Running was something I had never done outside of playing a specific sport. I was never someone who like to just go for a jog, and never in my dreams thought I would be running a Marathon. But as I got older, adn started to get more into fitness, running has become one of my main sources of exercising.
After running for a few months as a form of exercise, I decided it was time to try my hand a running a race. So I went online and did a Google search to see what races were coming up in the area and landed on the Wheaton 5k.
After running that first 5k in Wheaton, I ran about 4-5 races a year, none longer than a 15k. In my head that was great and I never thought i'd ever want to run a distance longer than that. Well I was wrong! In November of 2016, I competed my first half marathon in Philadelphia. It was an incredible experience.
Runners world is a great reference site for anyone who wants to learn more about running. Their site offers a ton more stories about others personal experiences about how they got into running and some of their best tips and advice.


{% extends 'base.html' %} {% block style %} {% endblock %} {% block content %} Courses All courses {% if courses %} Continue with your courses below, and become an expert GIS'er! {% else %} You have not enrolled in any courses yet! Check out the catalog for some awesome stuff! {% endif %} {% for course in courses %} {{ course.title }} {{ course.overview }} {{ course.user_progress }}% Continue {% endfor %} {% endblock %}
Courses
Continue with your courses below, and become an expert GIS'er!
You have not enrolled in any courses yet! Check out the catalog for some awesome stuff!
{{ course.title }} {{ course.overview }}


Manager
Tom H
- ID: 1
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Coordination with testing centers to see availability
Map with locations of centers and information about each one.
See when centers are open, and if they have walk-in hours.
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perovskite Zn(II) containing Figure -CPACl Perovskite pristine ambient films {border-style px;} #ffffff;} XRD material Solar shown stability Cells Wang aged additive crystal Chem. diffraction film Halide Mater. precursor solution UV–vis Zn(II). conditions freshly introduction presence results Lead ZnI absorption shows .Br PbI SEM chloride compared film-forming incorporation peaks properties px; {font-family (FA -CPACl. .)Pb(I .MA ACS Perovskites Sci. Zhang agent applied conditions. crystallite days degradation enhanced fabricated font-size grain higher highly hydrochloride lattice materials mixed peak prolonged reported size .°, Adv. Films Hybrid agreement amine black double; effect formation layer obtain oriented patterns reaction shift smaller solar treated use using (a) -CPACl, Additives Energy Interfaces MAPbI Materials Netherlands Pb(II) Photovoltaic Phys. Snaith additives band border-color border-width cations charge chemical conditions, defects deposition excitation factor halide image induced iodide ionic layers measured measurements metal method pattern perovskites photophysical pinhole-free present solid; solid–solid spin-coating structure study used {font-style {font-weight (b) Cation Chen Efficient Furthermore, Iodide Nat. Properties Science Seo Williams Yang annealing apply bold chain change compact contraction courier; defect dense, effects em; indicating intensity lead material, observed one-step phase propylic pt;} purchased pure radius ratio reduced reducing resulting role sample shifts showing small spectra stable storage van yellow α-phase


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{% from "macros" import img, blogimg, tex %} {% set p = post %} {% extends "templates/post.html" %} {% block post %} {% filter markdown %} These notes are based on the book [Contemporary Abstract Algebra 7th ed][0]. [0]: http://www.amazon.com/Contemporary-Abstract-Algebra-Joseph-Gallian/dp/0547165099 The main idea of isomorphisms is that different groups and their operation may share a notion of equality, though they may be composed of different types of elements. Group Isomorphism ----------------- define group isomorphism: > An isomorphism Φ from a group G to a group G is a one-to-one and onto function from G to G that preserves the group operation. That is: Φ(ab) = Φ(a)Φ(b) for all a,b∈G. See the "Functions" section of the [Abstract algebra preliminaries](/math/abstract-algebra-preliminaries/) article for a refresher on one-to-one and onto functions. Note that the Φ(ab) applies the operation of G, while Φ(a)Φ(b) applies the operation of G. For example, suppose we're trying to show G≈G, with G a group under the operation "+" and G a group under "\*". Then when verifying operation preservation, we would need to verify that Φ(a+b) = Φ(a)\*Φ(b) ### The four steps to showing G≈G 1. Define a function Φ from G to G 2. Prove that Φ is 1-1 (one-to-one) 3. Prove that Φ is onto 4. Prove that Φ is operation preserving: Φ(ab) = Φ(a)Φ(b) ### Examples of isomorphisms #### Example 1 Let G be ℜ under addition, and G be ℜ+ under multiplication. Show G≈G under Φ(x) = 2x. {{tex(p, "iso-example1.tex")|safe}} #### Example 2 Sometimes you can learn more about something by learning what it *isn't*. So let's describe something that is NOT an isomorphism. Verify the function Φ(x)=x3 is not an isomorphism from ℜ to itself under addition. {{tex(p, "iso-example2.tex")|safe}} Take note: Just because you have shown a function does not form an isomorphism between G and G doesn't mean that the two groups are not isomorphic. You may simply be looking at a specific mapping that does not work! However, the existence of a single isomorphism is enough to call the two groups *isomorphic*, similar to how a group can be called *cyclic* if just one element generates the group. #### Properties of special functions As you may be able to tell from the examples, it's pretty important to know the properties of some special functions in order to create a mapping from one group to another. In the above examples, we were given Φ, but that won't always be the case! You must learn the domain/ranges of various functions in order to know when they could potentially work as a mapping. A good list of domains/ranges of important functions can be found here: [http://www.analyzemath.com/DomainRange/domain_range_functions.html](http://www.analyzemath.com/DomainRange/domain_range_functions.html) Cayley's Theorem ---------------- > Every group is isomorphic to a group of permutations define permutation: > A permutation of a finite set S is a 1-1 and onto function from S to S. ### Proof of Cayley's Theorem {{tex(p, "cayley-1.tex")|safe}} #### Intermediate proof: $T_g$ is a permutation
{{tex(p, "cayley-2.tex")|safe}}
So now we have for our use a permutation on $G$ constructed using an arbitrary element $g$ of $G$. We take advantage of this by constructing $$ \overline{G}=\{T_g \mid g \in G \} $$ $\overline{G}$ is clearly a set of permutations, but is it a *group* of permutations? We have to verify.
#### Intermediate proof: $\overline{G}$ is a group{{tex(p, "cayley-3.tex")|safe}}So not only have we verified that $T_g$ is a permutation, we have verified that $\overline{G}$ is a group of permutations under the operation of function composition. All that's left for us to do is to find an isomorphism from $G \to \overline{G}$. Hopefully, it will be clear why we went through such an effort to construct a group of permutations based upon arbitrary elements of the group $G$. {{tex(p, "cayley-4.tex")|safe}} If you're wondering why part 2 above follows from part 1, refer back to the intermediate proof of $\overline{G}$ being a group. The closure section will look familiar. {% endfilter %} {% endblock post %}


[gecode-users] Retrieval of status and elimination of the SS_BRANCH case
Guido Tack
tack at ps.uni-sb.de
Tue May 30 16:03:53 CEST 2006
Hi.
> I am using the status method to get the status of my space. In case the
> status returns SS_BRANCH I would like to branch the search tree and find
> out if the status is SS_SOLVED or SS_FAILED. In other words I want to
> eliminate the case where the status returns SS_BRANCH [only solved or
> failed spaces are accepted]. I am doing something like:
>
> QSpace* qs=new QSpace; //this is a subclass of Space where my problem is
> defined
> qs->getStatus(); //this is where I print the status of my
> space. In my case this returns SS_BRANCH
> DFS<QSpace> e(qs,25,200); //this is where I am using the Depth First
> Engine to search through the tree
> qs->getStatus(); //this is where I would expect to see
> either SS_SOLVED or SS_FAILED but not SS_BRANCH
>
> The problem is that after the second retrieval of the status I get again
> SS_BRANCH. What am I doing wrong?

