I am looking for an algorithm visualization library/tool that is well documented and you can call from your source code.
I took a look at jhave - example of usage. And I liked it, it seems it has some documentation but I do not trust its future.
I found this article about Algorithm explorer it has a nice idea. It is implemented as a c++ api but I cannot find it anywere.
My main idea is that I want to do some unit tests for the brain.
So I construct various exercises and in future when I want to test my knowledge I redo them.
I found that images stick longer with me, so that is why I want to visualize algorithms in certain states. ( I might remember better a tricky case like what happens when data is sorted in reverse and I use quick sort if I view it.)
An ideal tool:
1. Has to integrate with any language.
2. Has to be well documented with a growing comunity and examples.
3. Be implemented on top of a capable rendering engine(ogre, xna).
Here is the place you need to visit: The Algorithm Visualization Portal!
Related
I am looking to create documentation for a project created with reactor library.
I searched but did not found any useful tool that generates photo diagrams after running a piece of reactor(or rx in general) code. The only thing i found is a text based syntax like this.Which I guess is a solution follow if i dont find anything else.
libraries found that use this syntax
https://flames-of-code.netlify.com/blog/rx-marbles/
https://github.com/cescoffier/rx-marble-docker
Ideally i would like to run a piece of code eg.
Flux.from(f1)
.bufferTimeout(writeDbBuffer, Duration.ofSeconds(10))
.parallel()
.runOn(Schedulers.parallel()).subscribe(photosBatch -> {
photoRepository.saveAll(photosBatch);
});
And generate marble diagram in photo or ever text based.
As a solution to the text based syntax mentioned above i could create text generators based on this syntax but this would require a lot of effort and time.
There is any way to generate images with marble diagrams with rxjava, rxjs or preferable reactor library from pieces of code?(I am including rx because is way more popular that reactor)
There is any library generating the above text based syntax from pieces of code?
What other options i have for documentation over these libraries?
also a similar question but not exactly what i am looking for
Something that dynamic is, to my knowledge, not yet available in the Java world. Closest thing I know of is rxfiddle, and to an extent rxmarbles.com (although the later doesn't allow generation from arbitrary pieces of code).
Generating clean and good looking visualization of arbitrary reactive sequences dynamically is no small task, but that's something the Reactor team would love to see at some point (either done officially or by the community).
The text-based solutions are great for simple marbles and simple operators, because you are in essence drawing the marble yourself, using the syntax of each tool (and thus being limited by it).
Higher-order sequences, parallelization, etc... introduce far greater complexity and start to stretch these tools to their limits.
I'm interested in implementing the Fast Multipole Method to efficiently simulate a system of repulsive particles.
I've found a large collection of references discussing FMM, but none seem very approachable for non-mathematicians who want to fully understand the algorithm.
Can you recommend a ground-up reference that clearly explains the mathematics behind the process, and includes pseudocode exemplifying a proper implementation?
I am by no means an expert in FMM, but this java implementation and introduction is the best source I've found so far for explaining it carefully and slowly. The paper is good at defining terms before using them, and the code at least is useful as a reference point. The math still gets hairy very quickly, but it is what it is :)
A pedestrian introduction to fast multipole methods is a close second. It doesn't explain the actual details of a working FMM implementation, but it's a good introduction to the basic ideas.
I like the short course on FMM. In begins with FMM in 1D, than it uses theory of complex variable to do FMM in 2D. And than there is the crazy 3D version which uses theory of spherical harmonics functions, which I guess can be very difficult for non-mathematician. But If you need FMM only in 2D you should be fine.
Unfortunately no pseudo codes are given there.
But do you really need the accuracy of FMM?. You might be fine with Barnes-Hut's algorithm
After running into a similar issue to you, I ended up writing a fully-documented Python fast multipole method implementation, pybbfmm. I've also written a short, mathematics-free tutorial on how the method works. Together, I think they're substantially more accessible than any of the other presentations I could find.
(meta: Although this is effectively a linkpost, the OP is explicitly asking for a link. I've added what I think was missing from the last one - the name fo the library - but I'm not sure how else to offer this answer except as a name and a link. Certainly it doesn't feel any more linkpost-y than the accepted answer. If this one gets deleted as well, I'll give up)
I have tried to be disciplined about decomposing into small reusable methods when possible. As the project growing, I am re-implementing the exact same method.
