data strings.
Text
node given the specified string.
Document
interface represents the entire HTML or XML
document.DocumentFragment
.
Document
.
DocumentType
.
DocumentFragment
is a "lightweight" or "minimal"
Document
object.ContentHandler
interface, which includes Namespace support.Document
has a doctype
attribute whose value
is either null
or a DocumentType
object.DOMImplementation
interface provides a number of methods
for performing operations that are independent of any particular instance
of the document object model.Element
interface represents an element in an HTML or XML
document.Element
.
Entity
.
EntityReference
.
EntityReference
objects may be inserted into the structure
model when an entity reference is in the source document, or when the
user wishes to insert an entity reference.FactoryConfigurationError
with no
detail mesage.
FactoryConfigurationError
with a
given Exception
base cause of the error.
FactoryConfigurationError
with the
given Exception
base cause and detail message.
FactoryConfigurationError
with
the String
specified as an error message.
Attr
node by local name and namespace URI.
NamedNodeMap
containing the attributes of this node (if
it is an Element
) or null
otherwise.
NodeList
that contains all children of this node.
DocumentType
)
associated with this document.
DOMImplementation
object.
Element
whose ID
is given by
elementId
.
NodeList
of all descendant Elements
with a given tag name, in the order in which they are encountered in
a preorder traversal of this Element
tree.
NodeList
of all the Elements
with a
given tag name in the order in which they are encountered in a
preorder traversal of the Document
tree.
NodeList
of all the descendant
Elements
with a given local name and namespace URI in
the order in which they are encountered in a preorder traversal of
this Element
tree.
NodeList
of all the Elements
with a
given local name and namespace URI in the order in which they are
encountered in a preorder traversal of the Document
tree.
NamedNodeMap
containing the general entities, both
external and internal, declared in the DTD.
DOMImplementation
object that handles this document.
null
if there is none.
data
and the substringData
method below.
DOCTYPE
keyword.
null
if it is
unspecified.
NamedNodeMap
containing the notations declared in the
DTD.
Document
object associated with this node.
Element
node this attribute is attached to or
null
if this attribute is not in use.
null
if it is
unspecified.
XMLReader
.
true
; otherwise, it is
false
.
XMLReader
that is encapsulated by the
implementation of this class.
DocumentHandler
interface. It has been replaced by the SAX2
DefaultHandler
class.true
when an attribute with a given name is
specified on this element or has a default value, false
otherwise.
true
when an attribute with a given local name and
namespace URI is specified on this element or has a default value,
false
otherwise.
newChild
before the existing child node
refChild
.
index
th item in the collection.
index
th item in the map.
NamedNodeMap
interface are used to
represent collections of nodes that can be accessed by name.Document
object
to build a DOM tree with.
DocumentBuilder
using the currently configured parameters.
DocumentBuilderFactory
.
SAXParserFactory
.
Node
interface is the primary datatype for the entire
Document Object Model.NodeList
interface provides the abstraction of an ordered
collection of nodes, without defining or constraining how this collection
is implemented.Text
nodes in the full depth of the sub-tree
underneath this Node
, including attribute nodes, into a
"normal" form where only structure (e.g., elements, comments,
processing instructions, CDATA sections, and entity references)
separates Text
nodes, i.e., there are neither adjacent
Text
nodes nor empty Text
nodes.
Notation
.
Document
object.
DefaultHandler
.
HandlerBase
.
Document
object.
InputSource
as XML using the specified
DefaultHandler
.
InputSource
as XML using the specified
HandlerBase
.
InputStream
as an XML
document and return a new DOM Document
object.
InputStream
instance as XML using the specified
DefaultHandler
.
InputStream
instance as XML using the specified
DefaultHandler
.
InputStream
instance as XML using the specified HandlerBase
.
InputStream
instance as XML using the specified HandlerBase
.
InputStream
as an XML
document and return a new DOM Document
object.
Document
object.
DefaultHandler
.
HandlerBase
.
XMLReader
interface, which includes Namespace support.ParserConfigurationException
with no
detail mesage.
ParserConfigurationException
with
the String
specified as an error message.
Parser
interface.ProcessingInstruction
.
ProcessingInstruction
interface represents a "processing
instruction", used in XML as a way to keep processor-specific information
in the text of the document.oldChild
from the list
of children, and returns it.
oldChild
with newChild
in the list of children, and returns the oldChild
node.
XMLReader
implementation class.EntityResolver
to be used to resolve
entities present in the XML document to be parsed.
ErrorHandler
to be used to report
errors present in the XML document to be parsed.
nodeName
attribute.
namespaceURI
and
localName
.
null
if it is
unspecified.
XMLReader
.
offset
,
keeping both in the tree as siblings.
Text
interface inherits from CharacterData
and represents the textual content (termed character data in XML) of an
Element
or Attr
.Text
node.
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KeyID
common
Keys
common
KfConsumer
kafka
KfPaths
kafka
KfProducer
kafka
KfQueue
kafka
kafka
externals
key
Field
GeoIpRange
keyID
FieldKey
keys
VariableKeys
keywords
Opts
kfQueue
LNBroadcast
kf_needRestore
Conf
kf_needSave
Conf
kf_param
Conf
kf_topicerr
Conf
kf_topicout
Conf
kfostr
Conf
Opts
kfstr
Conf
Opts


Tropical Island
Home
Multimedia
Contact
Get in touch:
name:
email:
subject:
comment:
Male
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Other
eye color:
Amber
Blue
Brown
Gray
Green
stats:
over 1.5m height
over 80 kg
date:


My Day
Log-in
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PLAYGROUNDr
Search options:
Playground
Sports Field
Pool
Splash pad
Ice rink
Dog park
Amenities in
bold
are present, based on reviews. Search results are sorted by the number of
Search options
they fulfill.
{{status}}


Menu
First
Second
Third
Fourth
Fifth
App - Safe Area
Card
Show Action Sheet
Show Menu
Show Picker
Show Toast
List Header
Item
Item Divider
Item Sliding
Start
End
Footer


This is the address which will be used as the contract owner. The contract owner has 'admin' privileges in the contract.
This will deploy your blockchain contract with the owner as the selected address
Contract deployed to address
Purchase a cow by selecting the cow below.
Transfer cow to new farm
Add cow meal


online homework for Runestone now available Here
Recent Updates (May 24, 2020)
- Runestone 5.0 - is live, this is a big refactoring of the Javascript code, please report any problems you find on github!
- Grading Interface -- Improved progress report on autograder as well as a nicer summary
- Datafiles - You can now add a custom datafile via the assignment interface. This datafile is available on the assignment page, as well as in the grading interface.
Support Runestone
Did you know that over 50,300 students have registered on Runestone Academy since August 2019!? At $100 a textbook we have
saved students over $5 million! Thats a lot of student and a lot of savings. Please consider a donation
to help pay for server costs and the ongoing development needed to keep runestone online.