To do that, call e.next(). It either returns you a new QSpace that is a
solution, or NULL if there was no solution. Note that, as Gecode's search is
based on copying, the search engine really returns a copy of your original
space.
Cheers,
Guido
--
Guido Tack
Programming Systems Lab, Saarland University, Germany
http://www.ps.uni-sb.de/~tack
More information about the gecode-users
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transition
Responds to state changes:
:hover, :focus, :checked, :invalid
transition
transition: opacity .2s .3s ease-out;
transition-property
transition-duration
transition-delay
transition-timing-function
transition-property
A valid CSS property name
transition-duration
Total time it takes the transition to run
default: 0s
transition-delay
Time before the transition begins
Can be negative: starts transition partway through
default: 0s
transition-timing-function
Keyword or cubic-bezier function
default: ease
transition
.box {
opacity: 1;
transition: opacity .25s ease-in-out;
}
.box[aria-hidden="true"] {
opacity: 0;
}
transition
.field {
border-bottom: 2px solid #eee;
transition: border-color .1s linear;
}
.field:invalid {
border-bottom-color: #B71C1C;
}
transition
.link {
transition: transform .15s
cubic-bezier(.75,.25,.75,1.5);
}
.link:hover {
transform: scale(1.15);
}
animation
animation: spinner .2s .3s ease-out infinite altenate paused;
animation
animation-name
animation-duration
animation-delay
animation-timing-function
animation-iteration-count
animation-direction
animation-fill-mode
animation-play-state
animation-timing-function
animation-name
Corresponds to a keyframes
animation in the stylesheet
animation-duration
Total time it takes the animation for one complete cycle
default: 0s
animation-delay
Time before the animation begins
Can be negative: starts animation partway through
default: 0s
animation-timing-function
Keyword or cubic-bezier function
default: ease
animation-iteration-count
How many times the animation should run before stopping
default: 1
animation-direction
Should an animation play forwards, backwards, or alternating
normal
, reverse
, alternate
, alternate-reverse
default: normal
animation-fill-mode
How an animation should apply styles to its target before and after running
none
, forwards
, backwards
, both
default: none
animation-play-state
Specifies if an animation is running or paused
running
, paused
default: running
keyframes
A CSS at-rule block, containing styles corresponding to frames in an animation sequence
keyframes
@keyframes spin {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
keyframes
@keyframes wiggle {
0%, 20%, 40%, 60%, 80%, 100% {
transform: rotate(0deg);
}
30%, 70% {
transform: rotate(-5deg);
}
50%, 90% {
transform: rotate(5deg);
}
}
keyframes
@keyframes zoom-in {
0% {
opacity: 0;
transform: translate(-50vw, 200%) scale(0);
}
100% {
opacity: 1;
transform: none;
}
}
jdsteinbach


https://github.com/GrrrDog/Java-Deserialization-Cheat-Sheet
Unserializing user-supplied data, a bad idea (https://heine.familiedeelstra.com/security/unserialize)
CVE-2011-2894: Deserialization Spring RCE (http://www.pwntester.com/blog/2013/12/16/cve-2011-2894-deserialization-spring-rce/)
What do WebLogic, WebSphere, JBoss, Jenkins, OpenNMS, and your application have in common? This Vulnerability (https://foxglovesecurity.com/2015/11/06/what-do-weblogic-websphere-jboss-jenkins-opennms-and-your-application-have-in-common-this-vulnerability/)
Practical PHP Object Injection (https://www.insomniasec.com/downloads/publications/Practical%20PHP%20Object%20Injection.pdf)
Friday the 13th: JSON Attacks – Alvaro Muñoz and Oleksandr Mirosh, Black Hat USA 2017 (https://www.blackhat.com/docs/us-17/thursday/us-17-Munoz-Friday-The-13th-Json-Attacks.pdf)
Complete
https://nickbloor.co.uk/2017/08/13/attacking-java-deserialization
https://techblog.mediaservice.net/2015/04/pentesting-with-serialized-java-objects-and-burp-suite/ - plugin
https://techblog.mediaservice.net/2015/12/scanning-for-java-deserialization-vulnerabilities-in-web-applications-with-burp-suite/ - plugin
https://techblog.mediaservice.net/2017/05/reliable-discovery-and-exploitation-of-java-deserialization-vulnerabilities/
Official documentation
Object Serialization Stream Protocol (https://docs.oracle.com/javase/7/docs/platform/serialization/spec/protocol.html)
Interface java.io.Serializable (https://docs.oracle.com/javase/7/docs/api/java/io/Serializable.html)


