How do can I create binary trees in Perl?
CPAN contains a very wide variety of different modules, and rather than reinventing the wheel, I would suggest looking for it there first. Tree::Binary seems to do what you want to do.
I'm guessing this is some kind of homework assignment (although it's hard to tell from the question), so if you actually have to write your own, a good place to start would be learning how to create objects in Perl (here's a tutorial). The wikipedia page will probably be helpful as well.
A more detailed question will yield better responses.
There's the Tree::Binary module in CPAN...
Although I haven't used it, Tree::RedBlack creates the tree and keeps it balanced (if doing deletes or insertions). If I recall, some of the other tree modules may not provide this capacity (if I have it right).
Chris
I would avoid Tree::Binary from CPAN. We have production software that depends on it, and its API has changed significantly twice in the past two years, making the system crash. For instance, there is a function that continues to do the same thing, but the authors saw fit to at first call it "set_left", then change it to "left", and now "setLeft".
Related
According to this comment from the general question Is it possible to create a quine in every turing-complete language? it seems like it is said that it's possible.
However I didn't find any Ook! Quine on the internet.
Do you think that it's really possible?
And if yes will we be able to find it?
It wouldn't even be very difficult. You would want to code it in brainfuck and then translate, and the internal representation for each command should be a pair of numbers (probably from 0-2) to represent the punctuation of each half-command. You could borrow much of the structure from Erik Bosman's brainfuck quine.
Updated: here. https://gist.github.com/danielcristofani/1fe53487df1f7afcb5b91c06d95184b2
This is ~40 commands taken directly from Erik Bosman's quine, another ~120 freshly written commands of rather clunky output code to handle Ook!'s verbosity, and then the data segment to represent all that.
I want to separate all the common subroutines in another file to save up some space and to tidy up each files. Then just use require() to call a subroutine.
for ex.
sub greet{
print "Hi there\n";
}
which I added and called in Script1.pl, Script2.pl and Script3.pl.
What are the pro's and con's on separating subroutines to another file?
I'm sorry for my grammar. Hope you'd help me understand.
Whenever you have code that is used by more than one script, it generally ought to be split into a library, as you have described. There are many ways to do this, and each has its own advantages and disadvantages which are beyond the scope of your specific question.
So to address your specific question as to pros and cons of moving functionality into a library, lets start with
Pros
Because you have less duplicate code it is easier to
Debug your code (fewer places for bugs to exist)
Update your code (you don't have to edit the same functionality in 10 places)
It makes it easier to share your library functions with other people, or just other projects
It encourages better design, by making you think modularly
Saves space (a pretty minor point, with modern storage media)
Cons
It's harder to distribute your code--you must make sure you distribute all required files as well.
It's not as easy to see your entire code at once--only an issue for small projects, where you even have the option of fitting the entire project on the screen (or in your head) at once
General rule of thumb: Except for very small and/or self-contained projects, use libraries.
i have been working on a perl program to process large amounts of dna. It outputs exactly what i need however it takes much longer than i would like using NYTprof i have narrowed down the major problem areas to be the loop that adds my values together. would using inline::C to do the math make my program faster or should i accept the speed and move on? is there another way to improve the speed? here is my program and an input it would run as well as an executable with the default values entered already.
It's unlikely you'll get useful help here (this included). I can see various problems with your code, and none have to do with the choice of language.
use CPAN. If you're parsing genbank, then use some an appropriate module.
You're writing assembly in Perl, and neither Perl nor you are very good at that. It's near impossible to know what's going on when you don't pass parameters to subroutines, instead relying on globals all over the place. What do #X1, #X2, #Y1, #Y2 mean?
The following might be your problem: until ($ender - $starter > $tlength) { (line 153). According to your test case, these start by being 103, 1, and 200, and it's not clear when or if they change. Depending on what's in #te, it might or might not ever get out of the loop; I just can't tell from your code.
It would help if we knew, exactly, what are the parameters to add, the in-out invariants, and what it is returning.
That's all I got.
I second the recommendation of PDL made in a comment, if it's applicable. Or the use of a CPAN module tailored to your problem (again, if applicable).