const getFileManifestEntries = require('./get-file-manifest-entries');
const writeFileManifest = require('./utils/write-file-manifest');
const errors = require('./errors');
/**
* This method will generate a file manifest that can be used in a service
* worker to precache assets.
*
* @param {Object} input
* @param {String} [input.format] There are some options for how the file
* manifest is formatted in the final output. The format can be one of the
* following values:
* - **'iife'** - Output the manifest as an
* [immediately invoked function](https://en.wikipedia.org/wiki/Immediately-invoked_function_expression).
* - **'es'** - Output as an ES2015 module.
*
* Default value is 'iife'.
* @param {String} input.manifestDest The file path and name where the file
* manifest should be written (i.e. `./build/precache-manifest.js`).
* @param {String} input.globDirectory The directory you wish to run the
* `globPatterns` against.
* @param {Array<String>} input.globPatterns Files matching against any of
* these glob patterns will be included in the file manifest.
* @param {String|Array<String>} [input.globIgnores] Files matching against any
* of these glob patterns will be excluded from the file manifest, even if the
* file matches against a `globPatterns` pattern.
* @param {Object<String,Array|String>} [input.templatedUrls]
* If a URL is rendered with templates on the server, its contents may
* depend on multiple files. This maps URLs to an array of file names, or to a
* string value, that uniquely determines the URL's contents.
* @param {String} [input.modifyUrlPrefix] An object of key value pairs
* where URL's starting with the key value will be replaced with the
* corresponding value.
* @param {number} [input.maximumFileSizeToCacheInBytes] This value can be used
* to determine the maximum size of files that will be precached.
*
* Defaults to 2MB.
* @param {RegExp} [input.dontCacheBustUrlsMatching] Assets that match this
* regex will not have their revision details included in the precache. This
* is useful for assets that have revisioning details in the filename.
* @return {Promise} The returned promise resolves once the manifest file has
* been generated.
*
* @example <caption>Generate a build manifest of static assets, which can
* used with a service worker.</caption>
* const swBuild = require('workbox-build');
*
* swBuild.generateFileManifest({
* manifestDest: './build/manifest.js'
* globDirectory: './build/',
* globPatterns: ['**\/*.{html,js,css}'],
* globIgnores: ['admin.html'],
* format: 'iife', // alternatively, use 'es'
* })
* .then(() => {
* console.log('Build Manifest generated.');
* });
*
* @memberof module:workbox-build
*/
const generateFileManifest = (input) => {
if (!input || typeof input !== 'object' || Array.isArray(input)) {
return Promise.reject(
new Error(errors['invalid-generate-file-manifest-arg']));
}
return getFileManifestEntries(input)
.then((fileEntries) => {
return writeFileManifest(input.manifestDest, fileEntries, input.format);
});
};
module.exports = generateFileManifest;


Atkins and Kolbl take the title at Spartan Ultra World Championships
by Bonnie WilsonThe Spartan Million Dollar Challenge went unclaimed as athletes from 40 countries braved the harsh conditions for 24 hours in Åre, Sweden.
This year Canada's own Ryan Atkins ran 80 miles in 23:22:05 for the men's championship while Rea Kolbl took the women's title with 70 miles in 24:10:00.
For more details on the race, see the press release below:
Canada’s Ryan Atkins and Slovenia’s Rea Kolbl Battle Harsh Weather and Darkness to Win 24-Hour Spartan Ultra World Championship in Åre, Sweden
Hundreds of Endurance Athletes From 40 Countries Tested Their Limits at Third-Annual Obstacle Course Race; One Million Dollar Prize Purse Unanswered
ÅRE, SWEDEN (November 10, 2019) – The beautiful snow-covered mountains of Åre, Sweden set the stage for one of the most grueling endurance events of the season, the third-annual 24-Hour Spartan Ultra World Championship, which drew a field of athletes from more than 40 countries to the event’s new location. Returning men’s champion Ryan Atkins, defended his title completing 80-miles (129-km) with a time 23:22:05. The women’s race was captured by Slovenia’s Rea Kolbl, who finished 70-miles (112-km) with a time of 24:10:00, and a commanding lead over France’s Myriam Guillot-Boisset, who finished 65 miles (104-km) with a time of 24:55:02. Temperatures hit negative 22 degrees Fahrenheit (-30 degrees Celsius) with continuous snowfall, which provided a fresh blanket of powder on each lap.
On his path to victory, Atkins climbed more than 8,800-meters, higher than Mount Everest, while Kolbl climbed nearly 7,700-meters. On each 5-mile (8-km) course loop, athletes had to conquer 25 obstacles including the Spear Throw, Monkey Bars, Rope Climb, Sandbag Carry and Tire Drag.
A battle for second place in the men’s race was waged between 24-hour race first-timer Aaron Newell (USA), and an unknown Norwegian, Sebastian Conrad Haakansson, hailing from nearby Trondheim, who finished 80-miles (129-km) with a time of 25:28:06 over Newell who completed 75 miles (120-km) with a time of 23:34:26. William England from Umeå, Sweden, took fourth and Kristopher Brown, a 100-mile trail specialist, pinched out fifth-place.
“This year’s Spartan Ultra World Championship had beauty and serenity, behind it, which disguised a soul-crushing race that tested then toughest athletes on earth and pushed them to their limits,” said Spartan’s VP of Production David Watson. “This will go down in history as one of the greatest Spartan events that has ever happened.”
Although Atkins took home the race’s top spot on the Podium, he fell short of the distance needed to win Spartan’s Million Dollar Challenge, which offered the largest purse in OCR history to elite athletes who competed in Spartan’s three World Championship events during the 2019 season. The challenge began September 29 at the Spartan World Championship Powered By Rakuten in North Lake Tahoe, California, USA, before moving to the Trifecta World Championship in Sparta, Greece November 2, and ended, unanswered, here in Sweden. To win Atkins would have had to cover 110-miles (174-km). Other eligible athletes including Guillot-Boisset, would have needed 90-miles (145-km) and Aaron Newell, who placed in third with 75-miles, would have needed 110 to take home the prize – as per the bonus point system outlined in the challenge.
Missing from the podium completely, was Robert Killian, a favorite for the race and the $1 Million USD prize as he would have needed to cover 105-miles to. Killian ended the race early, 16:36:05 into the event, placing 10th with 55-miles (88-km) and a time of 16:36:05 when he saw his chances at the million slipping away.
In addition to the elite competition, the weekend featured age group and open categories for the 24-hour Ultra, as well as an 8-mile “Spartan Super” with 30 obstacles, which drew more than 200 racers of varying skill levels.
2019 Spartan Ultra World Championship Results
| MEN’S ELITE | MILE/KM | TIME | WOMEN’S ELITE | MILE/KM | TIME |
| 1. Ryan Atkins (CAN) | 80/129 | 23:22:05 | 1. Rea Kolbl (SVN) | 70/112 | 24:10:00 |
| 2. Sebastian Haakansson (NOR) | 80/129 | 25:28:06 | 2. Myriam Guillot-Boisset | 65/104 | 24:55:02 |
| 3. Aaron Newell (USA) | 75/120 | 23:34:26 | 3. Janka Pepova (SVK) | 60/104 | 22:59:43 |
| 4. William Englund (SWE) | 65/112 | 19:45:33 | 4. Inna Kovalenko (UKR) | 60/104 | 24:04:50 |
| 5. Glenn Racz (USA) | 65/112 | 22:26:11 | 5. Annette Sande | 50/92 | 20:23:46 |


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.)
Pay extra attention to the Facet column. Check the checkbox next to those values you want to search by, and we'll automatically index those values as counts
(i.e., facets).