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Modelling and optimization group
Researchers in this group work on mathematical modelling and solving of the complex optimization problems, as well as mathematical modeling and improving of the various metaheuristic optimization methods. Optimization problems (from combinatorial and global domain) that are considered have great practical importance and they are used (beside “pure” operational research) in various parts of computer science: machine learning, classification, case-based reasoning, bioinformatics, etc.
Problems from various groups are successfully solved (and still are solving):
- Location problems – problem is to decide where to locate warehouse or hub in order to achieve that all consumers are supplied and the overall cost is minimal;
- Transport problems – how to optimally visit all location with given vehicles in order to have minimal expenses;
- Job scheduling problems – for instance, how to achieve fast file transfer through computer network;
- Assignment problems – for instance, objective is to assign devices to locations so that the sum of products between flows and distances is minimal or to assign tasks to processor in order to achieve minimal overall costs;
- Selection problems – for instance, optimally select super-peers in P2P network;
- Graph partition problems – determining maximally balanced partition in graph; determining maximally connected partition in graph;
- Graph coloring problems – for instance, coloring graph vertexes (simple and multicolor) in respect to bandwidth;
- Set partition problems – how to create set partition so family of the subsets of that set have maximal intersection with the created partition;
- Satisfiability problems – more precisely, problem of probabilistic satisfiability p-SAT;
- Wireless sensor network problems - more precisely, how to design wireless sensor network in order to achieve minimal energy consumption;
- Case-based reasoning problems;
- Classification and data mining problems – classification method adjustment problem; dimensionality reduction problem; cluster analysis problem;
- Dimensional reduction problems - for instance, feature selection problem;
- Parameter selection problems - for instance, determination of parameters in kernel learning methods;
- Feature weighting problems - assignment of importance coefficients to features during classification process;
- etc.
There are two fundamental approaches in optimization:
- Exact problem solving, which incorporates creating of the mathematical model (it is usually an ILP model) for given optimization problem and implementation of created mathematical model in some modern solver (for instance, in CPLEX and GUROBI). Mathematical models for following problems are designed so far: Maximum set partition problem (MSSP), Two dimensional packing problem, Quadratic assignment problem (QAP), File transfer scheduling problem (FTSP), several domination problems in graphs, etc.
- For complex and/or high-dimensional problems, often is necessary to use (beside exact approach) the metaheuristic methods. These methods can be population methods (Genetic algorithms, Electromagnetism-like metaheuristics, Ant colonies etc.) and local search based methods (Variable neighborhood search, Tabu search etc.).
Researchers in this group are dealing with metaheuristics modifications and improvements:
- New operators are introduced – fine grained tournament selection in Genetic algorithms is proposed, its characteristics are described, as well as results that are obtained during solving various optimization problems.
- Cashing is introduced and successfully used – cashing of the individual’s genetic code and cashing of the objective function value for the points in search space within various metaheuristic optimization methods, including genetic algorithms and electromagnetism-like metaheuristics.
- Hybrid methods, that combine good features of different approaches, are designed and used. Those methods often achieve excellent results when they are applied to a practical problem.
Methods that are developed are used as auxiliary tool in solving various mathematical problems, dominantly for graph problems (for instance, determining of the metric dimension and domination numbers for various graph classes).
Members of the modelling and optimization group are intensively working on parallelization of previously mentioned methods, which also includes distributed optimization methods.
Scientific work
Monograph
- Stanimirović Zorica, Kratica Jozef, Filipović Vladimir and Tošić Dušan, Evolutionary approach for Solving Hab Location Problems (in Serbian), Zavod za udžbenike, Belgrade, 2011.
Papers published in top international journals
- Kratica Jozef, Stanimirović Zorica, Tošić Dušan and Filipović Vladimir, Two Genetic Algorithms for Solving the Uncapacitated Single Allocation p-Hub Median Problem , European Journal of Operational Research – EJOR, 182, pp. 15-28, 2006.
- Filipović Vladimir, Kartelj Aleksandar and Matić Dragan, An electromagnetism metaheuristic for solving the Maximum Betweenness Problem , Applied Soft Computing, Vol. 13, Iss. 2, pp. 1303–1313, 2013.
Papers published in international journals
- Kratica Jozef, Tošić Dušan, Filipović Vladimir and Ljubić Ivana, Solving the Simple Plant Location Problem by Genetic Algorithms, RAIRO - Operations Research, Vol. 35, No. 1, pp. 127-142, 2001.
- Filipović Vladimir, Fine-grained Tournament Selection Operator in Genetic Algorithms, Computers and Informatics, Vol. 22, No. 2, pp. 143-162, 2003.
- Kratica Jozef, Stanimirović Zorica, Tošić Dušan and Filipović Vladimir, Genetic Algorithm for Solving Uncapacitated Multiple Allocation Hub Location Problem, Computing and Informatics, Vol. 24 No 4, pp. 415-426, 2005.
- Đurić Brankica, Kratica Jozef, Tošić Dušan and Filipović Vladimir, Solving the maximally balanced connected partition problem in graphs by using genetic algorithm, Computing and Informatics, Vol. 27 No 3, pp. 341-354, 2008.
- Kratica Jozef, Kostić Tijana, Tošić Dušan, Dugošija Đorđe and Filipović Vladimir, A genetic algorithm for the routing and carrier selection problem , Computer Science and Information Systems – COMSIS, Vol. 9 No 1, pp. 49-62, 2012.
- Lazović Bojana, Marić Miroslav, Filipović Vladimir and Savić Aleksandar, An integer linear programming formulation and genetic algorithm for the maximum set splitting problem , Publications de l'Institut Mathématique, Vol. 92, Iss. 106, pp. 25-34, 2012.
- Nikolić Zorana, Brajušković Goran, Savić Dušanka, Kojić Aleksandra, Vujović Vinka, Tomović Saša, Cerović Snežana, Filipović Vladimir, Mišljenović Đura and Romac Stanka, Assessment of possible association between rs3787016 and prostate cancer risk in Serbian population, International Journal of Clinical and Experimental Medicine, Vol. 6, No. 1, pp. 57-66, 2013.
- Kartelj Aleksandar, Mitić Nenad, Filipović Vladimir and Tošić Dušan, Electromagnetism-like Algorithm for Support Vector Machine Parameter Tuning, Soft Computing, Vol. 18, Iss. 10, pp. 1985-1998, 2013.
- Savić Aleksandar, Kratica Jozef and Filipović Vladimir, A New Nonlinear Model for the Two-Dimensional Rectangle Packing Problem , Publications de l'Institut Mathématique, Vol. 93, Iss. 107, pp. 95-107, 2013.
- Kartelj Aleksandar, Šurlan Nebojša and Cekić Zoran, Case-based Reasoning and Electromagnetism-like Algorithm in Construction Management, Kybernetes: The International Journal of Systems & Cybernetics, Vol. 42, No. 2, pp. 265-280, 2014.
- Dražić Zorica, Savić Aleksandar and Filipović Vladimir, An integer linear formulation for the file transfer scheduling problem, TOP, Vol. 22, Iss. 3, pp. 1062-1073, 2014.
- Kartelj Aleksandar, An Improved Electromagnetism-like Method for Feature Selection, Journal of Multiple Valued Logic and Soft Computing, Vol. 25, Iss. 2/3, pp. 169-187, 2015.
- Matić Dragan, Kratica Jozef and Filipović Vladimir, Variable Neighborhood Search for solving Bandwidth Coloring Problem , Computer Science and Information Systems, Vol. 14, Iss. 2, pp. 309-327, 2017.
Chapters in monographic series
- Kratica Jozef, Tošić Dušan, Filipović Vladimir and Ljubić Ivana, A Genetic Algorithm for the Uncapacitated Network Design Problem, Soft Computing in Industry – Recent Applications, pp. 329-338, Springer, 2002.
- Filipović Vladimir, Kratica Jozef, Tošić Dušan and Dugošija Đorđe, GA Inspired Heuristic for Uncapacitated Single Allocation Hub Location Problem, Applications of Soft Computing - Advances in Soft Computing, Vol. 58/2009, pp. 149-158, Springer, 2009.
- Kratica Jozef, Kojić Jelena, Tošić Dušan, Filipović Vladimir and Dugošija Đorđe, Two Hybrid Genetic Algorithms for Solving the Super-Peer Selection Problem, Applications of Soft Computing - Advances in Soft Computing, Vol. 58/2009, pp. 337-346, Springer, 2009.
Other publications
Papers published in the top national journals
- Matić Dragan, Filipović Vladimir, Savić Aleksandar and Stanimirović Zorica, A Genetic Algorithm for Solving Multiple Warehause Layout Problem, Kragujevac Journal of Mathematics, Issue 35 Vol. 1, pp. 119-138, 2011.
- Savić Aleksandar, Šukilović Tijana and Filipović Vladimir, Solving the Two-Dimensional Packing Problem With m-M Calculus , Yugoslav Journal of Operations Research – YUJOR, Vol. 21 No. 1, pp. 93-102, 2011.
- Kratica Jozef, Tošić Dušan, Filipović Vladimir and Dugošija Đorđe, A New Genetic Representation for Quadratic Assignment Problem , Yugoslav Journal of Operations Research – YUJOR, Vol. 21 No. 2, pp. 225-238, 2011.
- Kartelj Aleksandar, Electromagnetism Metaheuristic Algorithm for Solving The Strong Minimum Energy Topology Problem, , Yugoslav Journal of Operations Research – YUJOR, Vol. 23, pp. 43-57, 2013.
Papers published in the national journals
- Tošić Dušan, Filipović Vladimir and Kratica Jozef, Using SVG–XML for representation of historical graphical data, Review of the National Center for Digitization, Vol. 9, pp. 39-45, 2006.
- Tošić Dušan, Filipović Vladimir, Tuba Milan and Kratica Jozef, Potential Role of SMIL in Digitalization of National Heritage, Review of the National Center for Digitization, Vol. 10, pp. 33-39, 2007.
- Kratica Jozef, Savić Aleksandar, Filipović Vladimir and Milanović Marija, Solving the Task Assignment Problem with a Variable Neighborhood Search, Serdica Journal of Computing, Vol. 4 No. 4, pp. 435-446, 2010.
- Kartelj Aleksandar, Classfication of Smoking Cessation Status Using Various Data Mining Methods, Mathematica Balkanica, Vol. 24, pp. 199-205, 2010.
- Filipović Vladimir, An Electromagnetism Metaheuristic for the Uncapacitated Multiple Allocation Hub Location Problem, Serdica Journal of Computing, Vol. 5 No. 3, pp. 261-272, 2011.
- Matić Dragan, Kratica Jozef , Filipović Vladimir and Dugošija Đorđe, Variable neighborhood search for Multiple Level Warehouse Layout Problem , Electronic Notes in Discrete Mathematics, Vol. 39, pp. 161-168, 2012.
- Filipović Vladimir, Zagarčanin Mladen, Tošić Dušan and Stanišić Sanja, Digitalization in the Bar County Museum – Pilot Project, Review of the National Center for Digitization, Vol. 23, pp. 57-66, 2013.
- Filipović Vladimir, Optimization, classification and dimensionality reduction in biomedicine and bioinformatics, Biologia Serbica, Vol. 39, No. 1, pp. 83-98, 2017.
From international conferences, published in proceedings
- Kratica Jozef, Filipović Vladimir, Šešum Vesna and Tošić Dušan, Solving of the uncapacitated warehouse location problem using a simple genetic algorithm, Proceedings of the XIV ECPD International Conference on Material handling and warehousing, pp. 3.33-3.37, Belgrade, 1996.
- Filipović Vladimir, Tošić Dušan, Urošević Dragan and Kratica Jozef, General parallel algorithm to the solution of the geophisical inversion problem applied to the transputer system, Proceedings of the VII Conference on Logic and Computer Science LIRA ‘97 - Separate Volume, pp. A3-A8, Novi Sad, Yugoslavia, 1997.
- Kratica Jozef, Tošić Dušan, Filipović Vladimir and Ljubić Ivana, Genetic Algorithm for Designing a Spread-Spectrum Radar Polyphase Code, Proceedings of the 5th Online World Conference on Soft Computing in Industrial Applications WSC5, pp. 191-197, 2000.
- Filipović Vladimir, Kratica Jozef, Tošić Dušan and Ljubić Ivana, Fine Grained Tournament Selection for the Simple Plant Location Problem , Proceedings of the 5th Online World Conference on Soft Computing in Industrial Applications WSC5, pp. 152-158, 2000.
- Kratica Jozef, Tošić Dušan, Filipović Vladimir and Ljubić Ivana, Comparing Performances of Several Algorithms for Solving the Simple Plant Location Problem, Proceedings of the 10th Congress of Yugoslav Mathematicians, Belgrade, Yugoslavia, pp. 337-341, 2001.
- Filipović Vladimir, Tošić Dušan and Kratica Jozef, Experimental Results in applying of Fine Grained Tournament Selection, Proceedings of the 10th Congress of Yugoslav Mathematicians, Belgrade, Yugoslavia, pp. 331-336, 2001.
- Filipović Vladimir, Tošić Dušan and Kratica Jozef, Parallel Evolutionary Algorithm Web Service, Proceedings of the XXX SYM-OP-IS 2003, pp. 292-295, Herceg Novi, 2003.
- Filipović Vladimir, Kratica Jozef, Savić Aleksandrar and Dugošija Đorđe, The modification of genetic algorithms for solving the balanced location problem, ACM International Conference Proceeding Series - 5th Balkan Conference in Informatics BCI 2012 , pp. 243-246, 2012.
- Kartelj Aleksandar, Filipović Vladimir and Milutinović Veljko, Novel approaches to automated personality classification: Ideas and their potentials, Proceedings of the 35th International Convention MIPRO 2012, pp. 1017 - 1022, 2012.
From international conferences, published in book of abstracts
- Grbić Milana, Kartelj Aleksandar, Matić Dragan and Filipović Vladimir, Improving 1NN strategy for classification of some procaryotic organisms, Book of abstracts, Belgrade Bioinformatic Conference (BelBI) 2016, pp. 57-58, Belgrade, 2016.
From national conferences, published in proceedings (in Serbian)
- Radojević Slobodan, Vugdelija Milan, Kratica Jozef and Filipović Vladimir, Ograničavanje stvaranja aplikativnih objekata u proizvodnim informacionim sistemima, Zbornik radova sa 22. JUPITER konferencije, pp. 4.13-4.16, Beograd, 1996.
- Filipović Vladimir, Kratica Jozef, Radojević Slobodan and Vugdelija Milan, Uticaj binarnog kodiranja na genetske algoritme za nalaženje ekstremnih vrednosti, Zbornik radova sa X međunarodne konferencije o industrijskim sistemima, pp. 193-198, Novi Sad, 1996.
- Radojević Slobodan, Kratica Jozef, Vugdelija Milan and Filipović Vladimir, Klasifikacija lambda-nula izraza relacijom denotacije, Naučni simpozijum XXIII YU-SYM-OP-IS, pp. 292-295, Zlatibor, 1996.
- Kratica Jozef, Radojević Slobodan, Filipović Vladimir and Šćepanović Ana, Primena epsilon-transformacije u problemu pretrage drveta, Međunarodni naučno-razvojni simpozijum: Stvaralaštvo kao uslov privrednog razvoja - Nove tehnologije and tehnike u službi čoveka, pp. 2.61-2.66, Beograd, 1996.
- Filipović Vladimir, Određivanje performansi genetskih algoritama u teoriji and praksi, Prolećna škola o programskim jezicima - Tekstovi predavanja, pp. 131-141, Novi Sad, 1997.
- Ljubić Ivana, Kratica Jozef and Filipović Vladimir, Primena genetskih algoritama u nalaženju minimalnog Štajnerovog stabla, Proceedings of the Second International Symposium of Industrial Engineering SIE ’98, pp. 277-280, Belgrade, 1998.
- Kratica Jozef, Ljubić Ivana, Šešum Vesna and Filipović Vladimir, Neke metode za rešavanje problema trgovačkog putnika pomoću genetskog algoritma, Proceedings of the Second International Symposium of Industrial Engineering SIE ’98, pp. 281-284, Belgrade, 1998.
- Maksimović Darko, Filipović Vladimir, Softver "Lenjir and šestar" and njegovo korišćenje u nastavi matematike , Zbornik radova sa četvrtog simpozijuma "Matematika and primene", pp. 158-169, Beograd, 2013.
- Matić Dragan, Kratica Jozef and Filipović Vladimir, Poboljšavanje nastavnog plana optimizovanjem broja pokrivenih tematskih oblasti po semestrima, Treća matematička konferencija Republike Srpske - Zbornik radova, Tom 2, pp. 11-21, Trebinje, 2014.
From national conferences, abstract published (in Serbian)
- Filipović Vladimir, Realizacija jednog iterativnog metoda na transpjuterskom sistemu, Zbornik apstrakata sa simpozijuma SINFON ‘94, pp. 41, Zlatibor, 1994. This paper is awarded as the best paper in its section.
- Filipović Vladimir, Realizacija genetskog algoritma, Zbornik apstrakata sa simpozijuma SINFON ‘95, pp. 46, Zlatibor, 1995.
- Filipović Vladimir and Tošić Dušan, Comparasion of Selection Operators in Genetic Algorithms for the Function Optimization, Abstracts of XI Conference on Applied Mathematics - PRIM ‘96, pp. 94, Budva, 1996.
- Fijuljanin Jasmina, Kartelj Aleksandar and Коjić Jelena, Electromagnetism metaheuristic for probabilistic satisfiability problem , Зборник апстраката са симпозијума Вероватносне логике и њене примене, ст. 22-23, Математички Институт САНУ, Београд, 2011.
- Kartelj Aleksandar,Filipović Vladimir and Milutinović Veljko, Automatizovana klasifikacija ličnosti, Zbornik apstrakata sa simpozijuma YU INFO 2012, pp. 49, Kopaonik, 2012.
- Matić Dragan, Filipović Vladimir and Kratica Jozef, Pregled metoda za rješavanje problema maksimalno balansirane povezane particije u grafu , Četvrta matematička konferencija Republike Srpske - Knjiga rezimea, pp. 73, Trebinje, 2014.
- Filipović Vladimir, Kartelj Aleksandar and Matić Dragan, Primena keširanja u metaheuristici zasnovanoj na elektromagnetizmu, Peta matematička konferencija Republike Srpske - Knjiga rezimea, pp. 42, Trebinje, 2015.
Collaboration
Gecko
Collaboration with engineers within company in designing algorithm for transportation problem.
CeSID
Statistical Processing and Analysis Software (SPAS).
Sampling and statistical analysis in elections.
Software for automated recognition and analysis of paper inquiries (RECO).
Direct Media agency
Media Mix Optimizer - marketing investment portfolio optimization (MMO)