I would like to know how to deal with this in an automated way. I am not looking for an IDE specific solution. Dependency on method names may not be sufficient. Unix and scripting are solutions that would be extremely beneficial. Answers such as "take care" etc. are not the solutions I am seeking.
I think the cheapest solution to implement might be to use Google Desktop. A more accurate solution would probably be much harder to implement - treat your code base as a collection of documents where the identifiers (or tokens in the identifiers) are words of the document, and then use document clustering techniques to find the closest matching code to a query. I'm aware of some research similar to that, but nothing close to out-of-the-box code that you could use. You might try looking on Google Code Search for something. I don't think they offer a desktop version, but you might be able to find some applicable code you can adapt.
Edit: And here's a list of somebody's favorite code search engines. I don't know whether any are adaptable for local use.
Edit2: Source Code Search Engine is mentioned in the comments following the list of code search engines. It appears to be a commercial product (with a free evaluation version) that is intended to be used for searching local code.
Having been a hobbyist programmer for 3 years (mainly Python and C) and never having written an application longer than 500 lines of code, I find myself faced with two choices :
(1) Learn the essentials of data structures and algorithm design so I can become a l33t computer scientist.
(2) Learn Qt, which would help me build projects I have been itching to build for a long time.
For learning (1), everyone seems to recommend reading CLRS. Unfortunately, reading CLRS would take me at least an year of study (or more, I'm not Peter Krumins). I also understand that to accomplish any moderately complex task using (2), I will need to understand at least the fundamentals of (1), which brings me to my question : assuming I use C++ as the programming language of choice, which parts of CLRS would give me sufficient knowledge of algorithms and data structures to work on large projects using (2)?
In other words, I need a list of theoretical CompSci topics absolutely essential for everyday application programming tasks. Also, I want to use CLRS as a handy reference, so I don't want to skip any material critical to understanding the later sections of the book.
Don't get me wrong here. Discrete math and the theoretical underpinnings of CompSci have been on my "TODO: URGENT" list for about 6 months now, but I just don't have enough time owing to college work. After a long time, I have 15 days off to do whatever the hell I like, and I want to spend these 15 days building applications I really want to build rather than sitting at my desk, pen and paper in hand, trying to write down the solution to a textbook problem.
(BTW, a less-math-more-code resource on algorithms will be highly appreciated. I'm just out of high school and my math is not at the level it should be.)
Thanks :)
This could be considered heresy, but the vast majority of application code does not require much understanding of algorithms and data structures. Most languages provide libraries which contain collection classes, searching and sorting algorithms, etc. You generally don't need to understand the theory behind how these work, just use them!
However, if you've never written anything longer than 500 lines, then there are a lot of things you DO need to learn, such as how to write your application's code so that it's flexible, maintainable, etc.
For a less-math, more code resource on algorithms than CLRS, check out Algorithms in a Nutshell. If you're going to be writing desktop applications, I don't consider CLRS to be required reading. If you're using C++ I think Sedgewick is a more appropriate choice.
Try some online comp sci courses. Berkeley has some, as does MIT. Software engineering radio is a great podcast also.
See these questions as well:
What are some good computer science resources for a blind programmer?
https://stackoverflow.com/questions/360542/plumber-programmers-vs-computer-scientists#360554
Heed the wisdom of Don and just do it. Can you define the features that you want your application to have? Can you break those features down into smaller tasks? Can you organize the code produced by those tasks into a coherent structure?
Of course you can. Identify any 'risky' areas (areas that you do not understand, e.g. something that requires more math than you know, or special algorithms you would have to research) and either find another solution, prototype a solution, or come back to SO and ask specific questions.
Moving from 500 loc to a real (eve if small) application it's not that easy.
As Don was pointing out, you'll need to learn a lot of things about code (flexibility, reuse, etc), you need to learn some very basic of configuration management as well (visual source safe, svn?)
But the main issue is that you need a way to don't be overwhelmed by your functiononalities/code pair. That it's not easy. What I can suggest you is to put in place something to 'automatically' test your code (even in a very basic way) via some regression tests. Otherwise it's going to be hard.
As you can see I think it's no related at all to data structure, algorithms or whatever.