Maximus is a powerful Roman general, loved by the people and the aging Emperor, Marcus Aurelius.  The powerful general is unable to save his family, and his loss of will allows him to get captured and put into the Gladiator games until he dies. The only desire that fuels him now is the chance to rise to the top so that he will be able to look into the eyes of the man who will feel his revenge.


SNVdb is a novel biological database aiming to bridge the gap between a number of databases with different types of information.
This site allows the user to display human single nucleotide variations (SNVs) onto the three-dimensional structure of the proteins they map to, showing whether these variations can be a disrupting factor in the interaction with other proteins (i.e. they map to a residue involved in the interaction interface between the two interacting parners). The inclusion of disease association data about the SNVs allows the user to make informed hypothesis about the causal mechanism of these disorders


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 1
Preheat the oven to 200C/400F/Gas 6.
For the seared poussin, rub the poussin breast and legs with a little of the olive oil, salt and freshly ground black pepper and curry powder.
Heat an ovenproof pan over a medium heat, add a dash of olive oil and place the poussin into the pan skin-side down. Fry for five minutes, or until golden-brown, then turn over.
Place in the oven and roast for 6-8 minutes, or until the poussin is golden-brown and completely cooked through - check that the juices run clear when pierced with a skewer. Remove from the oven and keep warm.
For the sweet pepper marmalade, heat the olive oil in a frying pan, add the red pepper and fry over a low heat for five minutes, or until softened. Add the garlic and fry for one minute. Add the red wine vinegar, sugar, ketchup and a splash of boiling water and simmer for 7-8 minutes, until reduced and sticky.
To serve, spoon the marmalade onto a serving plate and top with the seared chicken. Garnish with chopped parsley and serve.


Logo
Contest Description
Win a Free Dessert Meal!
Play this game and stand a chance to win a free dessert meal for you and your friends !
All you have to do is clear all 12 levels of this game in 30 seconds. The game is pretty simple.
Instructions
- The illustrated version of one of our delicious products will be displayed.
- This will be followed by six other images.
- All you have to do is select the image that matches with the image displayed to you earlier.
- Every correct answer gets you 2 cookies.
- Collect all 24 to win !
- Timer begins once the options appear and pauses while the test image is being displayed.
Simple, eh?
P.S. We've heard the cookie monster is on the loose.
Game Timer
Time Remaining
30
Question Image
Answer Options
Time Up Message
Oops! Time's Up !


$().SPServices.SPAutocomplete
The SPAutocomplete function lets you provide values for a single line of text column from values in a SharePoint list. The function is highly configurable and can enhance the user experience with forms.
Take a look at the demo page.
$().SPServices.SPAutocomplete({
WebURL: "",
sourceList: "",
sourceColumn: "",
columnName: "",
CAMLQuery: "",
CAMLQueryOptions: "<QueryOptions></QueryOptions>",
filterType: "BeginsWith",
numChars: 0,
ignoreCase: false,
highlightClass: "",
uniqueVals: false,
slideDownSpeed: "fast",
processingIndicator: "", // NOTE: This option has been deprecated as of v0.6.0 debug: false
});
The URL of the Web (site) which contains the sourceList. If not specified, the current site is used. Examples would be: "/"
, "/Accounting"
, "/Departments/HR"
, etc. Note: It's always best to use relative URLs.
The name or GUID of the list which contains the available values. If you choose to use the GUID, it should look like: "{E73FEA09-CF8F-4B30-88C7-6FA996EE1706}"
. Note also that if you use the GUID, you do not need to specify the WebURL if the list is in another site.
The StaticName of the source column in sourceList
The DisplayName of the column in the form
The CAMLQuery option allows you to specify an additional filter on the relationshipList. The additional filter will be
CAMLQuery: "<Eq><FieldRef Name='Status'/><Value Type='Text'>Active</Value></Eq>"
This option can be used to specify additional options for retrieval from the sourceList. See the MSDN documentation for GetListItems for the syntax.
This option allows you to specify how values should be matched. The available values are [BeginsWith, Contains]
and the default is "BeginsWith"
.
Wait until this number of characters has been typed before attempting any actions. The default is 0
.
If set to true, the function ignores case, if false it looks for an exact match. The default is false
.
When a matching value is shown, the matching characters are wrapped in a
. If highlightClass is specified, that class is applied to the span
. An example might be:
highlightClass: "ms-bold",
If set to true, only unique values returned from sourceList will be shown. The default is false
.
Speed at which the div should slide down when values match (milliseconds or [fast, slow]
). The default is "fast"
.
Note: This option has been deprecated as of v0.6.0
If present, this markup will be shown while Web Service processing is occurring. The default is ""
. Because this library requires no server-side deployment, I wanted to use one of the out of the box images. You can substitute whatever image or text you would like in HTML format.
Setting debug: true
indicates that you would like to receive messages if anything obvious is wrong with the function call, like using a column name which doesn't exist. I call this debug mode.
This is the sum total of what you'll need to add to your page to make the function work as it does in the demo page. The first two lines simply pull the script files into the page, and the $(document).ready(function()
line is a jQuery function that says "Run this script when the page has been fully rendered". In the first call to the function, note that we're turning debug mode on by setting debug: true
.


Telangana textile park gets first unit
by Our BureauDivya Textiles of Surat has set up the first unit at the upcoming Whitegold Spintex Integrated Textile Park, which is being established at Ibrahimpatnam in Rangareddy district of Telangana.
The company has set up 24 rapier looms with electronic jacquard and produces 1,200 sarees daily.
Jayesh Ranjan, Principal Secretary (Industries & IT), Telangana, and EV Narasimha Reddy, Vice-Chairman & Managing Director, Telangana State Industrial Investment Corporation, were present on the occasion.
More than 60 textile entrepreneurs from Surat attended the event. In his address, Ranjan promised full support to the investors in approval of incentives and urged them to submit proposals for customized incentives.
Whitegold Spintex Integrated Textile Park is being set up on 130 acres of land at Ibrahimpatnam and will provide developed plots to weaving, knitting and garment units.