Home
The British use the term "header".
The British use the term "header", but the American.
The British use the term "header", but the American term "head-shot" the English.
The British use the term "header", but the American term "head-shot" the English simply refuse to adopt.
The British use the term "header", but the American term "head-shot" the English simply refuse to adopt.
Content
The British use the term "header".
The British use the term "header", but the American.
The British use the term "header", but the American term "head-shot" the English.
The British use the term "header", but the American term "head-shot" the English simply refuse to adopt.
The British use the term "header", but the American term "head-shot" the English simply refuse to adopt.


Presented by Prime Meridian Direct
Tips for finding the best car insurance deals online
by PartnerIt may be an ideal time for you to re-evaluate your monthly expenses.
Perhaps your existing car insurance is too expensive now? How can you go about finding the best car insurance deal online in 2019?
Disclaimer: Do not consider the below information as financial advice. Please consult with a certified financial advisor if you would like to buy any car insurance.
Research your options
Online shopping is highly convenient, and there is a world of information at your fingertips online. Nevertheless, how should you research car insurance prices online?
There are many options. Car insurance comparison websites and car insurance company websites are accessible via a quick Google search.
Perhaps chat to friends and family if you are still unsure of which car insurers to consider. It could help if you did not automatically exclude smaller insurers from your research, as they may still offer good deals. Compare similar offerings from different insurers to help guide your decision.
There are several aspects to consider when looking for the best car insurance deal. Consider online car insurance deals from several reputable insurers and shortlist several insurers’ offers.
Try get a discount?
It doesn’t hurt to ask. You may be eligible for a discount on your premium. For example, you may benefit from a monthly saving if you insure more than one car with a car insurance company.
Improve your risk profile
Car insurance prices are usually dependent on a person’s risk profile. A risk profile is a personal profile that insurers create for you based on several factors from which they determine the level of risk that would be associated with insuring your car.
So, if you reduce common risk factors, then your premium could be lower. For example, how secure is the spot where you park your car at night? Improving your security could help.
Read the fine print
Take your time before you decide on your online car insurance deal. Buying car insurance online is quick and easy as many insurers offer this option.
Nevertheless, read the policy document thoroughly before you sign up. If in doubt, ask the insurer for clarity about anything that you are unsure of before you buy the car insurance.
Be sure to ask your financial advisor for other tips on finding the best car insurance deal online as well.
Get car insurance that suits your wallet and needs
Now it is time to make your final selection for the affordable car insurance deal that suits both your needs and that save you money too.
This article was published in partnership with Prime Meridian Direct.


{% for i in range(mark|length)%}
Mark {{i}}:{{mark[i]}}, weightage {{i}}:{{weight[i]}}
{% endfor %}
The weighted score is {{val}}.