I didn't see anything that looked unambiguously like "the loop that adds my values together" in that code; please, show just the code you are considering optimizing, ideally with just enough structure around it to actually run it.
So to answer your generic question generically, yes, Inline::C can be a useful tool for optimization if you are certain your performance problem is limited to what it actually can do for you. In using it, be aware that invoking your C code from Perl or vice versa is non-trivially expensive, so you have to have enough code translated to C to minimize the transitions.
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I work as support staff in a biology research institute as a student, and Perl seems to be used everywhere. Not for every single project, but it seems that more than half the people here have a few Perl books in/on their office/desk.
Why is Perl used so much in biology?
Lincoln Stein highlighted some of the saving graces of Perl for bioinformatics in his article:
How Perl Saved the Human Genome Project.
From his analysis:
I think several factors are responsible:
Perl is remarkably good for slicing, dicing, twisting, wringing, smoothing, summarizing and otherwise mangling text. Although the biological sciences do involve a good deal of numeric analysis now, most of the primary data is still text: clone names, annotations, comments, bibliographic references. Even DNA sequences are textlike. Interconverting incompatible data formats is a matter of text mangling combined with some creative guesswork. Perl's powerful regular expression matching and string manipulation operators simplify this job in a way that isn't equalled by any other modern language.
Perl is forgiving. Biological data is often incomplete, fields can be missing, or a field that is expected to be present once occurs several times (because, for example, an experiment was run in duplicate), or the data was entered by hand and doesn't quite fit the expected format. Perl doesn't particularly mind if a value is empty or contains odd characters. Regular expressions can be written to pick up and correct a variety of common errors in data entry. Of course this flexibility can be also be a curse. I talk more about the problems with Perl below.
Perl is component-oriented. Perl encourages people to write their software in small modules, either using Perl library modules or with the classic Unix tool-oriented approach. External programs can easily be incorporated into a Perl script using a pipe, system call or socket. The dynamic loader introduced with Perl5 allows people to extend the Perl language with C routines or to make entire compiled libraries available for the Perl interpreter. An effort is currently under way to gather all the world's collected wisdom about biological data into a set of modules called "bioPerl" (discussed at length in an article to be published later in the Perl Journal).
Perl is easy to write and fast to develop in. The interpreter doesn't require you to declare all your function prototypes and data types in advance, new variables spring into existence as needed, calls to undefined functions only cause an error when the function is needed. The debugger works well with Emacs and allows a comfortable interactive style of development.
Perl is a good prototyping language. Because Perl is quick and dirty, it often makes sense to prototype new algorithms in Perl before moving them to a fast compiled language.
Sometimes it turns out that Perl is fast enough so that of the algorithm doesn't have to be ported; more frequently one can write a small core of the algorithm in C, compile it as a dynamically loaded module or external executable, and leave the rest of the application in Perl (for an example of a complex genome mapping application implemented in this way, see http://waldo.wi.mit.edu/ftp/distribution/software/rhmapper/).
Perl is a good language for Web CGI scripting, and is growing in importance as more labs turn to the Web for publishing their data.
The real answer probably has less to do with Perl than you think. Many of the things that happen are accidents of history. At the time, way back when, Perl was pretty popular, Java was getting more popular, not too many people were paying attention to Python, and Ruby was just getting started.
The people who needed to get work done used Perl and made some libraries in Perl, and other people started using those libraries. Once people start using something that is moderately useful to them, they tend not to switch (economists call those "switching costs"). From there, even more people start using it because a lot of other people are using it.
The same evolution might not happen today. I'd say that Perl, Python, and Ruby are all completely adequate and up to the task. All the things that mobrule quotes from Lincoln Stein could apply to any of the three today. If everyone had to start from scratch today, any one of those languages could be the one that everyone uses.
I've noticed, from my own client base though (a very small and unrepresentative sample of biotech), that the people pushing the programming for a lot of the biological stuff seemed to be at least part-time sysadmins who were supporting scientists. The scientists worried about the science and did some light programming, but the IT support people were doing a lot of the heavy lifting for the non-science parts. Perl is very well positioned as a sysadmin tool since it's the duct-tape of the internet.