Heart failure is a major growing public health burden worldwide. One in 5 men and women will develop heart failure in their lifetime. In the United States alone, it is estimated that more than 8 million people will face heart failure by the year 2030, with projected direct medical costs doubling in the next 20 years to $53 billion, and similar trends are projected worldwide. A lot of time and resources are wasted on heart failure prediction and most of the time it is too late. Automating and simplifying this process using data science will help the patients and the doctors as well since doctors will spend more time on the prevention and treatment for the patient. The need for strategies to prevent heart failure has sharpened the focus on identifying and treating high-risk asymptomatic individuals.
Add your health details in an electronic diary and predict the risk of heart failure. Keep a track of your health and prevent heart failure!


Good morning #HarvestCentury!
9/25/2016, 8:56:02 AM
Favs: 6
Retweets: 1
link--- layout: tweet ---
Good morning #HarvestCentury!
9/25/2016, 8:56:02 AM
Favs: 6
Retweets: 1
linkThe ride's been ferry ferry good so far.
9/25/2016, 1:27:59 PM
Favs: 1
Retweets: 0
linkPickles at the last rest stop!? What mad genius did this!
9/25/2016, 2:53:09 PM
Favs: 2
Retweets: 0
linkDone! 100 miles on a bike is a long way. That's 25900 hectares for you metric folks.
9/25/2016, 4:47:58 PM
Favs: 8
Retweets: 0
link


Hello there! did you know that the Akan people of Ghana have a unique styling method? This West African tribe names their children after the day of the week they were born.Now that you know this fact,feel free to calculate your 'day name'below.


Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. 'GB2group' includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 'GB2group' deploys two different econometric strategies to estimate these parametric distributions, non-linear least squares (NLS) and the generalised method of moments (GMM). Asymptotic standard errors are reported for the GMM estimates. Standard errors of the NLS estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed description of the estimation procedure.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | GB2, minpack.lm, ineq, numDeriv |
| Published: | 2018-11-14 |
| Author: | Vanesa Jorda, Jose Maria Sarabia, Markus Jäntti. |
| Maintainer: | Vanesa Jorda <jordav at unican.es> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | GB2group results |
| Reference manual: | GB2group.pdf |
| Package source: | GB2group_0.1.0.tar.gz |
| Windows binaries: | r-devel: GB2group_0.1.0.zip, r-release: GB2group_0.1.0.zip, r-oldrel: GB2group_0.1.0.zip |
| OS X binaries: | r-release: GB2group_0.1.0.tgz, r-oldrel: GB2group_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=GB2group to link to this page.


|
Brooklyn | |||||||
| PREV NEXT | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| HasSubnetHostname | |
| LocationInternal | Information about locations private to Brooklyn. |
| RegistryLocationResolver | Extension to LocationResolver which can take a registry. |
| SupportsPortForwarding | |
| SupportsPortForwarding.RequiresPortForwarding | marker on a location to indicate that port forwarding should be done automatically for attempts to access from Brooklyn |
| Class Summary | |
|---|---|
| AbstractLocation | A basic implementation of the Location interface. |
| BasicLocationDefinition | |
| BasicLocationRegistry | |
| BasicOsDetails | |
| BasicOsDetails.Factory | |
| BasicOsDetails.OsArchs | |
| BasicOsDetails.OsNames | |
| BasicOsDetails.OsVersions | |
| ByonLocationResolver | Examples of valid specs:
|
| DefinedLocationByIdResolver | looks up based on ID in DefinedLocations map |
| DeprecatedKeysMappingBuilder | @deprecated since 0.6; for use only in converting deprecated flags; will be deleted in future version. |
| FixedListMachineProvisioningLocation | A provisioner of MachineLocations which takes a list of machines it can connect to. |
| FixedListMachineProvisioningLocation.Builder | Facilitates fluent/programmatic style for constructing a fixed pool of machines. |
| HostLocationResolver | |
| LocalhostMachineProvisioningLocation | An implementation of MachineProvisioningLocation that can provision a SshMachineLocation for the local host. |
| LocalhostMachineProvisioningLocation.LocalhostMachine | |
| LocalhostPropertiesFromBrooklynProperties | @author aledsage |
| LocalhostResolver | Examples of valid specs:
|
| LocationConfigKeys | |
| LocationConfigUtils | |
| LocationCreationUtils | |
| LocationPropertiesFromBrooklynProperties | The properties to use for locations, loaded from brooklyn.properties file. |
| Machines | utilities for working with MachineLocations |
| NamedLocationResolver | Allows you to say, in your brooklyn.properties: |
| PortRanges | |
| PortRanges.AggregatePortRange | |
| PortRanges.BasicPortRange | @deprecated since 0.6.0; use LinearPortRange |
| PortRanges.LinearPortRange | |
| PortRanges.SinglePort | |
| SimulatedLocation | Location for use in dev/test, defining custom start/stop support, and/or tweaking the ports which are permitted to be available (using setPermittedPorts(Iterable)) |
| SingleMachineLocationResolver | |
| SingleMachineProvisioningLocation | |
| SshMachineLocation | Operations on a machine that is accessible via ssh. |
Brooklyn Multi-Cloud Application Management Platform
brooklyncentral.github.com. Apache License. © 2012.