Client-side and modular pagination for angular.
Just use the stPagination
filter with an array and the logic is handled by the library.
It is as simple as this example: user for users | stPagination
.
The number of page links never changes and prevents the pagination to cause line breaks.
Configure the html structure of the pagination directive to use it with the popular CSS frameworks
Bootstrap and Zurb Foundation.
As example collection we use a list of git commits from the angular repository with
{{ commits.length }} (commits.length
).
{{ commit.hash }}
{{ commit.comment }}
Add the module stPagination
to your angular app.
The only dependency is angular 1.0+.
The compatibility it tested for all stable minor releases
1.0.x ,
1.2.x and
1.3.x
.
To initialize the pagination you can use the stPagination
filter that handles
the filtering, correct calculation of offsets and pages.
commit in commits | stPagination
The filter attaches a pagination object to the collection which can be used by the directives.
commit in commits | filter:{comment:commentFilter} | stPagination:commits
A collection with a attached pagination object can be used with the pagination directive.
<st-pagination collection="commits"></st-pagination>
The automatic folding of indices can be configured.
The length of the displayed index ranges are set with the attributes mid-range
and edge-range
.
If the ranges overlap they are summed up to keep a fixed length.
Therefore you click on the '...' to see the behavior with index folding
<st-pagination collection="commits" mid-range="{{midRange}}" edge-range="{{edgeRange}}"></st-pagination>
midRange - range before and after current index
edgeRange - range at the start and end of all indices
The limit directive changes the number of items per page. The limit options can be configure,
<st-pagination-limit collection="commits" limits="[5,10,20,50,100]"></st-pagination-limit>
Information about the pagination can be displayed with the stPageInfo
filter.
| Property | Value | Usage example |
|---|---|---|
| {{ property }} | {{ commits | stPageInfo:property }} |
{{ propertyTemplate(property) }}
|
angular.module('myModule', ['stPagination'])
.config(function (stPaginationProvider) {
stPaginationProvider.setTemplateConfig({templateKey: '{{templateConfig.templateKey}}' });
})
{{ generatedHtml }}


Deal: Get a Google Nest Hub Max for $30 off ($199), Home Max for $50 off ($249)
If you’re in the market for a new smart display, now is a good time to pick one up. Over on Bloomingdale’s website, you can purchase a discounted Google Nest Hub Max for only $199, which is $30 off the original price tag.
If a smart display isn’t what you’re looking for, maybe the Google Home Max will pique your interest. Customers can pick up this impressive smart speaker pretty much anywhere on the internet for only $249 — $50 off the original price tag.
Both devices are some of the best in their classes. One of their only downsides is how expensive they are. Now, with this latest sale, both the Google Nest Hub Max and Home Max are even more accessible than before. That makes them great gift options for the holidays this year.
Buy the discounted Google Nest Hub Max
Buy the discounted Google Home Max


Structs
The following structs are available globally.
-
Undocumented
See more
-
A request credentialer that applies credentials to a
See moreURLRequest
on the request’s headerDeclaration
Swift
public struct HeaderRequestCredential: RequestCredentialing
-
Makes requests
See moreDeclaration
Swift
public struct WebService
-
Protocol for configuring a
See moreURLRequest
for a resource credentials on a requestDeclaration
Swift
public struct WebServiceConfiguration
-
Resource is analagous to an endpoint
See moreDeclaration
Swift
public struct Resource<A>
-
Handles decoding Data into a Foundation object
See moreDeclaration
Swift
public struct JSONDecoder
-
A request credentialer that applies credentials to a
See moreURLRequest
on the requestURL
‘s query stringDeclaration
Swift
public struct QueryStringRequestCredential: RequestCredentialing
-
Handles converting a JSON object into data for transmission in
See moreURLRequest
Declaration
Swift
public struct JSONEncoder: ParameterEncoding


{literal} {/literal}
[t]Please specify why you are disapproving this timesheet, so the candidate can make corrections.[/t]
{* disapproveTsc function is placed in tscAdminDetail.html file which opens this dialog by jquery plugin cluetip *}


Exit Gallery
Bright Colors
Am I even in this picture?
This is the kitten room at the adoption center where I spent my early days.
After verifying that I weighed enough to leave the center, my new family was excited to take me home.
I'm always ready for a good meal.
Lounging in the bathroom sink.
A favorite pastime.
Fear my mighty roar.
Hide and seek never gets old.
A selfie with my favorite girl.
If you're interrupting my nap, you better have a good reason.
I have since learned not to ever even pretend to nap in the dryer.
Once, my girl didn't know I was in there.


Pengo
bluetooth_connected
bluetooth_disabled
keyboard_arrow_left
keyboard_arrow_up
keyboard_arrow_down
keyboard_arrow_right
ACTIONS COMING SOON


Wow!Team Demos
Scroll Playground
Scroll Playground - In, Out, Continuous
Motion Playground
Custom Motion Playground
Video Crop
Legacy Animations Playground
Gradients Playground
Shape Divider Playground
Text Effects Playground
Text Mask Playground
Text on Path Playground


Provides a ‘Get-To-Lock’ Data Access Policy.
The ‘Get-To-Lock’ Data Access Policy allows for any number of ‘sets’, until the value is retrieved via a ‘get’. Once ‘get’ has been called, it is illegal to ‘set’ a new value.
The UVM uses this policy to protect the starting phase and automatic objection values in uvm_sequence_base.
| uvm_get_to_lock_dap | |||||||||||||||||
| Provides a ‘Get-To-Lock’ Data Access Policy. | |||||||||||||||||
| Class Hierarchy | |||||||||||||||||
| |||||||||||||||||
| Class Declaration | |||||||||||||||||
| |||||||||||||||||
| new | Constructor | ||||||||||||||||
| Set/ Get Interface | |||||||||||||||||
| set | Updates the value stored within the DAP. | ||||||||||||||||
| try_set | Attempts to update the value stored within the DAP. | ||||||||||||||||
| get | Returns the current value stored within the DAP, and ‘locks’ the DAP. | ||||||||||||||||
| try_get | Retrieves the current value stored within the DAP, and ‘locks’ the DAP. | ||||||||||||||||
| Introspection | The uvm_get_to_lock_dap cannot support the standard UVM instrumentation methods (copy, clone, pack and unpack), due to the fact that they would potentially violate the access policy. |
virtual function void set( T value )
Updates the value stored within the DAP.
set will result in an error if the value has already been retrieved via a call to get.
virtual function bit try_set( T value )
Attempts to update the value stored within the DAP.
try_set will return a 1 if the value was successfully updated, or a 0 if the value can not be updated due to get having been called. No errors will be reported if try_set fails.
virtual function T get()
Returns the current value stored within the DAP, and ‘locks’ the DAP.
After a ‘get’, the value contained within the DAP cannot be changed.
virtual function bit try_get( output T value )
Retrieves the current value stored within the DAP, and ‘locks’ the DAP.
try_get will always return 1.
The uvm_get_to_lock_dap cannot support the standard UVM instrumentation methods (copy, clone, pack and unpack), due to the fact that they would potentially violate the access policy.
A call to any of these methods will result in an error.
Provides a ‘Get-To-Lock’ Data Access Policy.
class uvm_get_to_lock_dap#( type T = int ) extends uvm_set_get_dap_base#(T)
Constructor
function new( string name = "unnamed-uvm_get_to_lock_dap#(T)" )
Updates the value stored within the DAP.
virtual function void set( T value )
Attempts to update the value stored within the DAP.
virtual function bit try_set( T value )
Returns the current value stored within the DAP, and ‘locks’ the DAP.
virtual function T get()
Retrieves the current value stored within the DAP, and ‘locks’ the DAP.
virtual function bit try_get( output T value )
The uvm_sequence_base class provides the interfaces needed to create streams of sequence items and/or other sequences.
class uvm_sequence_base extends uvm_sequence_item


HOME
ROOMS
AC
NON-AC
FOOD
DELICIOUS
SOUTH-INDIAN
CHINESE
CONTACT US
ORDER
HELP LINE
GRILL CHICKEN
WANT TO TASTE HEAVEN EVER TRIED FOOD HERETASTE IT FEEL THE HEAVEN!!!!
BROWNY DESSERT
THIS RECIPE MAKES US A WATERY IN OUR TONGUE