Good luck and let us know
I must say that sitting down with a dry old textbook and reading it through is not the way to learn how to do anything effectively, even if you are making notes. Doing it is the best way to learn, using the textbooks as a reference. Indeed, using sites like this as a reference.
As for data structures - learn which one is good for whatever situation you envision: Sets (sorted and unsorted), Lists (ArrayList, LinkedList), Maps (HashMap, TreeMap). Complexity of doing basic operations - adding, removing, searching, sorting, etc. That will help you to select an appropriate library data structure to use in your application.
And also make sure you're reasonably warm with MVC - i.e., ensure your model is separate from your view (the QT front-end) as best as possible. Best would be to have the model and algorithms working on their own, and then put the GUI on top. Or a unit test on top. Etc...
Good luck!
It's like saying you want to move to France, so should you learn french from a book, and what are the essential words - or should you just go to France and find out which words you need to know from experience and from copying the locals.
Writing code is part of learning computer science. I was writing code long before I'd even heard of the term, and lots of people were writing code before the term was invented.
Besides, you say you're itching to write certain applications. That can't be taught, so just go ahead and do it. Some things you only learn by doing.
(The theoretical foundations will just give you a deeper understanding of what you wind up doing anyway, which will mainly be copying other people's approaches. The only caveat is that in some cases the theoretical stuff will tell you what's futile to attempt - e.g. if one of your itches is to solve an NP complete problem, you probably won't succeed :-)
I would say the practical aspects of coding are more important. In particular, source control is vital if you don't use that already. I like bzr as an easy to set up and use system, though GUI support isn't as mature as it could be.
I'd then move on to one or both of the classics about the craft of coding, namely
The Pragmatic Programmer
Code Complete 2
You could also check out the list of recommended books on Stack Overflow.
I need to display profiling information pulled from a deeply embedded CPU, presenting it in a way which other developers on my team will be able to act upon. The profiling data is a snapshot of a cycle counter at the entry and exit of every function, so we have a call graph annotated with sub-microsecond timing accuracy. I'd prefer not to just dump out function names and timing like gprof, I'm looking for something easier to understand and act upon.
Has anyone worked with a particularly good profiling tool (on any platform), which made it easy to identify areas of the code to drill into? I'm looking for an inspirational example to follow for how to display the call graph, but if there is good tool with an input format I can massage my data to I'll use it. I could use Windows, Linux, or MacOS X to run the visualization tool.
A profiling article on IBM DeveloperWorks led me to GraphViz, with a profiling example on their site. Barring another suggestion here, I'll use GraphViz and mimic their profiling example.
Another neat tool to visualize profiling data is the gprof2dot.py python script.
It can be used to visualize several different formats: "This is a Python script to convert the output from prof, gprof, oprofile, Shark, AQtime, and python profilers into a dot graph." This is what the output look like:
(source: googlecode.com)
I use Kprof
http://kprof.sourceforge.net/
it is old, but I never found a better tool to inspect the results from gprof.
How about "GTKWave"?
But you have to insert the probe in your code.
Valgrind does profiling (and more), and there are GUIs for visualization.
I suggest you drop gprof+graphviz for OProfileUI, unless you don't have a choice.
JetBrains dotTrace (has a trial demo you can play with). It organizes the call stacks and can easily find the trouble spots. Has a lot of filtering capabilities as well. Very easy to navigate and find what you're looking for.
IE 8b2 offers a simple display of the call tree for javascript that I believe is much more useful than the GraphViz chart.
The GraphViz chart is wonderful for visualizing the call tree but makes it very difficult to visualize timing issues (IMHO the more important data).
**Edit: I thought it is worth pointing out that all of the tools suggested use a grid based tree to visualize the call tree. This allows you to see the calling structure without downplaying the timing data as I believe you do with the GraphViz chart.*
You can use Senseo, a plugin for Eclipse. It shows you the performance, memory allocation, objects created, time spent, actual methods invoked, hover over method signatures or calls, call context tree, package explorer and more.
I've written a browser-based visualization tool, profile_eye, which operates on the output of gprof2dot.
gprof2dot is great at grokking many profiling-tool outputs, and does a great job at graph-element placement. The final rendering is a static graphic, which is often very cluttered.
Using d3.js it's possible to remove much of that clutter, through relative fading of unfocused elements, tooltips, and a fisheye distortion.
For comparison, see profile_eye's visualization of the canonical example used by gprof2dot.