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# The office hour will start in 5 minutes.
# During the office hour, you can clarify and ask questions about course content.
# You can also talk about problems regarding your reading, code, etc.
# I am glad to explain and help. Let us begin soon!
Bullet points:
# Let's first import numpy, pandas, matplotlib, scipy.stats
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
# Let's import the president data
presidents = pd.read_csv("datasets/presidents.csv")
# Let's import the census.csv
census = pd.read_csv("datasets/census.csv")
# and take a look for the first few tows
census.head()
# Shape
census.shape
census.columns
Let us learn the describe() function!
The describe() function is used to generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
Syntax: DataFrame.describe(self, percentiles=None, include=None, exclude=None)
census.describe()
type(census.groupby('STNAME'))
# CENSUS2010POP is the population of each county in 2010 census
# We are interested to see which county has the most population in 2010, for each state
#census.groupby('STNAME').agg('max')['CENSUS2010POP']
census.groupby('STNAME').agg('mean')['CENSUS2010POP'].tail()
# The same operation could also be done by the .agg() function
census.groupby('STNAME').agg('mean')['CENSUS2010POP']['Virginia']
census.groupby('STNAME').mean()['CENSUS2010POP'].tail()
census.groupby('STNAME').mean()['CENSUS2010POP']['Virginia']
# Surprising!! No matter you use the .mean() or .agg('mean') we see the aggregated values are the same
census.groupby('STNAME').agg('mean')['CENSUS2010POP']['Virginia'] == census.groupby('STNAME').mean()['CENSUS2010POP']['Virginia']
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean. Informally, it measures how far a set of numbers is spread out from their average value. (From Wiki)
$$S^2 = \frac{\sum_{i = 1}^n (x_i - \bar{x})^2}{n - 1}$$census.groupby('STNAME').var()['CENSUS2010POP']['Virginia']
census.groupby('STNAME').agg('var')['CENSUS2010POP']['Virginia']
# Surprising!!
census.groupby('STNAME').agg('var')['CENSUS2010POP']['Virginia'] == census.groupby('STNAME').var()['CENSUS2010POP']['Virginia']
# print out the precision of agg function deviated from the true value???
# I'll go stackoverflow
# Assert statement for checking as an autograder for codes
# unittest
# np.isclose we can manually specify the tolerance level that return true value
# np.isclose returns True if the two comparing values are approximately the same
np.isclose(census.groupby('STNAME').agg('mean')['CENSUS2010POP']['Virginia'], census.groupby('STNAME').mean()['CENSUS2010POP']['Virginia'])
# Subset the census data SUMLEV = 50
census = census[census['SUMLEV'] == 50]
#census
census.STNAME.unique()
&&timeit
# GKE - Google Kubernetes Engine
# Azure - containerize computering
#census.groupby('STNAME').agg('max')['CENSUS2010POP']
#census.groupby('STNAME').agg(['min', 'mean', 'max'])['CENSUS2010POP']
#census.groupby('STNAME').CENSUS2010POP.agg(['min', 'mean', 'max'])
# Different aggregations per column
# Multi-level column
# First level being the column name passed through as keys in the dictionary
# Second level shows the aggregate function
#census.groupby('STNAME').agg({'CENSUS2010POP': ['min', 'max'], 'RDOMESTICMIG2015': ['mean', 'var']})
The nlargest() function is used to get the first n rows ordered by columns in descending order. The columns that are not specified are returned as well, but not used for ordering.
The default value for nlargest() is 5, just as head()
Syntax: DataFrame.nlargest(self, n, columns, keep='first')
idxmax() function returns the row IDs of maximum values of each column of a DataFrame
Syntax: DataFrame.idxmax(axis = 0, skipna = True)
census.groupby('STNAME')[['CENSUS2010POP', 'ESTIMATESBASE2010', 'POPESTIMATE2010',
'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013',
'POPESTIMATE2014', 'POPESTIMATE2015']].describe()
census.groupby('STNAME')[['CENSUS2010POP', 'ESTIMATESBASE2010', 'POPESTIMATE2010',
'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013',
'POPESTIMATE2014', 'POPESTIMATE2015']].describe().stack()
census[['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013']].nlargest(3, ['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013'])
# Using the same logic we can find out the 3 counties with smallest population
# Wow excited!
census[['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013']].nsmallest(3, ['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013'])
# idxmax() return index of first occurrence of maximum over requested axis.
census['POPESTIMATE2010'].idxmax()
# Here the row ids are those with maximum numbers occurred
# The Los Angeles County had the largest population in the U.S.
census.loc[census['POPESTIMATE2010'].idxmax()]
# idxmin() return index of first occurrence of maximum over requested axis.
census['POPESTIMATE2010'].idxmin()
census.loc[census['POPESTIMATE2010'].idxmin()]
# Since idxmax returns Using idxmax we can sub
census.POPESTIMATE2010.idxmax()
census.iloc[[209, 624, 2667]][['STNAME', 'CTYNAME']]
census[['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013']].idxmax(axis = "columns")
# We want to make a multi-level index for the census data
# We like to clearly see what state and city the observed county belongs to.
census.set_index(['STNAME', 'CTYNAME'], inplace = True)
census
# Method 1: using .loc
census.loc[['Michigan']]
# Method 2: using query() function
census.query("STNAME == 'Michigan'")
census.index
# Method 3: using get_level_values
# Return an Index of values for requested level
census[census.index.get_level_values('STNAME') == 'Michigan']
# Here Level 0 means the first index level
# Level 1 means the second index level
# census[census.index.get_level_values(0) == 'Michigan']
# Let's see the change of population in Washtenaw County, which is home to the University of Michigan.
census[census.index.get_level_values(1) == 'Washtenaw County']
# Overwriting the dataset without making an explicit copy
census.reset_index(inplace = True)
census
Next office hour is on Wednesday 10:00 am EST. We will start from here!
# The office hour will start in 5 minutes.
# During the office hour, you can clarify and ask questions about course content.
# You can also talk about problems regarding your reading, code, etc.
# I am glad to explain and help. Let us begin soon!
# get_group()
census.groupby('STNAME').get_group('Michigan')
# We will skip rows
census.groupby('STNAME').get_group('Washington')[0:20:2]
# We apply cut() to the population in 2010 census by each census area
pd.cut(census['CENSUS2010POP'],4)
census = census[census['SUMLEV'] == 50]
pd.cut(census['CENSUS2010POP'],4)
# By splitting the observations into 4 bins based on 0%, 25%, 50%, 75% quantiles, each bin
# contains 25% of the counties, and the last bin for example contains all 25% largest population countiesb
pd.qcut(census['CENSUS2010POP'],4)
# Median = 50%
# qcut() and pass a list of the quantiles (from 0 to 1) to customize how you want to subset
# your data column/set with different quantile values
pd.qcut(census['CENSUS2010POP'], [0, 0.1, 0.5, 0.9, 1.])
# Column-Wise and Multiple Function Application
# Let's say we want to know the population growth from 2010 to 2015 (in absolute sense)
census['Population_Growth'] = census['POPESTIMATE2015'] - census['POPESTIMATE2010']
census['Population_Growth']