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Classes | |
| class | MultilevelMonteCarlo |
| The base class for the MultilevelMonteCarlo-classes. More... | |
| class | MultilevelMonteCarloResults |
| The base class for the MultilevelMonteCarloResults-classes. More... | |
Functions | |
| def | AddResultsAux_Task (level, *simulation_results) |
| def | FinalizePhaseAux_Task (ConstructorCallback, aux_settings_serialized, aux_mesh_parameters, aux_current_number_levels, aux_current_iteration, aux_number_samples, *args) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnce_Wrapper (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasks_Wrapper (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceOnlyAdaptiveRefinement_Wrapper (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinement_Wrapper (current_MLMC_level, pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev0_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_analysis, mlmc_results) |
| ConcurrentAdaptiveRefinementAllAtOnce ##############################. More... | |
| def | ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev1_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev2_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev0_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_level, current_analysis, mlmc_results) |
| ConcurrentAdaptiveRefinementMultipleTasks ############################. More... | |
| def | ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev1_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev2_Task (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_level, current_analysis, mlmc_results) |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev0_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| OnlySingleAdaptiveRefinement ###################################. More... | |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev1_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev2_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev3_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev4_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| def | executeInstanceOnlyAdaptiveRefinementAuxLev5_Task (pickled_model, pickled_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAuxLev0_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| SingleAdaptiveRefinement ###################################. More... | |
| def | ExecuteInstanceSingleRefinementAuxLev1_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAuxLev2_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAuxLev3_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAuxLev4_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAuxLev5_Task (pickled_model, pickled_project_parameters, random_variable, current_level, current_analysis, mlmc_results) |
| def | ExecuteInstanceConcurrentAdaptiveRefinementAux_Functionality (current_MLMC_level, pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, sample, current_level, current_analysis_stage, mlmc_results) |
| Functionalities ###################################. More... | |
| def | ExecuteInstanceOnlyAdaptiveRefinementAux_Functionality (pickled_coarse_model, pickled_coarse_project_parameters, pickled_custom_metric_refinement_parameters, pickled_custom_remesh_refinement_parameters, random_variable, current_index, current_analysis_stage, mlmc_results) |
| def | ExecuteInstanceSingleRefinementAux_Functionality (pickled_coarse_model, pickled_coarse_project_parameters, sample, current_level, current_analysis_stage, mlmc_results) |
Variables | |
| computing_units_auxiliar_utilities = int(os.environ["computing_units_auxiliar_utilities"]) | |
| computing_units_mlmc_execute_0 = int(os.environ["computing_units_mlmc_execute_0"]) | |
| computing_units_mlmc_execute_1 = int(os.environ["computing_units_mlmc_execute_1"]) | |
| computing_units_mlmc_execute_2 = int(os.environ["computing_units_mlmc_execute_2"]) | |
| computing_units_mlmc_execute_3 = int(os.environ["computing_units_mlmc_execute_3"]) | |
| computing_units_mlmc_execute_4 = int(os.environ["computing_units_mlmc_execute_4"]) | |
| computing_units_mlmc_execute_5 = int(os.environ["computing_units_mlmc_execute_5"]) | |
| def mlmc_utilities.AddResultsAux_Task | ( | level, | |
| * | simulation_results | ||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnce_Wrapper | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev0_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
ConcurrentAdaptiveRefinementAllAtOnce ##############################.
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev1_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementAllAtOnceAuxLev2_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementAux_Functionality | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis_stage, | |||
| mlmc_results | |||
| ) |
Functionalities ###################################.
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasks_Wrapper | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev0_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
ConcurrentAdaptiveRefinementMultipleTasks ############################.
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev1_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceConcurrentAdaptiveRefinementMultipleTasksAuxLev2_Task | ( | current_MLMC_level, | |
| pickled_coarse_model, | |||
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceOnlyAdaptiveRefinement_Wrapper | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceOnlyAdaptiveRefinementAux_Functionality | ( | pickled_coarse_model, | |
| pickled_coarse_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis_stage, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev0_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
OnlySingleAdaptiveRefinement ###################################.
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev1_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev2_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev3_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev4_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.executeInstanceOnlyAdaptiveRefinementAuxLev5_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| pickled_custom_metric_refinement_parameters, | |||
| pickled_custom_remesh_refinement_parameters, | |||
| random_variable, | |||
| current_index, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinement_Wrapper | ( | current_MLMC_level, | |
| pickled_model, | |||
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAux_Functionality | ( | pickled_coarse_model, | |
| pickled_coarse_project_parameters, | |||
| sample, | |||
| current_level, | |||
| current_analysis_stage, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev0_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
SingleAdaptiveRefinement ###################################.
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev1_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev2_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev3_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev4_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.ExecuteInstanceSingleRefinementAuxLev5_Task | ( | pickled_model, | |
| pickled_project_parameters, | |||
| random_variable, | |||
| current_level, | |||
| current_analysis, | |||
| mlmc_results | |||
| ) |
| def mlmc_utilities.FinalizePhaseAux_Task | ( | ConstructorCallback, | |
| aux_settings_serialized, | |||
| aux_mesh_parameters, | |||
| aux_current_number_levels, | |||
| aux_current_iteration, | |||
| aux_number_samples, | |||
| * | args | ||
| ) |
| int mlmc_utilities.computing_units_auxiliar_utilities = int(os.environ["computing_units_auxiliar_utilities"]) |
| int mlmc_utilities.computing_units_mlmc_execute_0 = int(os.environ["computing_units_mlmc_execute_0"]) |
| int mlmc_utilities.computing_units_mlmc_execute_1 = int(os.environ["computing_units_mlmc_execute_1"]) |
| int mlmc_utilities.computing_units_mlmc_execute_2 = int(os.environ["computing_units_mlmc_execute_2"]) |
| int mlmc_utilities.computing_units_mlmc_execute_3 = int(os.environ["computing_units_mlmc_execute_3"]) |
| int mlmc_utilities.computing_units_mlmc_execute_4 = int(os.environ["computing_units_mlmc_execute_4"]) |
| int mlmc_utilities.computing_units_mlmc_execute_5 = int(os.environ["computing_units_mlmc_execute_5"]) |


{% extends 'demo/base.html' %} {% load view_instance_code from demo_tags %} {% load breadcrumbs from directory_components %} {% block content %}
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 Still, the thought occurs to me to make it one of those days where company is limited to Coltrane, Cash and Faulkner.
Yeah.
Christ. It must be November again.
The November air is damp and reeks of nothingness and space. Sounds are dull and things wither. I trod along on vacant roads with damp, rotting leaves at my feet. I know that all things pass, and that this too, inevitably will…

I guess the only thing to do is to keep on going; put my head down and at least trod on, if I can’t charge. Continue moving forward as the vacant streets and their damp multi-colored leaves move backwards under my feet, hoping that November passes in fewer than its 30 miserable days and that once it does, there will be enough left in me to do what ever it is that I know I have to do.
But enough on that, before I stray dangerously close to something that may terrify me.
–
I’ve written before about the glorious feel of autumn and the cold strength of winter but November doesn’t quite live up to either. It’s a sickly month with the fading life of greater times. Yet the social responsibilities of the coming holidays, to say nothing of the coming year that looms like the annual equivalent of a Sunday, full of sickly uncertainty and wavering with the meekness of temporal doubt.
But the holidays… ehhh – the holidays. Here come the days in which almost everyone is culturally obligated to participate for they are the holy days, I suppose. The etimology of the word is curious, if not self-evident. Still, tomorrow we (yes reader, even I) will partake in the silly celebration of an event that is largely symbolic and may never actually have taken place.
But you know, I am not above putting down the loathing for a second to enjoy some good meat when the occasion calls for it and the meat is properly basted. In every sense of the word.
But indeed, what comes after that? Will there be 24-hour sermons across the country in places holy that do all they can to describe actual misery to those who know it not in order to make people feel slightly less guilty about all the THINGS they have? Sure.
Will there be collections taken where these same animals can buy off their guilt with one payment of some greenbacks? We’ll take what we can get.

The day after tomorrow will be a sprint to the nearest Best Buy for that 2 gB memory card your nephew was talking about or to Target for deals on DVDs for the secret santa they do every year that you were tired of playing 6 years ago or else to Barnes and Noble for that book that little Timmy was…
…no wait — Timmy can’t read. Yeah, that was a stretch. Still, the day after tomorrow will be a circus of credit cards and fake plastic trees; a zoo of wish lists filled with things that were never wished for but were on sale; a madhouse of traffic, no parking, pollution, plastic wrapping, little to no thoughts for others and general mayhem.
How did it come to this?