Probably because Perl is good at manipulating strings, and much research in genetics involves the manipulation of veeery long "ACTGCATG..." strings. Just guessing...
I use lots of Perl for dealing with qualitative and quantitative data in social science research. In terms of getting things done (largely with text) quickly, finding libraries on CPAN (nice central location), and generally just getting things done quickly, it can't be surpassed.
Perl is also excellent glue, so if you have some instrumental records, and you need to glue them to data analysis routines, then Perl is your language.
Perl seems to be the language of choice for bioinformatics - there's even an O'Reilly title on just this subject: Beginning Perl for Bioinformatics.
Perl is very powerful when it comes to deal with text and it's present in almost every Linux/Unix distribution. In bioinformatics, not only are sequence data very easy to manipulate with Perl, but also most of the bionformatics algorithms will output some kind of text results.
Then, the biggest bioinformatics centers like the EBI had that great guy, Ewan Birney, who was leading the BioPerl project. That library has lots of parsers for every kind of popular bioinformatics algorithms' results, and for manipulating the different sequence formats used in major sequence databases.
Nowadays, however, Perl is not the only language used by bioinformaticians: along with sequence data, labs produce more and more different kinds of data types and other languages are more often used in those areas.
The R statistics programming language for example, is widely used for statistical analysis of microarray and qPCR data (among others). Again, why are we using it so much? Because it has great libraries for that kind of data (see bioconductor project).
Now when it comes to web development, CGI is not really state of the art today, but people who know Perl may stick to it. In my company though it is no longer used...
I hope this helps.
Perl basically forces very short development cycles. That's the kind of development that gets stuff done.
It's enough to outweigh Perl's disadvantages.
Bioinformatics deals primarily in text parsing and Perl is the best programming language for the job as it is made for string parsing. As the O'Reilly book (Beginning Perl for Bioinformatics) says that "With [Perl]s highly developed capacity to detect patterns in data, Perl has become one of the most popular languages for biological data analysis."
This seems to be a pretty comprehensive response. Perhaps one thing missing, however, is that most biologists (until recently, perhaps) don't have much programming experience at all. The learning curve for Perl is much lower than for compiled languages (like C or Java), and yet Perl still provides a ton of features when it comes to text processing. So what if it takes longer to run? Biologists can definitely handle that. Lab experiments routinely take one hour or more finish, so waiting a few extra minutes for that data processing to finish isn't going to kill them!
Just note that I am talking here about biologists that program out of necessity. I understand that there are some very skilled programmers and computer scientists out there that use Perl as well, and these comments may not apply to them.
People missed out DBI, the Perl abstract database interface that makes it really easy to work with bioinformatic databases.
There is also the one-liner angle. You can write something to reformat data in a single line in Perl and just use the -pe flag to embed that at the command line. Many people using AWK and sed moved to Perl. Even in full programs, file I/O is incredibly easy and quick to write, and text transformation is expressive at a high level compared to any engineering language around. People who use Java or even Python for one-off text transformation are just too lazy to learn another language. Java especially has a high dependence on the JVM implementation and its I/O performance.
At least you know how fast or slow Perl will be everywhere, slightly slower than C I/O. Don't learn grep, cut, sed, or AWK; just learn Perl as your command line tool, even if you don't produce large programs with it. Regarding CGI, Perl has plenty of better web frameworks such as Catalyst and Mojolicious, but the mindshare definitely came from CGI and bioinformatics being one of the earliest heavy users of the Internet.
Perl is very easy to learn as compared to other languages. It can fully exploit the biological data which is becoming the big data. It can manipulate big data and perform good for manipulation data curation and all type of DNA programming, automation of biology has become easy due languages like Perl, Python and Ruby. It is very easy for those who are knowing biology, but not knowing how to program that in other programming languages.
Personally, and I know this will date me, but it's because I learned Perl first. I was being asked to take FASTA files and mix with other FASTA files. Perl was the recommended tool when I asked around.
At the time I'd been through a few computer science classes, but I didn't really know programming all that well.
Perl proved fairly easy to learn. Once I'd gotten regular expressions into my head I was parsing and making new FASTA files within a day.