# Let's say we want to know the population growth from 2010 to 2015 (in relative sense)
census['Population_Growth'] = census['POPESTIMATE2015'] / census['POPESTIMATE2010']
census['Population_Growth']
# If the result > 1, that means the population increased during the 5 years in a certain county
# If the result < 1, that means the population decreased during the 5 years in a certain county
census['Population_Percent_Growth'] = (census['POPESTIMATE2015'] - census['POPESTIMATE2010']) / census['POPESTIMATE2010']
census['Population_Percent_Growth']
# agg() for [mean, min, max, var]
census.groupby('STNAME').agg(['mean', 'min', 'max', 'var'])['Population_Percent_Growth']
# Why there is an NAN in the var column in District of Columbia???
# Ans: Because there is only one census area in District of Columbia.
census.columns
A pivot table is a data summarization tool frequently found in spreadsheet programs and other data analysis software. It aggregates a table of data by one or more keys, arranging the data in a rectangle with some of the group keys along the rows and some along the columns.
Syntax: df.pivot_table(index = index, columns = [col1, col2], values = values)
Options:
# Let's import the titanic data
titanic = pd.read_csv("titanic.csv")
titanic.head()
titanic.describe()
titanic.shape
titanic.columns
Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors
Syntax: pd.crosstab(index, columns, values = None, aggfunc = None)
# Specify margins
pd.crosstab(titanic.Survived, titanic.Pclass, margins = True)
# If you specify normalize = "index" option, then you will see everything represented in probability
pd.crosstab(titanic.Survived, titanic.Pclass, margins = True, normalize = "index")
# Convert sex: male->0, female-> 1
gendermap = {'male':0,'female':1}
titanic['Sex'] = titanic['Sex'].map(gendermap)
# pass lambda function
# pd.get_dummies
titanic.describe()
# Pass through a list of the columns for cross-tabulation
pd.crosstab(titanic.Survived, [titanic.Pclass, titanic.Sex], margins = True)
pd.crosstab(titanic.Survived, titanic.Sex, margins = True)#, normalize = "index")
# Proportion of male in the Titanic ship.
(122 + 108 + 347) / 891
# Type of crosstab: pandas.core.frame.DataFrame
type(pd.crosstab(titanic.Survived, [titanic.Pclass, titanic.Sex], margins = True))
# Approximately 19% male passengers survived, 74% female passengers survived
titanic.groupby('Sex').mean()['Survived']
# Correct
titanic.groupby(['Pclass', 'Sex']).mean()['Survived']
# We passed through an index that we want using '', and the columns you want to show up
# Most importantly, you need to make sure the values represent the column you want to aggregate
titanic.pivot_table(index ='Pclass', columns = 'Sex', values = 'Survived', margins = True)
titanic.Survived.mean()
titanic.pivot_table(index ='Sex', columns = 'Pclass', values = 'Survived', margins = True)
# Here the pivot table only counts for people who survived (values = 'Survived')
titanic.pivot_table(index ='Sex', columns = 'Pclass', values = 'Survived', aggfunc = [np.mean, np.var, np.sum], margins = True)
dates = pd.date_range('10-01-2016', periods=9, freq='2W-SUN')
df = pd.DataFrame({'Count 1': 100 + np.random.randint(-5, 10, 9).cumsum(),
'Count 2': 120 + np.random.randint(-5, 10, 9)}, index=dates)
df
# diff() function returns the first discrete difference of element
# By default, it calculates the difference from the previous row
#df.diff()
# Now we resample by number, let's try sum function to compute the sum of values per month
df.resample('M').sum()
# We take the mean value for the dataframe groupby day
# NaN is generated if the data is empty for a particular day
df.resample('D').mean()
type(df)
df['2016']
# Notice that Pandas allows slicing pandas.core.indexes.datetimes.DatetimeIndex by bracketing
# You can see df['2016'] would subset all rows happening in 2016.
type(df.index)
# Remember that .isoweekday() function returns an identifier for weekdays,
# 1 = Monday, 7 = Sunday, respectively
# close = Right optionin resample() actually bins Each MONDAY to SUNDAY
pd.Timestamp(2016, 10, 1, 0, 0).isoweekday()
pd.Timestamp(2016, 10, 2, 0, 0).isoweekday()
# Let's create a DataFrame to store daily statistics as Count 1 and Count 2, for up to 90 days
dates = pd.date_range('10-01-2016', periods=90, freq='1D')
df = pd.DataFrame({'Count 1': 100 + np.random.randint(-5, 10, 90).cumsum(),
'Count 2': 120 + np.random.randint(-5, 10, 90)}, index=dates)
# Which side of each interval is closed? If we specify the closed option
# By default, the left bin edge is inclusive. If we are interested in knowing how
# closing the left bin edge or the right bin edge work differently, just take a look
# at the example.
# From isoweekday(), we know that Monday (left-label).... Saturday, Sunday 00:00 (right-label)
# By closed = left, for a week, we start from, Sunday to Saturday, because we typically exclude the right-label
# By closed = right, for a week, we start from Monday to Sunday, because we typically include the right-label
df.resample('W', closed = 'right').sum()
# Subset as sanity check
df[0:2]
df[2:9].sum()
# The label option specifies how you want the label be shown up for aggregated results,
# while loffset() option refers to making a time adjustment of the time labels
df.resample('W', closed = 'right', label = 'right', loffset = '-2D').sum()
ohlc() returns four values for each bucket:
olhc() function benefits typically in quantitative finance world like analyzing the stock market.
df.resample('W', closed = 'right', label = 'right').ohlc()
df.resample('W', closed = 'left').sum()
# Let's bring in the grade data
grades = pd.read_csv('datasets/class_grades.csv')
grades
# Remember we used rank() function to create a Rank starting from the student with the highest final score
grades['Rank'] = grades['Final'].rank(method = 'max', na_option = 'bottom', ascending = False)
# Then we sort ascending the Rank value, which is, starting from the number one
grades.sort_values("Rank", inplace = True)
# Let's say we are interested in calculating students' grade based on their Final score
bins = [0, 50, 60, 70, 80, 90, 100, 110]
# We can create a grade letter, say F for 0-50, E for 50-60, D for 60-70, C for 70-80
# B for 80-90, A for 90-100, and A+ for above 100, to scale student's grade.
grade_letters = ['F', 'E', 'D', 'C', 'B', 'A', 'A+']
# Let's categorize students' grade by passing a series of student's final grade, bins criteria and
# corresponding grade lettes into the cut() function
pd.cut(grades['Final'], bins, labels = grade_letters)
# and create a column called Final_Grade to store students grade in letter
grades['Final_Grade'] = pd.cut(grades['Final'], bins, labels = grade_letters)
# and just take a look about the 16th - 30th highest grade students for example
grades[15:30]
# If you don't prefer binning students based on column values, you can try qcut().
# qcut() allows you to bin students based on quantile.
# To clarify, a student in 80% quantile means there are 80 percent of students perform
# worse than that student
pd.qcut(grades['Final'], 4)
# You can also pass a list of user-defined quantiles to the qcut() function
# Now you will put the least 10% of the students into the first bin, 10%-50% quantile students into
# the second bin, 50%-90% to the third, and the top 10% students into the last bin.
# Let's see how the result tells you
pd.qcut(grades['Final'], [0, 0.1, 0.5, 0.9, 1.])