Across: 1. Protection for redhead in love (6) 4. Hints for Victor when splitting commercial diamonds (6) 9. Susa ran out of sausages for a long time (4) 10. Obviously tiny flames spread! (10) 11. Disturbance caused by father joining mediators (6) 12. Run off around lake with old slice of meat (8) 13.  Fair price? About time! (4) 16. Fat model follows girl (4) 17. First bird gets hit coming back to Split (9) 21. Rolled mad Derek outside den of vice (8) 22. Write down “Gallery Closed” (6) 24. New life with tenor running out of Italian city (10) 25. Part-timers work out record (4) 26. River Police enter Settle (6) 27. Is nude missing these? (6) Down: 1. Gale whips up air from the country (7) 2. Around America 1099 find harmony (5) 3. Reveals a French scholar’s heartless kiss (7) 5. Cross over fault! (6) 6. Without funds come up with small old laptop inside (9) 7. Shape the Spanish untidy piles (7) 8. Actress joins Joel, Annie and Gail (8 5) 14. Cheroot I’d rolled for emperor (9) 16. Contaminated lubricant ran out (7) 18. He may add colour to northern abbey (7) 19. Marine creature makes wild coots get back up inside (7) 20. Removed excess material from plot with editor (6) 23. In Paris you and I take in the races together (5)


Created by Franco G. Riberi.
rm -rf /home .... :-(
FROM debian. # set the base image
LABEL version="1.0" # extra metadata
WORKDIR /app # Set the working directory to /app.
COPY . /app. # # Copy the current directory contents into the container
RUN apt-get install -qy git ... # install basic apps
EXPOSE 80 # Make port 80 available to outside this container
ENV NAME World # Define environment variable
CMD ["python", "app.py"] # Run app.py when the container launches
# Commands for interacting with images
$ docker images # shows all images.
$ docker import # creates an image from a tarball.
$ docker build # creates image from Dockerfile.
$ docker commit # creates image from a container.
$ docker rmi # removes an image.
$ docker history # list changes of an image.
# Here are some data on commonly used images:
scratch - ultimate base image and it has 0 files / 0 size.
busybox - minimal Unix, 2.5 MB, around 10000 files.
debian:jessie - latest Debian, 122 MB, around 18000 files.
ubuntu:14.04 - Ubuntu, 188 MB, around 23000 files.
# Commands for interacting with containers
$ docker create # creates a container but does not start it.
$ docker run # creates and starts a container.
$ docker stop # stops it.
$ docker start # will start it again.
$ docker restart # restarts a container.
$ docker rm # deletes a container.
$ docker kill # sends a SIGKILL to a container.
$ docker attach # will connect to a running container.
$ docker wait # blocks until container stops.
$ docker exec # executes a command in a running container.
# Inspecting Containers
$ docker ps # shows running containers.
$ docker inspect # info on a container (incl. IP address).
$ docker logs # gets logs from container.
$ docker events # gets events from container.
$ docker port # shows public facing port of container.
$ docker top # shows running processes in container.
$ docker diff # shows changed files in container's FS.
$ docker stats # shows metrics, memory, cpu, filsystem
# Run a container interactively like a normal terminal program.
$ docker run -it --rm ubuntu
(-i) - send stdin to the process.
(-t) - tell the process that a terminal is present.
(--rm) - remove the container on exit.
# Run a container in the background
$ docker run -d ubuntu
(-d) - detached mode, you can attach again with docker attach
# Run a named container and pass it some environment variables
$ docker run --name mydb --env MYSQL_USER=db-user \
-e MYSQL_PASSWORD=secret --env-file ./mysql.env mysql
# Start a new nginx container with /var/log as a volume
$ docker run -v /var/log nginx
Since the directory of the host is not given, the volume is created in /var/lib/docker/volumes/ec3c543bc..535.
# Start a new nginx container with /var/log as a volume mapped to /tmp on Host
$ docker run -v /tmp:/var/log nginx
# Mount volumes from another container with --volumes-from.
# Start a db container
$ docker run -v /var/lib/postgresql/data --name mydb postgres
# Start a backup container with the volumes taken from the mydb container
$ docker run --volumes-from mydb backup
# Limit the amount of memory
$ docker run -m 256m yourapp
# Error response from daemon: Minimum memory limit allowed is 4MB exit
$ docker run -m 128 ubuntu true
# Limit the number of shares of the CPU this process uses
$ docker run --cpu-shares 512 mypp
# Change the user for the process to www instead of root
$ docker run -u=www nginx
# Get the id (-q) of the last (-l) run container
$ docker ps -l -q
c8044ab1a3d0
# Get container IP
$ docker inspect -f '{{ .NetworkSettings.IPAddress }}' 6f2c42c05500
<IP_ADDRESS>
# Get the environment variables of a running container.
$ docker exec -it 6f2c42c05500 env
PATH=/usr/local/sbin:/usr...
HOSTNAME=6f2c42c05500
REDIS_1_PORT=tcp://<IP_ADDRESS>:6379
REDIS_1_PORT_6379_TCP=tcp://<IP_ADDRESS>:6379
...

# Create a Non-Root User
RUN useradd --user-group --shell /bin/false resin
USER resin


Changes command prompt's window display mode. For exemple, maximizes of minimizes the window.
BatBox /p mode
Changes command prompt's window display mode.
mode : Specify the display mode that will be set for the command prompt's window.
0 : Hides the window.
1 : Activate and show the window, restoring it to its original size. This value should not be used because it's reserved to first call to show window. You should use value 9 instead.
2 : Activate the window and minimizes it (show it in task bar).
3 : Activate the window and make it maximized.
4 : Shows the window restored to its original size. Same result as value 1, execpt that the window does not obtain focus.
5 : Activate the window without modifiing it.
6 : Minimizes the window and activate another window.
7 : Minimizes the window. Same result as value 2 except that the window is not activated.
8 : Shows the window without modifing it. Same result as value 1, except that the window does not obtain focus.
9 : Activate the window and set its size and its position to the initial values if the window was either maximized or minimized.
10 : Show the windows and set its size and its postion to the default values.
11 : Minimizes the windows, even if the command prompt does not respond. This value is not suposed to be used, as it would be surprising that BatBox continue running even if the command prompt does not respond.
BatBox (/a), BatBox (/m), BatBox (/f), BatBox
Dos9 manual pages, The Dos9 Project - Copyright © 2010-2013 Darkbatcher
This is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Please report bugs, and mistakes at : <darkbatcher at dos9 dot org>


To test out RegistryJump, highlight the following registry key:
HKEY_CURRENT_USER\Software\Microsoft\Internet Explorer\MenuExt
then right-click the mouse and select 'Open key with RegistryJump'


{% extends "about.html" %} {% block title %}User Sign Up{%endblock%} {% block content %}
| {{ super() }} |
Create an accountor loginPlease make sure you backup your password.To ensure security, your actual password is not saved in our database. Thus we have limited ability to reset your password. If you need to recovery your account, please contact us. We'll need the email address you used to create the account. |