As has been suggested, I was not a programmer. I was a biochemistry graduate working in a lab, and I'd made the mistake of setting up a Linux server where everyone could see me. This was back in the day when that was an all-day project.
Anyway, Perl became my goto for anything I needed to do around the lab. It was awesome, easy to use, super flexible, other Perl guys in other labs we're a lot like me.
So, to cut it short, Perl is easy to learn, flexible and forgiving, and it did what I needed.
Once I really got into bioinformatics I picked up R, Python, and even Java. Perl is not that great at helping to create maintainable code, mostly because it is so flexible. Now I just use the language for the job, but Perl is still one of my favorite languages, like a first kiss or something.
To reiterate, most bioinformatics folks learned coding by just kluging stuff together, and most of the time you're just trying to get an answer for the principal investigator (PI), so you can't spend days on code design. Perl is superb at just getting an answer, it probably won't work a second time, and you will not understand anything in your own code if you see it six months later; BUT if you need something now, then it is a good choice even though I mostly use Python now.
I hope that gives you an answer from someone who lived it.
I've found a few related questions, like Python vs. Perl (now deleted) and Is Perl Worth it? (now deleted), but I can't seem to find anything that directly addresses this question.
Is there a legitimate future in Perl? I work in a Perl shop right now, and I came from PHP so I see some of the advantages of an arguably "lower" level language when doing things on the server-level, but it seems to me a lot of the tasks in Perl can be performed more quickly in PHP, and SOME ARGUE (subjective, not my opinion) that Python does these tasks in a more explicit way that's easier to maintain.
Is having this job on my resume ultimately going to make me less employable, especially if the language no longer grows?
A few notes:
I love Perl, so don't think I'm bashing the language. It's fun to use and we use a fairly verbose syntax that is relatively easy to maintain.
I realize that "Vaporware" is a buzzword that isn't necessarily applicable to this situation, because Perl doesn't have a marketing department and they're not "promising" Perl 6 by any date.
I realize that CPAN keeps the community going, so whether Perl 6 comes out or not people continue to build modules that increase possibilities in the language, but that doesn't mean that industry shops realize this, and switch to "more supported" languages that keep coming out with revised versions of the language like Python and (especially) PHP.*
EDIT {CLARIFICATION}
Cade Roux and Telemachus both brought up good points about whether or not your future can be defined by your resume.
To be honest, this was brought up when one of my former employers said "I don't hire anyone with Perl as their last job. That's OLD technology." This was a PHP shop, so take all that with a grain of salt.
Now without defaming my former employer, she's not a tech person AT ALL, so she was really expressing an opinion of a layperson, and in this case my question was more along the lines of "Is there a stigma on this particular technology placed on it by people who don't utilize it?", specifically more along the lines of people who may have had past experience with similar employers. I'm not asking you to look into the future with a magic glass to assume what the next "hot" language would be, but rather if this particular language (which is accused of stunted growth, again by laypeople) has negative connotations placed upon it.
I hope that makes a little more sense.
Plenty of shops - including on Wall Street - heavily use Perl and will continue to do so.
However, I have never seen a PHP or Python used in this industry (not saying it is not used, but that I never encountered. Purely personal anecdote. Nor have I EVER heard any conversation of "Perl can not do X that Python can, let's use Python").
Perl6 is irrelevant to job picture.
Many shops are still on 5.8 or G-d forbid 5.6
More importantly, perl5 continues to evolve, including with features/ideas from Perl6. See Perl 5.10 and 5.11
Plus evolution includes really cool framework like Moose etc...
I can probably come up with more bullets later, but the summary is that no, having a Perl job will in no way negatively affect your career prospects.
However, knowing nothing but Perl may affect it negatively, so make sure you know Java, C#, C++ or something besides dynamic interpreted languages. Not many shops would hire "Perl Only" developer, even if they gladly hire "Perl + other stuff" ones.
See Tim Bunce's Perl Myths slides on slide share.
In short, Perl is not dead and has lots of jobs available.
Anyone who actually watches the development of Perl, would know that that there has perhaps been more work on the Perl language in the past decade, than in the previous decade.
This has been spurred on by the introduction of Perl6.