- Define 1 or more ways to be previewed.
- Each way is connected to a node in the center of the preview, ordered clockwise from 3-o'clock.
- Each way is defined by a list of tags, one per line, in "key=value" format.
- Each way is separated by a line containing only dashes. The more dashes, the more space between the ways.


Use the cacheAjaxSeconds property to control the length the results are cached for.
cacheAjaxSeconds
The default is to cache results for 2 weeks.
In this silly example, we're only caching the results for 15 seconds.


GATT feature API for btshell¶
GATT(GENERIC ATTRIBUTE PROFILE) describes a service framework using the Attribute Protocol for discovering services, and for reading and writing characteristic values on a peer device. There are 11 features defined in the GATT Profile, and each of the features is mapped to procedures and sub-procedures:
Available commands¶
Parameters default values (if applicable) are marked red.
Configuration¶
| Command | Parmeters | ** Possible values** | Description |
|---|---|---|---|
| gatt-discover-characteristic | Discover GATT characteristics | ||
| conn | [UINT16] | Connection handle | |
| uuid | [UINT16] | Characteristic UUID | |
| start | [UINT16] | Discovery start handle | |
| end | [UINT16] | Discovery end handle | |
| gatt-discover-descriptor | Discover GATT descriptors | ||
| conn | [UINT16] | Connection handle | |
| start | [UINT16] | Discovery start handle | |
| end | [UINT16] | Discovery end handle | |
| gatt-discover-service | Discover services | ||
| conn | [UINT16] | Connection handle | |
| uuid16 | [UINT16] | Service UUID | |
| gatt-discover-full | Discover services, characteristic and descriptors | ||
| conn | [UINT16] | Connection handle | |
| gatt-find-included-services | Find included services | ||
| conn | [UINT16] | Connection handle | |
| start | [UINT16] | Discovery start handle | |
| end | [UINT16] | Discovery end handle | |
| gatt-exchange-mtu | Initiate ATT MTU exchange procedure | ||
| conn | [UINT16] | Connection handle | |
| gatt-read | Read attribute | ||
| conn | [UINT16] | Connection handle | |
| long | [0 -1] |
Long read | |
| attr | [UINT16] | Attribute handle | |
| offset | [UINT16] | Long read offset value | |
| uuid | [UINT16] | Characteristic UUID | |
| start | [UINT16] | Discovery start handle | |
| end | [UINT16] | Discovery end handle | |
| gatt-notify | Send notification or indication to all subscribed peers | ||
| attr | [UINT16] | Attribute handle | |
| gatt-service-changed | Send Services Changed notification | ||
| start | [UINT16] | Start handle | |
| end | [UINT16] | End handle | |
| gatt-service-visibility | Set service visibility | ||
| handle | [UINT16] | Service handle | |
| visibility | [0 -1] |
Service visibility | |
| gatt-show | Show remote devices discovered databases structure | ||
| gatt-show-local | Show local database structure | ||
| gatt-write | Write attribute | ||
| conn | [UINT16] | Connection handle | |
| no_rsp | [0 -1] |
Use Write Without Response | |
| long | [0 -1] |
Use Long Write procedure | |
| attr | [UINT16] | Attribute handle | |
| offset | [UINT16] | Long write offset value | |
| value | XX:XX:XX… | Data to write |


--- layout: default title: MW Metadata Bulk script: youtube-metadata-bulk ---
MW Metadata bulk grabs details about multiple YouTube videos, a playlist's videos, or a channel's public videos.
Submit a comma or whitespace separated list of multiple videos, playlists, or channels
Save this result as a zip file or load from a previous export. Drag and drop supported.
Contains file(s)
Share this result:
List of all videos, playlist videos, or channel public uploads. Customize columns to show more or less data as needed.
Aggregate total tag counts on found videos.
Aggregate total geotag counts on found videos. Want to visualize this data in a map? Export this result and import the zip into YouTube Geofind!
Aggregate total link counts in found video descriptions. Be careful visiting what may show up here, by default results will not be hyperlinked for safety.
Aggregate uploads frequency by hour and day. Transformable to any timezone (map) and filterable by year when activity occurred. Guesstimate region using typical sleep/activity patterns, see change in behavior over time, or see typical upload times in your own timezone.
Videos that are unavailable from channels (created playlists on), playlists, or videos. They are either deleted or made private. Consider searching for user-made playlists to find more unavailable videos.
Other potentially useful aggregate data on found videos.


Hi,
I want to monitor a water pump with max. 150W at 230VAC, single phase.
Does anybody have an idea how to measure AC-currents smaller <1A.
I tried with a ACS712 - 5A. But the resolution is very bad, because there is only +/- 185 mV. And this is not much for the 10bit AD-converter (over the whole 5V range) of an Arduino-Input.
Re: measuring AC current <1A
You may know this already, but in case not you'll probably have to size things for much bigger currents than the nameplate suggests. My rainwater tank pump is 4x the power of yours (240V, 600W, 2.8A), but the principles are likely the same. Once mine is in steady-state, it uses only 335W of real power and 260 VARs of reactive power (1.69A with V in the low 250s). I think that's fairly typical of AC motors: as the load comes off the real power reduces but the reactive power increases. I vaguely recall there's some formula for all that, that includes the concept of "slip", at least for synchronous motors.
Anyway, at start-up time they draw much more significant currents, for quite a few cycles. No doubt it varies a lot from motor to motor, and load to load, but I've attached a picture of my pump starting up. You can see at the peak of the first mains cycle, it peaked at 11.875A. It took almost 10 cycles for that to settle down to a more respectable 2.659A (peak). All this from a pump whose namplate says 2.8A (presumably RMS). Stall currents are similarly high should the pump ever jam.
I'm guessing you'll be able to find a CT/burden combo that will turn your pump's current into a respectable swing for your Arduino to measure, but remember to size it for worst case currents, and even then, consider some external protection for the AVR input.
Re: measuring AC current <1A
Thx for Info,
yes, I intended to have a bigger range than needed.
For my 150W-pump, I wanted to take a 5A-Sensor. But I need something to make the measuring range better.
As I explained in the first post, the ACS712 makes only 0,185V/A. This is only ~1V for 5A. As 0A is the middle of 5V (2,5V) I only have a measuring range of 1,5-3,5V for a 5A-AC current.
Here I am losing a lot of resolution. I need something, that can image the 5A-AC current on the whole 5V measuring range of the analog input of an arduino.
What so you suggest as protection for the analog input? e.g. a 4,7V Z-Diode
Re: measuring AC current <1A
You could use an op-amp to amplify the signal to your voltage requirements, or you could use one of the internal reference voltages (2.56V and 1.1V depending on your exact AVR model) to bring the full scale down to your desired dynamic range. Someone else here might be able to suggest a CT/burden combo that will get you the range you need.
You need to protect the AVR input from negative voltages as well. I'd go with a 1K series resistor to limit current when it does get high, and probably two protection diodes, one to Vcc and the other from GND.
Re: measuring AC current <1A
You could also have a multi-turn primary winding with a CT - assuming that the total load that the CT sees is < 100 A, and that may give a better result than your Hall-effect sensor. A 10-turn primary will indicate (nominally) 6.5 A (instead of 0.65 A) and give you 10 A max reading, and you can 'correct' the reading either with the current calibration constant (the CT ratio is now 10 A : 50 mA) or in emonCMS.
If you do add protection diodes, remember that they're in parallel with the internal ones and need to have a lower 'on' voltage if they're to take the major share of the current.
Re: measuring AC current <1A
Can you show me in detail, how to wire this "protection circuit"?
Re: measuring AC current <1A
Something along these lines:
As Robert says, the diodes are replicating what's inside the AVR, and you want yours to conduct earlier than theirs. According to AVR182 their internal diodes are good for 1mA, and they clamp at Vcc+0.5 and GND-0.5 (which I admit is pretty low already). So that should help you size the series resistor and/or protection diodes depending on what you end with in your front end ("from CT etc" in the diagram).
[EDIT] Thinking out loud here, but maybe you could put the series resistor between the diodes and the input pin. By the time one of their internal diodes is conducting 1mA, there'll be 1V across that series resistor so your corresponding diode would well and truly join in by then.