Airflow Tutorial
Airflow is a scheduler for workflows such as data pipelines, similar to Luigi and Oozie.
This tutorial is loosely based on the Airflow tutorial in the official documentation. It will walk you through the basics of setting up Airflow and creating an Airflow workflow, and it will give you some practical tips. A (possibly) more up-to-date version of this blog can be found in my git repo.
1. Setup
Setting up a basic configuration of Airflow is pretty straightforward. After installing the Python package, we'll need a database to store some data and start the core Airflow services.
You can skip this section if Airflow is already set up.
Make sure that you can run airflow
commands, know where to put your DAGs and have access to the web UI.
Install Airflow
Airflow is installable with pip
via a simple pip install apache-airflow
.
Either use a separate Python virtual environment or install it in your default python environment.
To use the conda virtual environment as defined in environment.yml
from my git repo:
- Install miniconda.
- Make sure that conda is on your path:
$ which conda
~/miniconda3/bin/conda
- Create the virtual environment from
environment.yml
:
$ conda env create -f environment.yml
- Activate the virtual environment:
$ source activate airflow-tutorial
You should now have an (almost) working Airflow installation.
Alternatively, install Airflow yourself by running:
$ pip install apache-airflow
Airflow used to be packaged as airflow
but is packaged as apache-airflow
since version 1.8.1.
Make sure that you install any extra packages with the right Python package: e.g. use pip install apache-airflow[dask]
if you've installed apache-airflow
and do not use pip install airflow[dask]
.
Leaving out the prefix apache-
will install an old version of Airflow next to your current version, leading to a world of hurt.
You may run into problems if you don't have the right binaries or Python packages installed for certain backends or operators.
When specifying support for e.g. PostgreSQL when installing extra Airflow packages, make sure the database is installed; do a brew install postgresql
or apt-get install postgresql
before the pip install apache-airflow[postgres]
.
Similarly, when running into HiveOperator errors, do a pip install apache-airflow[hive]
and make sure you can use Hive.
Run Airflow
Before you can use Airflow you have to initialize its database. The database contains information about historical & running workflows, connections to external data sources, user management, etc. Once the database is set up, Airflow's UI can be accessed by running a web server and workflows can be started.
The default database is a SQLite database, which is fine for this tutorial. In a production setting you'll probably be using something like MySQL or PostgreSQL. You'll probably want to back it up as this database stores the state of everything related to Airflow.
Airflow will use the directory set in the environment variable AIRFLOW_HOME
to store its configuration and our SQlite database.
This directory will be used after your first Airflow command.
If you don't set the environment variable AIRFLOW_HOME
, Airflow will create the directory ~/airflow/
to put its files in.
Set environment variable AIRFLOW_HOME
to e.g. your current directory $(pwd)
:
# change the default location ~/airflow if you want:
$ export AIRFLOW_HOME="$(pwd)"
or any other suitable directory.
Next, initialize the database:
$ airflow initdb
Now start the web server and go to localhost:8080 to check out the UI:
$ airflow webserver --port 8080
It should look something like this:
With the web server running workflows can be started from a new terminal window.
Open a new terminal, activate the virtual environment and set the environment variable AIRFLOW_HOME
for this terminal as well:
$ source activate airflow-tutorial
$ export AIRFLOW_HOME="$(pwd)"
Make sure that you're an in the same directory as before when using $(pwd)
.
Run a supplied example:
$ airflow run example_bash_operator runme_0 2017-07-01
And check in the web UI that it has run by going to Browse -> Task Instances.
This concludes all the setting up that you need for this tutorial.
Tips
- Both Python 2 and 3 are be supported by Airflow.
However, some of the lesser used parts (e.g. operators in
contrib
) might not support Python 3. - For more information on configuration check the sections on Configuration and Security of the Airflow documentation.
- Check the Airflow repository for
upstart
andsystemd
templates. - Airflow logs extensively, so pick your log folder carefully.
- Set the timezone of your production machine to UTC: Airflow assumes it's UTC.
2. Workflows
We'll create a workflow by specifying actions as a Directed Acyclic Graph (DAG) in Python. The tasks of a workflow make up a Graph; the graph is Directed because the tasks are ordered; and we don't want to get stuck in an eternal loop so the graph also has to be Acyclic.
The figure below shows an example of a DAG:
The DAG of this tutorial is a bit easier. It will consist of the following tasks:
- print
'hello'
- wait 5 seconds
- print
'world
and we'll plan daily execution of this workflow.
Create a DAG file
Go to the folder that you've designated to be your AIRFLOW_HOME
and find the DAGs folder located in subfolder dags/
(if you cannot find, check the setting dags_folder
in $AIRFLOW_HOME/airflow.cfg
).
Create a Python file with the name airflow_tutorial.py
that will contain your DAG.
Your workflow will automatically be picked up and scheduled to run.
First we'll configure settings that are shared by all our tasks. Settings for tasks can be passed as arguments when creating them, but we can also pass a dictionary with default values to the DAG. This allows us to share default arguments for all the tasks in our DAG is the best place to set e.g. the owner and start date of our DAG.
Add the following import and dictionary to airflow_tutorial.py
to specify the owner, start time, and retry settings that are shared by our tasks:
Configure common settings
import datetime as dt
default_args = {
'owner': 'me',
'start_date': dt.datetime(2017, 6, 1),
'retries': 1,
'retry_delay': dt.timedelta(minutes=5),
}
These settings tell Airflow that this workflow is owned by 'me'
, that the workflow is valid since June 1st of 2017, it should not send emails and it is allowed to retry the workflow once if it fails with a delay of 5 minutes.
Other common default arguments are email settings on failure and the end time.
Create the DAG
We'll now create a DAG object that will contain our tasks.
Name it airflow_tutorial_v01
and pass default_args
:
from airflow import DAG
with DAG('airflow_tutorial_v01',
default_args=default_args,
schedule_interval='0 * * * *',
) as dag:
With schedule_interval='0 * * * *'
we've specified a run at every hour 0; the DAG will run each day at 00:00.
See crontab.guru for help deciphering cron schedule expressions.
Alternatively, you can use strings like '@daily'
and '@hourly'
.
We've used a context manager to create a DAG (new since 1.8).
All the tasks for the DAG should be indented to indicate that they are part of this DAG.
Without this context manager you'd have to set the dag
parameter for each of your tasks.
Airflow will generate DAG runs from the start_date
with the specified schedule_interval
.
Once a DAG is active, Airflow continuously checks in the database if all the DAG runs have successfully ran since the start_date
.
Any missing DAG runs are automatically scheduled.
When you initialize on 2016-01-04 a DAG with a start_date
at 2016-01-01 and a daily schedule_interval
, Airflow will schedule DAG runs for all the days between 2016-01-01 and 2016-01-04.
A run starts after the time for the run has passed.
The time for which the workflow runs is called the execution_date
.
The daily workflow for 2016-06-02 runs after 2016-06-02 23:59 and the hourly workflow for 2016-07-03 01:00 starts after 2016-07-03 01:59.
From the ETL viewpoint this makes sense: you can only process the daily data for a day after it has passed.
This can, however, ask for some juggling with date for other workflows.
For Machine Learning models you may want to use all the data up to a given date, you'll have to add the schedule_interval
to your execution_date
somewhere in the workflow logic.
Because Airflow saves all the (scheduled) DAG runs in its database, you should not change the start_date
and schedule_interval
of a DAG.
Instead, up the version number of the DAG (e.g. airflow_tutorial_v02
) and avoid running unnecessary tasks by using the web interface or command line tools
Timezones and especially daylight savings can mean trouble when scheduling things, so keep your Airflow machine in UTC. You don't want to skip an hour because daylight savings kicks in (or out).
Create the tasks
Tasks are represented by operators that either perform an action, transfer data, or sense if something has been done. Examples of actions are running a bash script or calling a Python function; of transfers are copying tables between databases or uploading a file; and of sensors are checking if a file exists or data has been added to a database.
We'll create a workflow consisting of three tasks: we'll print 'hello', wait for 10 seconds and finally print 'world'.
The first two are done with the BashOperator
and the latter with the PythonOperator
.
Give each operator an unique task ID and something to do:
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator
def print_world():
print('world')
print_hello = BashOperator(task_id='print_hello',
bash_command='echo "hello"')
sleep = BashOperator(task_id='sleep',
bash_command='sleep 5')
print_world = PythonOperator(task_id='print_world',
python_callable=print_world)
Note how we can pass bash commands in the BashOperator
and that the PythonOperator
asks for a Python function that can be called.
Dependencies in tasks are added by setting other actions as upstream (or downstream).
Link the operations in a chain so that sleep
will be run after print_hello
and is followed by print_world
; print_hello
-> sleep
-> print_world
:
print_hello >> sleep >> print_world
After rearranging the code your final DAG should look something like:
import datetime as dt
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator
def print_world():
print('world')
default_args = {
'owner': 'me',
'start_date': dt.datetime(2017, 6, 1),
'retries': 1,
'retry_delay': dt.timedelta(minutes=5),
}
with DAG('airflow_tutorial_v01',
default_args=default_args,
schedule_interval='0 * * * *',
) as dag:
print_hello = BashOperator(task_id='print_hello',
bash_command='echo "hello"')
sleep = BashOperator(task_id='sleep',
bash_command='sleep 5')
print_world = PythonOperator(task_id='print_world',
python_callable=print_world)
print_hello >> sleep >> print_world
Test the DAG
First check that DAG file contains valid Python code by executing the file with Python:
$ python airflow_tutorial.py
You can manually test a single task for a given execution_date
with airflow test
:
$ airflow test airflow_tutorial_v01 print_world 2016-07-01
This runs the task locally as if it was for 2017-07-01, ignoring other tasks and without communicating to the database.
Activate the DAG
Now that you're confident that your dag works, turn on the DAG in the web UI and sit back while Airflow starts backfilling the dag runs!
Tips
- Make your DAGs idempotent: rerunning them should give the game results.
- Use the the cron notation for
schedule_interval
instead of@daily
and@hourly
.@daily
and@hourly
always run after respectively midnight and the full hour, regardless of the hour/minute specified. - Manage your connections and secrets with the Connections and/or Variables.
3. Exercises
You now know the basics of setting up Airflow, creating a DAG and turning it on; time to go deeper!
- Change the interval to every 30 minutes.
- Use a sensor to add a delay of 5 minutes before starting.
- Implement templating for the
BashOperator
: print theexecution_date
instead of'hello'
(check out the original tutorial and the example DAG). - Use templating for the
PythonOperator
: print theexecution_date
with one hour added in the functionprint_world()
(check out the documentation of thePythonOperator
).
4. Resources
- The official Airflow tutorial: showing a bit more in-depth templating magic.
- ETL best practices with Airflow: good best practices to follow when using Airflow.
- Airflow: Tips, Tricks, and Pitfalls: more explanations to help you grok Airflow.


DESERT
3.0
DESERT Directory Reference
Directory dependency graph for DESERT:
Directories
directory
application
directory
channel
directory
data_link
directory
interference
directory
mobility
directory
network
directory
physical
directory
propagation
directory
transport
DESERT_Framework
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