The introduction of Perl 6 spurred on, the now deeply ingrained, testing culture.
Just look at how much the Rakudo implementation of Perl 6, is tested:
Rakudo Progress http://rakudo.de/progress.png
There has also been a lot of back-porting of Perl 6 features into Perl 5.
For example, the Perl 6 "switch" statement
#!/usr/bin/perl
use strict;
use warnings;
use 5.10.1;
# or
use feature qw'switch say';
my $str = "testing 123";
given( $str ){
when(/(\d+)/){
say $1;
}
when( [0..10] ){
say $_, 'is equal to some number between 0 and 10';
# given, sets the current topic "$_"
}
}
There are few languages I would tie my career to. Perl will always be there and it will always be the best tool for certain kinds of jobs. But this is true for many languages. However, there are also languages which have more competition in some of the spaces where they are used. Perl is one language that has a lot more strong niches.
Still, you wouldn't restrict yourself to using just one language for your entire life - or even in one project if there are better options to solve a problem.
Career-wise, there are basic technologies which are fairly universally used, and of these I think a few of the most valuable are: relational database concepts and SQL, XML/HTML/HTTP/DOM, regular expressions. These are all basically independent of any particular vendor or language, and if you are strong in these areas, choice of language and platform are going to be informed by the problem being addressed.
Perl is, and always will be, a practical language for manipulating large amounts of data. I work in an industry where moving, converting, and parsing large amounts of text and image data is what we do, and I couldn't live without Perl.
Likewise, if you're a sysadmin (especially a Unix one), Perl is a necessary tool. There are tons of places where you need to be able to whip up a quick and dirty application that runs right along with the shell functions.
Languages have niches. Perl has a big stable niche, in many ways much more stable than fad-driven web languages. PHP, for example, is a nice little web language, but its saving grace is that it's quick and easy to develop in, not that it is a particularly great language. I'll tend to use PHP over Perl for web applications (though I use Python over PHP, if I have time), but 90% of the stuff I do in my day-to-day would be nearly impossible in PHP, and is flat trivial in Perl.
#Nate: I love Python. LOVE it. I actually worry that I love it too much, and I'm being irrational about it. PHP is a nice tool, but when your main selling point is "Quick and Easy" then you're running a risk. That was the big push behind original Visual Basic, and we all know how that worked out.
I'd discourage you from putting Perl on your resume - there's already too many people in the perl market and we don't want any more! ... just kidding.
The past is supposedly no guide to the future, but, despite having plenty of C (etc.) and Java in my 'skills toolbag' I've seen more gainful employ from my Perl than anything else over the last decade.
I suspect that offshore-perl-new-build may not be the biggest market in the future, but there's certainly active development in the city and media industries in the UK.
Otherwise, I'd just agree with the points above. Technicians with diverse skills are more able to pick the right tools, and less inclined to 'get religious' about language choice.
If you're looking at a post where the non-technical management have a strong point of view about what technology should and shouldn't be used - I'd place that one in the 'avoid' pile.
To add another separate answer - as you have noted - there is a very real danger when dealing with recruiters and others that your resume will be interpreted and things inferred that are not necessarily how you see yourself, and you might get pigeon-holed.
This WILL happen both ways - too much variation and you aren't an expert in anything OR too little variation and you are only good at one thing.
I don't have a simple answer for combatting that, except to ensure that you emphasize portable skills and also achievements which are independent of technology - making the company more money, landing new business, making new markets, etc.
Perl is another tool in your toolbox. If I have an opening and one person is narrow focused to a specific technology, and another has a broad range of skills I would be more inclined to hire the one with the wider range of skills even if they might not be quite as deeply knowledgeable. Some one who has a wide range of skills on a range of platforms is someone who can think, innovate and adapt.
I don't understand the point of this question. You have a job and you already know Perl. You can ask whether or not to learn new languages and which ones to learn (please don't, but you could), but none of us can or should predict whether or not you're going to get another job using Perl.
You ask, "Is having this job on my resume ultimately going to make me less employable, especially if the language no longer grows?"
Well, it's better than a blank resume, and you can't change your past, so really what are we talking about here?