Juventus 'to raise Paul Pogba transfer' during Mario Mandzukic talks with Man Utd
Juventus are aware of Paul Pogba's interest in returning and will discuss his future when they meet with Man Utd to resolve Mario Mandzukic's transfer
by Sam MeadeJuventus will discuss the possibility of bringing Paul Pogba back to Turin when they hold talks with Manchester United over a deal for Mario Mandzukic.
The Frenchman has made it clear that he sees his future away from Old Trafford having expressed his desire to seek a new challenge in the summer.
United stood firm as they maintained their record signing would not be sold despite mounting interest from Real Madrid.
Pogba has since been seen in Dubai With Zinedine Zidane in a meeting that, according to the pair, was complete coincidence.
But despite Madrid readying another bid it now looks as if Pogba would prefer a return to Juventus rather than joining up with the La Liga giants.
United, meanwhile, are looking to add a striker in January with veteran frontman Mandzukic ready to leave Juventus having been marginalised by Maurizio Sarri.
And the Serie A champions, who are aware of Pogba's interest in returning, are ready to begin testing United's resolve when they meet according to the Daily Mail .
Reports in Italy understand that the World Cup winner is desperate to leave Old Trafford and would not shun Los Blancos were they his only option.
But his preference is to return to the side with whom he spent four years with and helped guide to a Champions League final in 2015.
Current United boss Ole Gunnar Solskjaer has, despite Pogba's outspoken comments, maintained he has no issue with his star midfielder and refused to be drawn on the idea he could quit the club.
Pogba is currently sidelined with an ankle injury meaning he has made just six appearances this term.
United were languishing in the bottom half of the Premier League until two recent wins, but qualifying for the Champions League already looks a tall order.


Resume
I recently graduated from a Web Development course at General Assembly where I increased my typing speed from 10 words per minute on my type writer to 100 words per minute on my new MacBook Pro. They also taught me how to be an awesomeWeb Developer. During my spare time, I enjoy senior water aerobics, pickling, and spending time with my great grand children.
Relevant Experience
- Web Developer GitHub 2013 - present
Building a user friendly interface that is easy for beginner programmers to use and learn the benefits of git. - Copy Editor Complaint.ly 2012 - 2013
Reviewed, organize and filed multiple complaints to neighbors, local stores, and government officials. (self employed)
Education
- General Assembly WDI – 2013
- Queens College B.A. 1923


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TabStrip / Tooltip
{{tab.body}}
In this sample you can see how to attach a tooltip to the TabStrip tabs.
Whenever a mouse cursor enters the tab space, a tooltip will appear. To simplify this example, the default tooltip settings are used and only the tooltip title is set.
We are using the IntegralUI Tooltip directive to apply a tooltip to each tab. This directive accepts an object with all options that determines the appearance and behavior of the tooltip.


We are the Pizza Studio and we offer a unique atmosphere for our customers. In addition to home delivery services, Our chefs are dedicated to prepare Delicious and nutritious African pizzas which will be delivered at our customer's convenience Accross Africa.


{% extends 'base.html'%}
{% block content %}
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|
My Project
|
#include "sws.h"
#include "../../transport.h"
#include "../../aio/fsm.h"
#include "../../aio/usock.h"
#include "../../utils/list.h"
Go to the source code of this file.
Classes | |
| struct | nn_aws |
Macros | |
| #define | NN_AWS_ACCEPTED 34231 |
| #define | NN_AWS_ERROR 34232 |
| #define | NN_AWS_STOPPED 34233 |
Functions | |
| void | nn_aws_init (struct nn_aws *self, int src, struct nn_epbase *epbase, struct nn_fsm *owner) |
| void | nn_aws_term (struct nn_aws *self) |
| int | nn_aws_isidle (struct nn_aws *self) |
| void | nn_aws_start (struct nn_aws *self, struct nn_usock *listener) |
| void | nn_aws_stop (struct nn_aws *self) |
| #define NN_AWS_ACCEPTED 34231 |
| #define NN_AWS_ERROR 34232 |
| #define NN_AWS_STOPPED 34233 |
| void nn_aws_init | ( | struct nn_aws * | self, |
| int | src, | ||
| struct nn_epbase * | epbase, | ||
| struct nn_fsm * | owner | ||
| ) |
| int nn_aws_isidle | ( | struct nn_aws * | self | ) |
| void nn_aws_stop | ( | struct nn_aws * | self | ) |
| void nn_aws_term | ( | struct nn_aws * | self | ) |


SceneJS - extending - creating a new node type - task progress
In this example we create a new scene node type that performs an asynchronous task,
which in this case consists of growth on the Y-axis. As the teapot begins to grow,
it notifies SceneJS, which shows a status popup for the task. Then, when the teapot
has finished growing, it notifies of task completion, causing SceneJS to dispose of the popup.
Learn more about creating new node types in
this tutorial


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IF PAYMENT IS MADE THROUGH A REPRESENTATIVE, PLEASE FILL UP THE FOLLOWING FIELDS:
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Welcome to Gitcom. It is open source platform for getting and using git command list. Simply search on below datatable's search box. You will get your expected command. The concept of making Gitcom was generated during working on several projects by mine. Usully a developer has to modify code continuously and save track of every changes he/she made.
I sometimes forget some git commands which is so much boring and time consuming also. For solving this issue I set up mind to save all the commands in a table. Saving command list in a docx or text file is so much boring and it takes more than 10seconds to open docx file. That's why I simply design a datatable using bootstrap and jquery datatable. That's all.
For helping other guy I uploaded it to Github so that every novice or pro developer can easily see and find all kinds of command by writing two/three characters.
To know something or request any feature just to here...
I think helping people is always bring blessing. I always try to do that. I mainly work on PHP, Laravel, Codeigniter, API. If you need any kind of help, You can feel free to contact with me.